Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDAndrew Y. Ng
Andrew Yan-Tak Ng – 吳恩達
Person information
- unicode name: 吳恩達
- affiliation: Stanford University, Computer Science Department
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
Journal Articles
- 2024
[j36]Yuntao Ma
, Hiva Ghanbari, Tianyuan Huang, Jeremy Irvin, Oliver Brady, Sofian Zalouk, Hao Sheng, Andrew Y. Ng, Ram Rajagopal, Mayur Narsude:
A System for Automated Vehicle Damage Localization and Severity Estimation Using Deep Learning. IEEE Trans. Intell. Transp. Syst. 25(6): 5627-5639 (2024)- 2023
- [j35]David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Sasha Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla P. Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer T. Chayes, Yoshua Bengio:
Tackling Climate Change with Machine Learning. ACM Comput. Surv. 55(2): 42:1-42:96 (2023) - [j34]Feiyang Yu, Mark Endo, Rayan Krishnan, Ian Pan, Andy Tsai, Eduardo Pontes Reis, Eduardo Kaiser Ururahy Nunes Fonseca, Henrique Min Ho Lee, Zahra Shakeri Hossein Abad, Andrew Y. Ng, Curtis P. Langlotz, Vasantha Kumar Venugopal, Pranav Rajpurkar:
Evaluating progress in automatic chest X-ray radiology report generation. Patterns 4(9): 100802 (2023) - 2022
- [j33]Boyang Tom Jin, Raj Palleti, Siyu Shi, Andrew Y. Ng, James V. Quinn, Pranav Rajpurkar, David A. Kim:
Transfer learning enables prediction of myocardial injury from continuous single-lead electrocardiography. J. Am. Medical Informatics Assoc. 29(11): 1908-1918 (2022) - [j32]Adriel Saporta, Xiaotong Gui, Ashwin Agrawal, Anuj Pareek, Steven Q. H. Truong, Chanh D. T. Nguyen, Van Doan Ngo, Jayne Seekins, Francis G. Blankenberg, Andrew Y. Ng, Matthew P. Lungren, Pranav Rajpurkar:
Benchmarking saliency methods for chest X-ray interpretation. Nat. Mac. Intell. 4(10): 867-878 (2022) - [j31]Pratham N. Soni, Siyu Shi, Pranav R. Sriram, Andrew Y. Ng, Pranav Rajpurkar:
Contrastive learning of heart and lung sounds for label-efficient diagnosis. Patterns 3(1): 100400 (2022) - 2021
- [j30]Michael Ko, Emma Chen, Ashwin Agrawal, Pranav Rajpurkar, Anand Avati, Andrew Yan-Tak Ng, Sanjay Basu, Nigam H. Shah:
Improving hospital readmission prediction using individualized utility analysis. J. Biomed. Informatics 119: 103826 (2021) - [j29]Sharon Zhou, Jiequan Zhang, Hang Jiang, Torbjörn Lundh, Andrew Y. Ng:
Data augmentation with Mobius transformations. Mach. Learn. Sci. Technol. 2(2): 25016 (2021) - [j28]David Eng, Christopher Chute, Nishith Khandwala, Pranav Rajpurkar, Jin Long, Sam Shleifer, Mohamed H. Khalaf, Alexander T. Sandhu, Fátima Rodriguez, David J. Maron, Saeed Seyyedi, Daniele Marin, Ilana Golub, Matthew J. Budoff, Felipe Kitamura, Marcelo Straus Takahashi, Ross W. Filice, Rajesh Shah, John Mongan, Kimberly Kallianos, Curtis P. Langlotz, Matthew P. Lungren, Andrew Y. Ng, Bhavik N. Patel:
Automated coronary calcium scoring using deep learning with multicenter external validation. npj Digit. Medicine 4 (2021) - 2020
- [j27]Shih-Cheng Huang, Tanay Kothari, Imon Banerjee, Christopher Chute, Robyn L. Ball, Norah Borus, Andrew Huang, Bhavik N. Patel, Pranav Rajpurkar, Jeremy Irvin, Jared Dunnmon, Joseph Bledsoe, Katie S. Shpanskaya, Abhay Dhaliwal, Roham Zamanian, Andrew Y. Ng, Matthew P. Lungren:
PENet - a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging. npj Digit. Medicine 3 (2020) - [j26]Shih-Cheng Huang, Tanay Kothari, Imon Banerjee, Christopher Chute, Robyn L. Ball, Norah Borus, Andrew Huang, Bhavik N. Patel, Pranav Rajpurkar, Jeremy Irvin, Jared Dunnmon, Joseph Bledsoe, Katie S. Shpanskaya, Abhay Dhaliwal, Roham Zamanian, Andrew Y. Ng, Matthew P. Lungren:
Author Correction: PENet - a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging. npj Digit. Medicine 3 (2020) - [j25]Amirhossein Kiani, Bora Uyumazturk, Pranav Rajpurkar, Alex Wang, Rebecca Gao, Erik Jones, Yifan Yu, Curtis P. Langlotz, Robyn L. Ball, Thomas J. Montine, Brock A. Martin, Gerald J. Berry, Michael G. Ozawa, Florette K. Hazard, Ryanne A. Brown, Simon B. Chen, Mona Wood, Libby S. Allard, Lourdes Ylagan, Andrew Y. Ng, Jeanne Shen:
Impact of a deep learning assistant on the histopathologic classification of liver cancer. npj Digit. Medicine 3 (2020) - [j24]Pranav Rajpurkar, Chloe P. O'Connell, Amit Schechter, Nishit Asnani, Jason Li, Amirhossein Kiani, Robyn L. Ball, Marc Mendelson, Gary Maartens, Daniël J. van Hoving, Rulan Griesel, Andrew Y. Ng, Tom H. Boyles, Matthew P. Lungren:
CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV. npj Digit. Medicine 3 (2020) - 2018
- [j23]Anand Avati, Kenneth Jung, Stephanie Harman, Lance Downing, Andrew Y. Ng, Nigam H. Shah:
Improving palliative care with deep learning. BMC Medical Informatics Decis. Mak. 18(S-4): 55-64 (2018) - 2017
- [j22]Andrew L. Maas, Peng Qi, Ziang Xie, Awni Y. Hannun, Christopher T. Lengerich, Daniel Jurafsky, Andrew Y. Ng:
Building DNN acoustic models for large vocabulary speech recognition. Comput. Speech Lang. 41: 195-213 (2017) - [j21]Sherry Ruan, Jacob O. Wobbrock, Kenny Liou, Andrew Y. Ng, James A. Landay:
Comparing Speech and Keyboard Text Entry for Short Messages in Two Languages on Touchscreen Phones. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1(4): 159:1-159:23 (2017) - 2016
- [j20]Michiel Kallenberg, Kersten Petersen, Mads Nielsen, Andrew Y. Ng, Pengfei Diao, Christian Igel, Celine M. Vachon, Katharina Holland, Rikke Rass Winkel, Nico Karssemeijer, Martin Lillholm:
Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring. IEEE Trans. Medical Imaging 35(5): 1322-1331 (2016) - 2014
- [j19]Morgan Quigley, Curt Salisbury, Andrew Y. Ng, J. Kenneth Salisbury:
Mechatronic design of an integrated robotic hand. Int. J. Robotics Res. 33(5): 706-720 (2014) - [j18]Richard Socher, Andrej Karpathy, Quoc V. Le, Christopher D. Manning, Andrew Y. Ng:
Grounded Compositional Semantics for Finding and Describing Images with Sentences. Trans. Assoc. Comput. Linguistics 2: 207-218 (2014) - [j17]Andrew L. Maas, Chris Heather, Chuong B. Do, Relly Brandman, Daphne Koller, Andrew Y. Ng:
Offering Verified Credentials in Massive Open Online Courses: MOOCs and technology to advance learning and learning research (Ubiquity symposium). Ubiquity 2014(May): 2:1-2:11 (2014) - 2011
- [j16]Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng:
Unsupervised learning of hierarchical representations with convolutional deep belief networks. Commun. ACM 54(10): 95-103 (2011) - [j15]J. Zico Kolter, Andrew Y. Ng:
The Stanford LittleDog: A learning and rapid replanning approach to quadruped locomotion. Int. J. Robotics Res. 30(2): 150-174 (2011) - 2010
- [j14]Pieter Abbeel, Adam Coates, Andrew Y. Ng:
Autonomous Helicopter Aerobatics through Apprenticeship Learning. Int. J. Robotics Res. 29(13): 1608-1639 (2010) - 2009
- [j13]Jan Peters, Andrew Y. Ng:
Guest editorial: Special issue on robot learning, Part A. Auton. Robots 27(1): 1-2 (2009) - [j12]Jan Peters, Andrew Y. Ng:
Guest editorial: Special issue on robot learning, Part B. Auton. Robots 27(2): 91-92 (2009) - [j11]Adam Coates, Pieter Abbeel, Andrew Y. Ng:
Apprenticeship learning for helicopter control. Commun. ACM 52(7): 97-105 (2009) - [j10]Ashutosh Saxena, Min Sun, Andrew Y. Ng:
Make3D: Learning 3D Scene Structure from a Single Still Image. IEEE Trans. Pattern Anal. Mach. Intell. 31(5): 824-840 (2009) - 2008
- [j9]Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng:
3-D Depth Reconstruction from a Single Still Image. Int. J. Comput. Vis. 76(1): 53-69 (2008) - [j8]Ashutosh Saxena, Justin Driemeyer, Andrew Y. Ng:
Robotic Grasping of Novel Objects using Vision. Int. J. Robotics Res. 27(2): 157-173 (2008) - 2007
- [j7]Masayoshi Matsuoka, Alan Chen, Surya P. N. Singh, Adam Coates, Andrew Y. Ng, Sebastian Thrun:
Autonomous Helicopter Tracking and Localization Using a Self-surveying Camera Array. Int. J. Robotics Res. 26(2): 205-215 (2007) - 2006
- [j6]Pieter Abbeel, Daphne Koller, Andrew Y. Ng:
Learning Factor Graphs in Polynomial Time and Sample Complexity. J. Mach. Learn. Res. 7: 1743-1788 (2006) - 2005
- [j5]David Heckerman, Tom Berson, Joshua Goodman, Andrew Y. Ng:
The First Conference on E-mail and Anti-Spam. AI Mag. 26(1): 96 (2005) - 2004
- [j4]Sebastian Thrun, Yufeng Liu, Daphne Koller, Andrew Y. Ng, Zoubin Ghahramani, Hugh F. Durrant-Whyte:
Simultaneous Localization and Mapping with Sparse Extended Information Filters. Int. J. Robotics Res. 23(7-8): 693-716 (2004) - 2003
- [j3]David M. Blei, Andrew Y. Ng, Michael I. Jordan:
Latent Dirichlet Allocation. J. Mach. Learn. Res. 3: 993-1022 (2003) - 2002
- [j2]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng:
A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes. Mach. Learn. 49(2-3): 193-208 (2002) - 1997
- [j1]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron:
An Experimental and Theoretical Comparison of Model Selection Methods. Mach. Learn. 27(1): 7-50 (1997)
Conference and Workshop Papers
- 2023
- [c194]Vivek Shankar, Xiaoli Yang, Vrishab Krishna, Brent Tan, Oscar Silva, Rebecca Rojansky, Andrew Y. Ng, Fabiola Valvert, Edward Briercheck, David Weinstock, Yasodha Natkunam, Sebastian Fernandez-Pol, Pranav Rajpurkar:
LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype. ML4H@NeurIPS 2023: 528-558 - [c193]Mark Mazumder, Colby R. Banbury, Xiaozhe Yao, Bojan Karlas, William Gaviria Rojas, Sudnya Frederick Diamos, Greg Diamos, Lynn He, Alicia Parrish, Hannah Rose Kirk, Jessica Quaye, Charvi Rastogi, Douwe Kiela, David Jurado, David Kanter, Rafael Mosquera, Will Cukierski, Juan Ciro, Lora Aroyo, Bilge Acun, Lingjiao Chen, Mehul Raje, Max Bartolo, Evan Sabri Eyuboglu, Amirata Ghorbani, Emmett D. Goodman, Addison Howard, Oana Inel, Tariq Kane, Christine R. Kirkpatrick, D. Sculley, Tzu-Sheng Kuo, Jonas W. Mueller, Tristan Thrush, Joaquin Vanschoren, Margaret Warren, Adina Williams, Serena Yeung, Newsha Ardalani, Praveen K. Paritosh, Ce Zhang, James Y. Zou, Carole-Jean Wu, Cody Coleman, Andrew Y. Ng, Peter Mattson, Vijay Janapa Reddi:
DataPerf: Benchmarks for Data-Centric AI Development. NeurIPS 2023 - 2022
- [c192]Tianyuan Huang, Timothy Dai, Zhecheng Wang, Hesu Yoon, Hao Sheng, Andrew Y. Ng, Ram Rajagopal, Jackelyn Hwang:
Detecting Neighborhood Gentrification at Scale via Street-level Visual Data. IEEE Big Data 2022: 1632-1640 - [c191]Bryan Zhu, Nicholas Lui, Jeremy Irvin, Jimmy Le, Sahil Tadwalkar, Chenghao Wang, Zutao Ouyang, Frankie Y. Liu, Andrew Y. Ng, Robert B. Jackson:
METER-ML: A Multi-Sensor Earth Observation Benchmark for Automated Methane Source Mapping. CDCEO@IJCAI 2022: 33-43 - [c190]Damir Vrabac, Akshay Smit, Yujie He, Andrew Y. Ng, Andrew L. Beam, Pranav Rajpurkar:
MedSelect: Selective Labeling for Medical Image Classification Using Meta-Learning. MIDL 2022: 1301-1310 - 2021
- [c189]Saahil Jain, Akshay Smit, Steven Q. H. Truong, Chanh D. T. Nguyen, Minh-Thanh Huynh, Mudit Jain, Victoria A. Young, Andrew Y. Ng, Matthew P. Lungren, Pranav Rajpurkar:
VisualCheXbert: addressing the discrepancy between radiology report labels and image labels. CHIL 2021: 105-115 - [c188]Alexander Ke, William Ellsworth, Oishi Banerjee, Andrew Y. Ng, Pranav Rajpurkar:
CheXtransfer: performance and parameter efficiency of ImageNet models for chest X-Ray interpretation. CHIL 2021: 116-124 - [c187]Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek, Andrew Y. Ng, Matthew P. Lungren:
CheXternal: generalization of deep learning models for chest X-ray interpretation to photos of chest X-rays and external clinical settings. CHIL 2021: 125-132 - [c186]Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Gunnar E. Carlsson, Stefano Ermon:
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology. ICLR 2021 - [c185]Viswesh Krishna, Anirudh Joshi, Damir Vrabac, Philip L. Bulterys, Eric Yang, Sebastian Fernandez-Pol, Andrew Y. Ng, Pranav Rajpurkar:
GloFlow: Whole Slide Image Stitching from Video Using Optical Flow and Global Image Alignment. MICCAI (8) 2021: 519-528 - [c184]Soham Uday Gadgil, Mark Endo, Emily Wen, Andrew Y. Ng, Pranav Rajpurkar:
CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation. MIDL 2021: 190-204 - [c183]Siyu Shi, Ishaan Malhi, Kevin Tran, Andrew Y. Ng, Pranav Rajpurkar:
Unseen Disease Detection for Deep Learning Interpretation of Chest X-rays. MIDL 2021: 699-712 - [c182]Hari Sowrirajan, Jingbo Yang, Andrew Y. Ng, Pranav Rajpurkar:
MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models. MIDL 2021: 728-744 - [c181]Bryan Gopal, Ryan W. Han, Gautham Raghupathi, Andrew Y. Ng, Geoffrey H. Tison, Pranav Rajpurkar:
3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations. ML4H@NeurIPS 2021: 156-167 - [c180]Mark Endo, Rayan Krishnan, Viswesh Krishna, Andrew Y. Ng, Pranav Rajpurkar:
Retrieval-Based Chest X-Ray Report Generation Using a Pre-trained Contrastive Language-Image Model. ML4H@NeurIPS 2021: 209-219 - [c179]Emma Chen, Andy Kim, Rayan Krishnan, Jin Long, Andrew Y. Ng, Pranav Rajpurkar:
CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays. MLHC 2021: 103-125 - [c178]Yen Nhi Truong Vu, Richard Wang, Niranjan Balachandar, Can Liu, Andrew Y. Ng, Pranav Rajpurkar:
MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation. MLHC 2021: 755-769 - [c177]Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven Q. H. Truong, Du Nguyen Duong, Tan Bui, Pierre J. Chambon, Yuhao Zhang, Matthew P. Lungren, Andrew Y. Ng, Curtis P. Langlotz, Pranav Rajpurkar:
RadGraph: Extracting Clinical Entities and Relations from Radiology Reports. NeurIPS Datasets and Benchmarks 2021 - [c176]Cécile Logé, Emily Ross, David Yaw Amoah Dadey, Saahil Jain, Adriel Saporta, Andrew Y. Ng, Pranav Rajpurkar:
Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management. NeurIPS Datasets and Benchmarks 2021 - 2020
- [c175]Mang Tik Chiu, Xingqian Xu, Kai Wang, Jennifer A. Hobbs, Naira Hovakimyan, Thomas S. Huang, Honghui Shi, Yunchao Wei, Zilong Huang, Alexander G. Schwing, Robert Brunner, Ivan Dozier, Wyatt Dozier, Karen Ghandilyan, David Wilson, Hyunseong Park, Jun Hee Kim, Sungho Kim, Qinghui Liu, Michael C. Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg, Alexandre Barbosa, Rodrigo G. Trevisan, Bingchen Zhao, Shaozuo Yu, Siwei Yang, Yin Wang, Hao Sheng, Xiao Chen, Jingyi Su, Ram Rajagopal, Andrew Y. Ng, Van Thong Huynh, Soo-Hyung Kim, In Seop Na, Ujjwal Baid, Shubham Innani, Prasad Dutande, Bhakti Baheti, Sanjay N. Talbar, Jianyu Tang:
The 1st Agriculture-Vision Challenge: Methods and Results. CVPR Workshops 2020: 212-218 - [c174]Hao Sheng, Xiao Chen, Jingyi Su, Ram Rajagopal, Andrew Y. Ng:
Effective Data Fusion with Generalized Vegetation Index: Evidence from Land Cover Segmentation in Agriculture. CVPR Workshops 2020: 267-276 - [c173]Akshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pareek, Andrew Y. Ng, Matthew P. Lungren:
Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT. EMNLP (1) 2020: 1500-1519 - [c172]Tony Duan, Anand Avati, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler:
NGBoost: Natural Gradient Boosting for Probabilistic Prediction. ICML 2020: 2690-2700 - [c171]Nick A. Phillips, Pranav Rajpurkar, Mark Sabini, Rayan Krishnan, Sharon Zhou, Anuj Pareek, Nguyet Minh Phu, Chris Wang, Mudit Jain, Du Nguyen Duong, Steven Q. H. Truong, Andrew Y. Ng, Matthew P. Lungren:
CheXphoto: 10, 000+ Photos and Transformations of Chest X-rays for Benchmarking Deep Learning Robustness. ML4H@NeurIPS 2020: 318-327 - 2019
- [c170]Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Christopher Chute, Henrik Marklund, Behzad Haghgoo, Robyn L. Ball, Katie S. Shpanskaya, Jayne Seekins, David A. Mong, Safwan S. Halabi, Jesse K. Sandberg, Ricky Jones, David B. Larson, Curtis P. Langlotz, Bhavik N. Patel, Matthew P. Lungren, Andrew Y. Ng:
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. AAAI 2019: 590-597 - [c169]Yichen Shen, Maxime Voisin, Alireza Aliamiri, Anand Avati, Awni Y. Hannun, Andrew Y. Ng:
Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning. KDD 2019: 1909-1916 - [c168]Anand Avati, Tony Duan, Sharon Zhou, Kenneth Jung, Nigam H. Shah, Andrew Y. Ng:
Countdown Regression: Sharp and Calibrated Survival Predictions. UAI 2019: 145-155 - 2018
- [c167]Ziang Xie, Guillaume Genthial, Stanley Xie, Andrew Y. Ng, Dan Jurafsky:
Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction. NAACL-HLT 2018: 619-628 - 2017
- [c166]Anand Avati, Kenneth Jung, Stephanie Harman, Lance Downing, Andrew Y. Ng, Nigam H. Shah:
Improving palliative care with deep learning. BIBM 2017: 311-316 - [c165]Ziang Xie, Sida I. Wang, Jiwei Li, Daniel Lévy, Aiming Nie, Dan Jurafsky, Andrew Y. Ng:
Data Noising as Smoothing in Neural Network Language Models. ICLR (Poster) 2017 - [c164]Sercan Ömer Arik, Mike Chrzanowski, Adam Coates, Gregory Frederick Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Andrew Y. Ng, Jonathan Raiman, Shubho Sengupta, Mohammad Shoeybi:
Deep Voice: Real-time Neural Text-to-Speech. ICML 2017: 195-204 - 2016
- [c163]Andrew Y. Ng:
Deep Learning: What's Next. AAMAS 2016: 1 - [c162]Russell Stewart, Mykhaylo Andriluka, Andrew Y. Ng:
End-to-End People Detection in Crowded Scenes. CVPR 2016: 2325-2333 - [c161]Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Erich Elsen, Jesse H. Engel, Linxi Fan, Christopher Fougner, Awni Y. Hannun, Billy Jun, Tony Han, Patrick LeGresley, Xiangang Li, Libby Lin, Sharan Narang, Andrew Y. Ng, Sherjil Ozair, Ryan Prenger, Sheng Qian, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Chong Wang, Yi Wang, Zhiqian Wang, Bo Xiao, Yan Xie, Dani Yogatama, Jun Zhan, Zhenyao Zhu:
Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin. ICML 2016: 173-182 - 2015
- [c160]Andrew L. Maas, Ziang Xie, Dan Jurafsky, Andrew Y. Ng:
Lexicon-Free Conversational Speech Recognition with Neural Networks. HLT-NAACL 2015: 345-354 - 2014
- [c159]Brody Huval, Adam Coates, Andrew Y. Ng:
Deep learning for class-generic object detection. ICLR (Workshop Poster) 2014 - 2013
- [c158]Richard Socher, John Bauer, Christopher D. Manning, Andrew Y. Ng:
Parsing with Compositional Vector Grammars. ACL (1) 2013: 455-465 - [c157]Andrew Y. Ng:
The online revolution: education for everyone. CIKM 2013: 1913-1914 - [c156]Chris Piech, Jonathan Huang, Zhenghao Chen, Chuong B. Do, Andrew Y. Ng, Daphne Koller:
Tuned Models of Peer Assessment in MOOCs. EDM 2013: 153-160 - [c155]Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, Christopher Potts:
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. EMNLP 2013: 1631-1642 - [c154]Adam Coates, Brody Huval, Tao Wang, David J. Wu, Bryan Catanzaro, Andrew Y. Ng:
Deep learning with COTS HPC systems. ICML (3) 2013: 1337-1345 - [c153]Andrew Y. Ng, Daphne Koller:
The online revolution: education for everyone. KDD 2013: 2 - [c152]Richard Socher, Danqi Chen, Christopher D. Manning, Andrew Y. Ng:
Reasoning With Neural Tensor Networks for Knowledge Base Completion. NIPS 2013: 926-934 - [c151]Richard Socher, Milind Ganjoo, Christopher D. Manning, Andrew Y. Ng:
Zero-Shot Learning Through Cross-Modal Transfer. NIPS 2013: 935-943 - [c150]Danqi Chen, Richard Socher, Christopher D. Manning, Andrew Y. Ng:
Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors. ICLR (Workshop Poster) 2013 - [c149]Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher D. Manning, Andrew Y. Ng:
Zero-Shot Learning Through Cross-Modal Transfer. ICLR (Workshop) 2013 - 2012
- [c148]Eric H. Huang, Richard Socher, Christopher D. Manning, Andrew Y. Ng:
Improving Word Representations via Global Context and Multiple Word Prototypes. ACL (1) 2012: 873-882 - [c147]Richard Socher, Brody Huval, Christopher D. Manning, Andrew Y. Ng:
Semantic Compositionality through Recursive Matrix-Vector Spaces. EMNLP-CoNLL 2012: 1201-1211 - [c146]Quoc V. Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Greg Corrado, Kai Chen, Jeffrey Dean, Andrew Y. Ng:
Building high-level features using large scale unsupervised learning. ICML 2012 - [c145]Tao Wang, David J. Wu, Adam Coates, Andrew Y. Ng:
End-to-end text recognition with convolutional neural networks. ICPR 2012: 3304-3308 - [c144]Andrew L. Maas, Quoc V. Le, Tyler M. O'Neil, Oriol Vinyals, Patrick Nguyen, Andrew Y. Ng:
Recurrent Neural Networks for Noise Reduction in Robust ASR. INTERSPEECH 2012: 22-25 - [c143]Richard Socher, Brody Huval, Bharath Putta Bath, Christopher D. Manning, Andrew Y. Ng:
Convolutional-Recursive Deep Learning for 3D Object Classification. NIPS 2012: 665-673 - [c142]Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc'Aurelio Ranzato, Andrew W. Senior, Paul A. Tucker, Ke Yang, Andrew Y. Ng:
Large Scale Distributed Deep Networks. NIPS 2012: 1232-1240 - [c141]Adam Coates, Andrej Karpathy, Andrew Y. Ng:
Emergence of Object-Selective Features in Unsupervised Feature Learning. NIPS 2012: 2690-2698 - [c140]Will Y. Zou, Andrew Y. Ng, Shenghuo Zhu, Kai Yu:
Deep Learning of Invariant Features via Simulated Fixations in Video. NIPS 2012: 3212-3220 - 2011
- [c139]Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, Christopher Potts:
Learning Word Vectors for Sentiment Analysis. ACL 2011: 142-150 - [c138]Quoc V. Le, Will Y. Zou, Serena Y. Yeung, Andrew Y. Ng:
Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. CVPR 2011: 3361-3368 - [c137]Richard Socher, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, Christopher D. Manning:
Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions. EMNLP 2011: 151-161 - [c136]Adam Coates, Blake Carpenter, Carl Case, Sanjeev Satheesh, Bipin Suresh, Tao Wang, David J. Wu, Andrew Y. Ng:
Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning. ICDAR 2011: 440-445 - [c135]Richard Socher, Cliff Chiung-Yu Lin, Andrew Y. Ng, Christopher D. Manning:
Parsing Natural Scenes and Natural Language with Recursive Neural Networks. ICML 2011: 129-136 - [c134]Quoc V. Le, Jiquan Ngiam, Adam Coates, Ahbik Lahiri, Bobby Prochnow, Andrew Y. Ng:
On optimization methods for deep learning. ICML 2011: 265-272 - [c133]Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, Andrew Y. Ng:
Multimodal Deep Learning. ICML 2011: 689-696 - [c132]Adam Coates, Andrew Y. Ng:
The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization. ICML 2011: 921-928 - [c131]Andrew M. Saxe, Pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, Andrew Y. Ng:
On Random Weights and Unsupervised Feature Learning. ICML 2011: 1089-1096 - [c130]Jiquan Ngiam, Zhenghao Chen, Pang Wei Koh, Andrew Y. Ng:
Learning Deep Energy Models. ICML 2011: 1105-1112 - [c129]Ellen Klingbeil, Deepak Rao, Blake Carpenter, Varun Ganapathi, Andrew Y. Ng, Oussama Khatib:
Grasping with application to an autonomous checkout robot. ICRA 2011: 2837-2844 - [c128]Carl Case, Bipin Suresh, Adam Coates, Andrew Y. Ng:
Autonomous sign reading for semantic mapping. ICRA 2011: 3297-3303 - [c127]Morgan Quigley, Alan T. Asbeck, Andrew Y. Ng:
A low-cost compliant 7-DOF robotic manipulator. ICRA 2011: 6051-6058 - [c126]Richard Socher, Eric H. Huang, Jeffrey Pennington, Andrew Y. Ng, Christopher D. Manning:
Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection. NIPS 2011: 801-809 - [c125]Quoc V. Le, Alexandre Karpenko, Jiquan Ngiam, Andrew Y. Ng:
ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning. NIPS 2011: 1017-1025 - [c124]Jiquan Ngiam, Pang Wei Koh, Zhenghao Chen, Sonia A. Bhaskar, Andrew Y. Ng:
Sparse Filtering. NIPS 2011: 1125-1133 - [c123]Andrew M. Saxe, Maneesh Bhand, Ritvik Mudur, Bipin Suresh, Andrew Y. Ng:
Unsupervised learning models of primary cortical receptive fields and receptive field plasticity. NIPS 2011: 1971-1979 - [c122]Adam Coates, Andrew Y. Ng:
Selecting Receptive Fields in Deep Networks. NIPS 2011: 2528-2536 - [c121]Adam Coates, Andrew Y. Ng, Honglak Lee:
An Analysis of Single-Layer Networks in Unsupervised Feature Learning. AISTATS 2011: 215-223 - 2010
- [c120]Morgan Quigley, Alan T. Asbeck, Andrew Y. Ng:
Low-Cost Manipulation Powered by ROS. Enabling Intelligence through Middleware 2010 - [c119]Olga Russakovsky, Andrew Y. Ng:
A Steiner tree approach to efficient object detection. CVPR 2010: 1070-1077 - [c118]Adam Coates, Andrew Y. Ng:
Multi-camera object detection for robotics. ICRA 2010: 412-419 - [c117]Ellen Klingbeil, Blake Carpenter, Olga Russakovsky, Andrew Y. Ng:
Autonomous operation of novel elevators for robot navigation. ICRA 2010: 751-758 - [c116]J. Zico Kolter, Christian Plagemann, David T. Jackson, Andrew Y. Ng, Sebastian Thrun:
A probabilistic approach to mixed open-loop and closed-loop control, with application to extreme autonomous driving. ICRA 2010: 839-845 - [c115]Quoc V. Le, David Kamm, Arda F. Kara, Andrew Y. Ng:
Learning to grasp objects with multiple contact points. ICRA 2010: 5062-5069 - [c114]Deepak Rao, Quoc V. Le, Thanathorn Phoka, Morgan Quigley, Attawith Sudsang, Andrew Y. Ng:
Grasping novel objects with depth segmentation. IROS 2010: 2578-2585 - [c113]Ellen Klingbeil, Ashutosh Saxena, Andrew Y. Ng:
Learning to open new doors. IROS 2010: 2751-2757 - [c112]Morgan Quigley, Reuben D. Brewer, Sai Prashanth Soundararaj, Vijay Pradeep, Quoc V. Le, Andrew Y. Ng:
Low-cost accelerometers for robotic manipulator perception. IROS 2010: 6168-6174 - [c111]J. Zico Kolter, Siddharth Batra, Andrew Y. Ng:
Energy Disaggregation via Discriminative Sparse Coding. NIPS 2010: 1153-1161 - [c110]Quoc V. Le, Jiquan Ngiam, Zhenghao Chen, Daniel Jin hao Chia, Pang Wei Koh, Andrew Y. Ng:
Tiled convolutional neural networks. NIPS 2010: 1279-1287 - 2009
- [c109]Chuan-Sheng Foo, Chuong B. Do, Andrew Y. Ng:
A majorization-minimization algorithm for (multiple) hyperparameter learning. ICML 2009: 321-328 - [c108]J. Zico Kolter, Andrew Y. Ng:
Near-Bayesian exploration in polynomial time. ICML 2009: 513-520 - [c107]J. Zico Kolter, Andrew Y. Ng:
Regularization and feature selection in least-squares temporal difference learning. ICML 2009: 521-528 - [c106]Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng:
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. ICML 2009: 609-616 - [c105]Rajat Raina, Anand Madhavan, Andrew Y. Ng:
Large-scale deep unsupervised learning using graphics processors. ICML 2009: 873-880 - [c104]Ashutosh Saxena, Justin Driemeyer, Andrew Y. Ng:
Learning 3-D object orientation from images. ICRA 2009: 794-800 - [c103]J. Zico Kolter, Youngjun Kim, Andrew Y. Ng:
Stereo vision and terrain modeling for quadruped robots. ICRA 2009: 1557-1564 - [c102]J. Zico Kolter, Andrew Y. Ng:
Task-space trajectories via cubic spline optimization. ICRA 2009: 1675-1682 - [c101]Ashutosh Saxena, Andrew Y. Ng:
Learning sound location from a single microphone. ICRA 2009: 1737-1742 - [c100]Kaijen Hsiao, Paul Nangeroni, Manfred Huber, Ashutosh Saxena, Andrew Y. Ng:
Reactive grasping using optical proximity sensors. ICRA 2009: 2098-2105 - [c99]Morgan Quigley, Siddharth Batra, Stephen Gould, Ellen Klingbeil, Quoc V. Le, Ashley Wellman, Andrew Y. Ng:
High-accuracy 3D sensing for mobile manipulation: Improving object detection and door opening. ICRA 2009: 2816-2822 - [c98]Honglak Lee, Rajat Raina, Alex Teichman, Andrew Y. Ng:
Exponential Family Sparse Coding with Application to Self-taught Learning. IJCAI 2009: 1113-1119 - [c97]Quoc V. Le, Andrew Y. Ng:
Joint calibration of multiple sensors. IROS 2009: 3651-3658 - [c96]Adam Coates, Paul Baumstarck, Quoc V. Le, Andrew Y. Ng:
Scalable learning for object detection with GPU hardware. IROS 2009: 4287-4293 - [c95]Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee, Andrew Y. Ng:
Measuring Invariances in Deep Networks. NIPS 2009: 646-654 - [c94]Honglak Lee, Peter T. Pham, Yan Largman, Andrew Y. Ng:
Unsupervised feature learning for audio classification using convolutional deep belief networks. NIPS 2009: 1096-1104 - [c93]J. Zico Kolter, Andrew Y. Ng:
Policy search via the signed derivative. Robotics: Science and Systems 2009 - [c92]Savil Srivastava, Ashutosh Saxena, Christian Theobalt, Sebastian Thrun, Andrew Y. Ng:
i23 - Rapid Interactive 3D Reconstruction from a Single Image. VMV 2009: 19-28 - 2008
- [c91]Benjamin Sapp, Ashutosh Saxena, Andrew Y. Ng:
A Fast Data Collection and Augmentation Procedure for Object Recognition. AAAI 2008: 1402-1408 - [c90]Ashutosh Saxena, Lawson L. S. Wong, Andrew Y. Ng:
Learning Grasp Strategies with Partial Shape Information. AAAI 2008: 1491-1494 - [c89]Ashutosh Saxena, Min Sun, Andrew Y. Ng:
Make3D: Depth Perception from a Single Still Image. AAAI 2008: 1571-1576 - [c88]Rion Snow, Brendan O'Connor, Daniel Jurafsky, Andrew Y. Ng:
Cheap and Fast - But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks. EMNLP 2008: 254-263 - [c87]Adam Coates, Pieter Abbeel, Andrew Y. Ng:
Learning for control from multiple demonstrations. ICML 2008: 144-151 - [c86]J. Zico Kolter, Adam Coates, Andrew Y. Ng, Yi Gu, Charles DuHadway:
Space-indexed dynamic programming: learning to follow trajectories. ICML 2008: 488-495 - [c85]J. Zico Kolter, Mike P. Rodgers, Andrew Y. Ng:
A control architecture for quadruped locomotion over rough terrain. ICRA 2008: 811-818 - [c84]Pieter Abbeel, Dmitri Dolgov, Andrew Y. Ng, Sebastian Thrun:
Apprenticeship learning for motion planning with application to parking lot navigation. IROS 2008: 1083-1090 - [c83]Pieter Abbeel, Adam Coates, Timothy Hunter, Andrew Y. Ng:
Autonomous Autorotation of an RC Helicopter. ISER 2008: 385-394 - 2007
- [c82]Rion Snow, Sushant Prakash, Daniel Jurafsky, Andrew Y. Ng:
Learning to Merge Word Senses. EMNLP-CoNLL 2007: 1005-1014 - [c81]Ashutosh Saxena, Min Sun, Andrew Y. Ng:
Learning 3-D Scene Structure from a Single Still Image. ICCV 2007: 1-8 - [c80]Ashutosh Saxena, Min Sun, Andrew Y. Ng:
3-D Reconstruction from Sparse Views using Monocular Vision. ICCV 2007: 1-8 - [c79]Rajat Raina, Alexis J. Battle, Honglak Lee, Benjamin Packer, Andrew Y. Ng:
Self-taught learning: transfer learning from unlabeled data. ICML 2007: 759-766 - [c78]Stephen Gould, Joakim Arfvidsson, Adrian Kaehler, Benjamin Sapp, Marius Messner, Gary R. Bradski, Paul Baumstarck, Sukwon Chung, Andrew Y. Ng:
Peripheral-Foveal Vision for Real-time Object Recognition and Tracking in Video. IJCAI 2007: 2115-2121 - [c77]Anna Petrovskaya, Andrew Y. Ng:
Probabilistic Mobile Manipulation in Dynamic Environments, with Application to Opening Doors. IJCAI 2007: 2178-2184 - [c76]Ashutosh Saxena, Jamie Schulte, Andrew Y. Ng:
Depth Estimation Using Monocular and Stereo Cues. IJCAI 2007: 2197-2203 - [c75]Ted Kremenek, Andrew Y. Ng, Dawson R. Engler:
A Factor Graph Model for Software Bug Finding. IJCAI 2007: 2510-2516 - [c74]Ashutosh Saxena, Lawson L. S. Wong, Morgan Quigley, Andrew Y. Ng:
A Vision-Based System for Grasping Novel Objects in Cluttered Environments. ISRR 2007: 337-348 - [c73]Chuong B. Do, Chuan-Sheng Foo, Andrew Y. Ng:
Efficient multiple hyperparameter learning for log-linear models. NIPS 2007: 377-384 - [c72]J. Zico Kolter, Pieter Abbeel, Andrew Y. Ng:
Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion. NIPS 2007: 769-776 - [c71]Honglak Lee, Chaitanya Ekanadham, Andrew Y. Ng:
Sparse deep belief net model for visual area V2. NIPS 2007: 873-880 - [c70]J. Zico Kolter, Andrew Y. Ng:
Learning omnidirectional path following using dimensionality reduction. Robotics: Science and Systems 2007 - [c69]Roger B. Grosse, Rajat Raina, Helen Kwong, Andrew Y. Ng:
Shift-Invariance Sparse Coding for Audio Classification. UAI 2007: 149-158 - 2006
- [c68]Su-In Lee, Honglak Lee, Pieter Abbeel, Andrew Y. Ng:
Efficient L1 Regularized Logistic Regression. AAAI 2006: 401-408 - [c67]Rion Snow, Daniel Jurafsky, Andrew Y. Ng:
Semantic Taxonomy Induction from Heterogenous Evidence. ACL 2006 - [c66]Andrew Y. Ng:
Reinforcement Learning and Apprenticeship Learning for Robotic Control. ALT 2006: 29-31 - [c65]Mike Brzozowski, Kendra Carattini, Scott R. Klemmer, Patrick Mihelich, Jiang Hu, Andrew Y. Ng:
groupTime: preference based group scheduling. CHI 2006: 1047-1056 - [c64]Erick Delage, Honglak Lee, Andrew Y. Ng:
A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image. CVPR (2) 2006: 2418-2428 - [c63]Andrew Y. Ng:
Reinforcement Learning and Apprenticeship Learning for Robotic Control. Discovery Science 2006: 14 - [c62]Jenny Rose Finkel, Christopher D. Manning, Andrew Y. Ng:
Solving the Problem of Cascading Errors: Approximate Bayesian Inference for Linguistic Annotation Pipelines. EMNLP 2006: 618-626 - [c61]Pieter Abbeel, Morgan Quigley, Andrew Y. Ng:
Using inaccurate models in reinforcement learning. ICML 2006: 1-8 - [c60]Rajat Raina, Andrew Y. Ng, Daphne Koller:
Constructing informative priors using transfer learning. ICML 2006: 713-720 - [c59]Anna Petrovskaya, Oussama Khatib, Sebastian Thrun, Andrew Y. Ng:
Bayesian Estimation for Autonomous Object Manipulation based on Tactile Sensors. ICRA 2006: 707-714 - [c58]Honglak Lee, Yirong Shen, Chih-Han Yu, Gurjeet Singh, Andrew Y. Ng:
Quadruped Robot Obstacle Negotiation via Reinforcement Learning. ICRA 2006: 3003-3010 - [c57]Filip Krsmanovic, Curtis Spencer, Daniel Jurafsky, Andrew Y. Ng:
Have we met? MDP based speaker ID for robot dialogue. INTERSPEECH 2006 - [c56]Ashutosh Saxena, Justin Driemeyer, Justin Kearns, Chioma Osondu, Andrew Y. Ng:
Learning to Grasp Novel Objects Using Vision. ISER 2006: 33-42 - [c55]Pieter Abbeel, Adam Coates, Morgan Quigley, Andrew Y. Ng:
An Application of Reinforcement Learning to Aerobatic Helicopter Flight. NIPS 2006: 1-8 - [c54]Cheng-Tao Chu, Sang Kyun Kim, Yi-An Lin, YuanYuan Yu, Gary R. Bradski, Andrew Y. Ng, Kunle Olukotun:
Map-Reduce for Machine Learning on Multicore. NIPS 2006: 281-288 - [c53]Honglak Lee, Alexis J. Battle, Rajat Raina, Andrew Y. Ng:
Efficient sparse coding algorithms. NIPS 2006: 801-808 - [c52]Ashutosh Saxena, Justin Driemeyer, Justin Kearns, Andrew Y. Ng:
Robotic Grasping of Novel Objects. NIPS 2006: 1209-1216 - [c51]Ted Kremenek, Paul Twohey, Godmar Back, Andrew Y. Ng, Dawson R. Engler:
From Uncertainty to Belief: Inferring the Specification Within. OSDI 2006: 161-176 - [c50]Einat Minkov, William W. Cohen, Andrew Y. Ng:
Contextual search and name disambiguation in email using graphs. SIGIR 2006: 27-34 - 2005
- [c49]Rajat Raina, Andrew Y. Ng, Christopher D. Manning:
Robust Textual Inference Via Learning and Abductive Reasoning. AAAI 2005: 1099-1105 - [c48]Honglak Lee, Andrew Y. Ng:
Spam Deobfuscation using a Hidden Markov Model. CEAS 2005 - [c47]Dragomir Anguelov, Benjamin Taskar, Vassil Chatalbashev, Daphne Koller, Dinkar Gupta, Geremy Heitz, Andrew Y. Ng:
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data. CVPR (2) 2005: 169-176 - [c46]Masayoshi Matsuoka, Alan Chen, Surya P. N. Singh, Adam Coates, Andrew Y. Ng, Sebastian Thrun:
Autonomous Helicopter Tracking and Localization Using a Self-Surveying Camera Array. FSR 2005: 19-30 - [c45]Pieter Abbeel, Andrew Y. Ng:
Exploration and apprenticeship learning in reinforcement learning. ICML 2005: 1-8 - [c44]Jeff Michels, Ashutosh Saxena, Andrew Y. Ng:
High speed obstacle avoidance using monocular vision and reinforcement learning. ICML 2005: 593-600 - [c43]Erick Delage, Honglak Lee, Andrew Y. Ng:
Automatic Single-Image 3d Reconstructions of Indoor Manhattan World Scenes. ISRR 2005: 305-321 - [c42]Aria Haghighi, Andrew Y. Ng, Christopher D. Manning:
Robust Textual Inference via Graph Matching. HLT/EMNLP 2005: 387-394 - [c41]Pieter Abbeel, Varun Ganapathi, Andrew Y. Ng:
Learning vehicular dynamics, with application to modeling helicopters. NIPS 2005: 1-8 - [c40]J. Andrew Bagnell, Andrew Y. Ng:
On Local Rewards and Scaling Distributed Reinforcement Learning. NIPS 2005: 91-98 - [c39]Chuong B. Do, Andrew Y. Ng:
Transfer learning for text classification. NIPS 2005: 299-306 - [c38]Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng:
Learning Depth from Single Monocular Images. NIPS 2005: 1161-1168 - [c37]Yirong Shen, Andrew Y. Ng, Matthias W. Seeger:
Fast Gaussian Process Regression using KD-Trees. NIPS 2005: 1225-1232 - [c36]Pieter Abbeel, Adam Coates, Michael Montemerlo, Andrew Y. Ng, Sebastian Thrun:
Discriminative Training of Kalman Filters. Robotics: Science and Systems 2005: 289-296 - [c35]Pieter Abbeel, Daphne Koller, Andrew Y. Ng:
Learning Factor Graphs in Polynomial Time & Sample Complexity. UAI 2005: 1-9 - 2004
- [c34]Pieter Abbeel, Andrew Y. Ng:
Apprenticeship learning via inverse reinforcement learning. ICML 2004 - [c33]Shai Shalev-Shwartz, Yoram Singer, Andrew Y. Ng:
Online and batch learning of pseudo-metrics. ICML 2004 - [c32]Kristina Toutanova, Christopher D. Manning, Andrew Y. Ng:
Learning random walk models for inducing word dependency distributions. ICML 2004 - [c31]Andrew Y. Ng, Adam Coates, Mark Diel, Varun Ganapathi, Jamie Schulte, Ben Tse, Eric Berger, Eric Liang:
Autonomous Inverted Helicopter Flight via Reinforcement Learning. ISER 2004: 363-372 - [c30]Pieter Abbeel, Andrew Y. Ng:
Learning first-order Markov models for control. NIPS 2004: 1-8 - [c29]Sham M. Kakade, Andrew Y. Ng:
Online Bounds for Bayesian Algorithms. NIPS 2004: 641-648 - [c28]Andrew Y. Ng, H. Jin Kim:
Stable adaptive control with online learning. NIPS 2004: 977-984 - [c27]Rion Snow, Daniel Jurafsky, Andrew Y. Ng:
Learning Syntactic Patterns for Automatic Hypernym Discovery. NIPS 2004: 1297-1304 - 2003
- [c26]Rajat Raina, Yirong Shen, Andrew Y. Ng, Andrew McCallum:
Classification with Hybrid Generative/Discriminative Models. NIPS 2003: 545-552 - [c25]Andrew Y. Ng, H. Jin Kim, Michael I. Jordan, Shankar Sastry:
Autonomous Helicopter Flight via Reinforcement Learning. NIPS 2003: 799-806 - [c24]J. Andrew Bagnell, Sham M. Kakade, Andrew Y. Ng, Jeff G. Schneider:
Policy Search by Dynamic Programming. NIPS 2003: 831-838 - 2002
- [c23]Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, Stuart Russell:
Distance Metric Learning with Application to Clustering with Side-Information. NIPS 2002: 505-512 - [c22]Susan T. Dumais, Michele Banko, Eric Brill, Jimmy Lin, Andrew Y. Ng:
Web question answering: is more always better?. SIGIR 2002: 291-298 - [c21]Sebastian Thrun, Daphne Koller, Zoubin Ghahramani, Hugh F. Durrant-Whyte, Andrew Y. Ng:
Simultaneous Mapping and Localization with Sparse Extended Information Filters: Theory and Initial Results. WAFR 2002: 363-380 - 2001
- [c20]Andrew Y. Ng, Michael I. Jordan:
Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection. ICML 2001: 377-384 - [c19]Andrew Y. Ng, Alice X. Zheng, Michael I. Jordan:
Link Analysis, Eigenvectors and Stability. IJCAI 2001: 903-910 - [c18]David M. Blei, Andrew Y. Ng, Michael I. Jordan:
Latent Dirichlet Allocation. NIPS 2001: 601-608 - [c17]Andrew Y. Ng, Michael I. Jordan:
On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. NIPS 2001: 841-848 - [c16]Andrew Y. Ng, Michael I. Jordan, Yair Weiss:
On Spectral Clustering: Analysis and an algorithm. NIPS 2001: 849-856 - [c15]Alice X. Zheng, Andrew Y. Ng, Michael I. Jordan:
Stable Algorithms for Link Analysis. SIGIR 2001: 258-266 - [c14]Eric Brill, Jimmy Lin, Michele Banko, Susan T. Dumais, Andrew Y. Ng:
Data-Intensive Question Answering. TREC 2001 - 2000
- [c13]Andrew Y. Ng, Stuart Russell:
Algorithms for Inverse Reinforcement Learning. ICML 2000: 663-670 - [c12]Andrew Y. Ng, Michael I. Jordan:
PEGASUS: A policy search method for large MDPs and POMDPs. UAI 2000: 406-415 - 1999
- [c11]Andrew Y. Ng, Daishi Harada, Stuart Russell:
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping. ICML 1999: 278-287 - [c10]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng:
A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes. IJCAI 1999: 1324-1231 - [c9]Andrew Y. Ng, Michael I. Jordan:
Approximate Inference A lgorithms for Two-Layer Bayesian Networks. NIPS 1999: 533-539 - [c8]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng:
Approximate Planning in Large POMDPs via Reusable Trajectories. NIPS 1999: 1001-1007 - [c7]Andrew Y. Ng, Ronald Parr, Daphne Koller:
Policy Search via Density Estimation. NIPS 1999: 1022-1028 - 1998
- [c6]Scott Davies, Andrew Y. Ng, Andrew W. Moore:
Applying Online Search Techniques to Continuous-State Reinforcement Learning. AAAI/IAAI 1998: 753-760 - [c5]Andrew McCallum, Ronald Rosenfeld, Tom M. Mitchell, Andrew Y. Ng:
Improving Text Classification by Shrinkage in a Hierarchy of Classes. ICML 1998: 359-367 - [c4]Andrew Y. Ng:
On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples. ICML 1998: 404-412 - 1997
- [c3]Andrew Y. Ng:
Preventing "Overfitting" of Cross-Validation Data. ICML 1997: 245-253 - [c2]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng:
An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering. UAI 1997: 282-293 - 1995
- [c1]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron:
An Experimental and Theoretical Comparison of Model Selection Methods. COLT 1995: 21-30
Parts in Books or Collections
- 2012
- [p2]Adam Coates, Andrew Y. Ng:
Learning Feature Representations with K-Means. Neural Networks: Tricks of the Trade (2nd ed.) 2012: 561-580 - 1998
- [p1]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng:
An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering. Learning in Graphical Models 1998: 495-520
Editorship
- 2009
- [e1]Jeff A. Bilmes, Andrew Y. Ng:
UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18-21, 2009. AUAI Press 2009 [contents]
Reference Works
- 2017
- [r4]Adam Coates, Pieter Abbeel, Andrew Y. Ng:
Autonomous Helicopter Flight Using Reinforcement Learning. Encyclopedia of Machine Learning and Data Mining 2017: 75-85 - [r3]Pieter Abbeel, Andrew Y. Ng:
Inverse Reinforcement Learning. Encyclopedia of Machine Learning and Data Mining 2017: 678-682 - 2010
- [r2]Adam Coates, Pieter Abbeel, Andrew Y. Ng:
Autonomous Helicopter Flight Using Reinforcement Learning. Encyclopedia of Machine Learning 2010: 53-61 - [r1]Pieter Abbeel, Andrew Y. Ng:
Inverse Reinforcement Learning. Encyclopedia of Machine Learning 2010: 554-558
Informal and Other Publications
- 2024
- [i66]Muhammad Ahmed Chaudhry, Lyna Kim, Jeremy Irvin, Yuzu Ido, Sonia Chu, Jared Thomas Isobe, Andrew Y. Ng, Duncan Watson-Parris:
CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Clouds. CoRR abs/2401.14486 (2024) - [i65]Chih-Ying Liu, Jeya Maria Jose Valanarasu, Camila Gonzalez, Curtis P. Langlotz, Andrew Y. Ng, Sergios Gatidis:
Unlocking Robust Segmentation Across All Age Groups via Continual Learning. CoRR abs/2404.13185 (2024) - [i64]Tanvi Deshpande, Eva Prakash, Elsie Gyang Ross, Curtis P. Langlotz, Andrew Y. Ng, Jeya Maria Jose Valanarasu:
Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation. CoRR abs/2404.17033 (2024) - [i63]Yixing Jiang, Jeremy Irvin, Ji Hun Wang, Muhammad Ahmed Chaudhry, Jonathan H. Chen, Andrew Y. Ng:
Many-Shot In-Context Learning in Multimodal Foundation Models. CoRR abs/2405.09798 (2024) - 2023
- [i62]Tianyuan Huang, Timothy Dai, Zhecheng Wang, Hesu Yoon, Hao Sheng, Andrew Y. Ng, Ram Rajagopal, Jackelyn Hwang:
Detecting Neighborhood Gentrification at Scale via Street-level Visual Data. CoRR abs/2301.01842 (2023) - [i61]Cara Van Uden, Jeremy Irvin, Mars Huang, Nathan Dean, Jason Carr, Andrew Y. Ng, Curtis P. Langlotz:
How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems. CoRR abs/2305.08017 (2023) - [i60]Vivek Shankar, Xiaoli Yang, Vrishab Krishna, Brent Tan, Oscar Silva, Rebecca Rojansky, Andrew Y. Ng, Fabiola Valvert, Edward Briercheck, David Weinstock, Yasodha Natkunam, Sebastian Fernandez-Pol, Pranav Rajpurkar:
LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype. CoRR abs/2311.09574 (2023) - [i59]Ji Hun Wang, Jeremy Irvin, Beri Kohen Behar, Ha Tran, Raghav Samavedam, Quentin Hsu, Andrew Y. Ng:
Weakly-semi-supervised object detection in remotely sensed imagery. CoRR abs/2311.17449 (2023) - [i58]Jeremy Irvin, Lucas Tao, Joanne Zhou, Yuntao Ma, Langston Nashold, Benjamin Liu, Andrew Y. Ng:
USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery. CoRR abs/2312.02199 (2023) - [i57]Maya Srikanth, Jeremy Irvin, Brian Wesley Hill, Felipe Godoy, Ishan Sabane, Andrew Y. Ng:
An Empirical Study of Automated Mislabel Detection in Real World Vision Datasets. CoRR abs/2312.02200 (2023) - 2022
- [i56]Jon Braatz, Pranav Rajpurkar, Stephanie Zhang, Andrew Y. Ng, Jeanne Shen:
Deep Learning-Based Sparse Whole-Slide Image Analysis for the Diagnosis of Gastric Intestinal Metaplasia. CoRR abs/2201.01449 (2022) - [i55]Mark Mazumder, Colby R. Banbury, Xiaozhe Yao, Bojan Karlas, William Gaviria Rojas, Sudnya Frederick Diamos, Greg Diamos, Lynn He, Douwe Kiela, David Jurado, David Kanter, Rafael Mosquera, Juan Ciro, Lora Aroyo, Bilge Acun, Sabri Eyuboglu, Amirata Ghorbani, Emmett D. Goodman, Tariq Kane, Christine R. Kirkpatrick, Tzu-Sheng Kuo, Jonas Mueller, Tristan Thrush, Joaquin Vanschoren, Margaret Warren, Adina Williams, Serena Yeung, Newsha Ardalani, Praveen K. Paritosh, Ce Zhang, James Zou, Carole-Jean Wu, Cody Coleman, Andrew Y. Ng, Peter Mattson, Vijay Janapa Reddi:
DataPerf: Benchmarks for Data-Centric AI Development. CoRR abs/2207.10062 (2022) - [i54]Bryan Zhu, Nicholas Lui, Jeremy Irvin, Jimmy Le, Sahil Tadwalkar, Chenghao Wang, Zutao Ouyang, Frankie Y. Liu, Andrew Y. Ng, Robert B. Jackson:
METER-ML: A Multi-sensor Earth Observation Benchmark for Automated Methane Source Mapping. CoRR abs/2207.11166 (2022) - [i53]Yi-Lin Tsai, Jeremy Irvin, Suhas Chundi, João Estacio Gaspar Araujo, Andrew Y. Ng, Christopher B. Field, Peter K. Kitanidis:
Improving debris flow evacuation alerts in Taiwan using machine learning. CoRR abs/2208.13027 (2022) - 2021
- [i52]Alexander Ke, William Ellsworth, Oishi Banerjee, Andrew Y. Ng, Pranav Rajpurkar:
CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretation. CoRR abs/2101.06871 (2021) - [i51]Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek, Andrew Y. Ng, Matthew P. Lungren:
CheXternal: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays and External Clinical Settings. CoRR abs/2102.08660 (2021) - [i50]Soham Gadgil, Mark Endo, Emily Wen, Andrew Y. Ng, Pranav Rajpurkar:
CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation. CoRR abs/2102.10484 (2021) - [i49]Yen Nhi Truong Vu, Richard Wang, Niranjan Balachandar, Can Liu, Andrew Y. Ng, Pranav Rajpurkar:
MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation. CoRR abs/2102.10663 (2021) - [i48]Saahil Jain, Akshay Smit, Steven Q. H. Truong, Chanh D. T. Nguyen, Minh-Thanh Huynh, Mudit Jain, Victoria A. Young, Andrew Y. Ng, Matthew P. Lungren, Pranav Rajpurkar:
VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels. CoRR abs/2102.11467 (2021) - [i47]Siyu Shi, Ishaan Malhi, Kevin Tran, Andrew Y. Ng, Pranav Rajpurkar:
CheXseen: Unseen Disease Detection for Deep Learning Interpretation of Chest X-rays. CoRR abs/2103.04590 (2021) - [i46]Emma Chen, Andy Kim, Rayan Krishnan, Jin Long, Andrew Y. Ng, Pranav Rajpurkar:
CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays. CoRR abs/2103.09957 (2021) - [i45]Akshay Smit, Damir Vrabac, Yujie He, Andrew Y. Ng, Andrew L. Beam, Pranav Rajpurkar:
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning. CoRR abs/2103.14339 (2021) - [i44]Saahil Jain, Akshay Smit, Andrew Y. Ng, Pranav Rajpurkar:
Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation. CoRR abs/2104.00793 (2021) - [i43]Tianyuan Huang, Zhecheng Wang, Hao Sheng, Andrew Y. Ng, Ram Rajagopal:
Learning Neighborhood Representation from Multi-Modal Multi-Graph: Image, Text, Mobility Graph and Beyond. CoRR abs/2105.02489 (2021) - [i42]Bryan Gopal, Ryan W. Han, Gautham Raghupathi, Andrew Y. Ng, Geoffrey H. Tison, Pranav Rajpurkar:
3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations. CoRR abs/2106.04452 (2021) - [i41]Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven Q. H. Truong, Du Nguyen Duong, Tan Bui, Pierre J. Chambon, Yuhao Zhang, Matthew P. Lungren, Andrew Y. Ng, Curtis P. Langlotz, Pranav Rajpurkar:
RadGraph: Extracting Clinical Entities and Relations from Radiology Reports. CoRR abs/2106.14463 (2021) - [i40]Cécile Logé, Emily Ross, David Yaw Amoah Dadey, Saahil Jain, Adriel Saporta, Andrew Y. Ng, Pranav Rajpurkar:
Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management. CoRR abs/2108.01764 (2021) - 2020
- [i39]Sharon Zhou, Jiequan Zhang, Hang Jiang, Torbjörn Lundh, Andrew Y. Ng:
Data augmentation with Möbius transformations. CoRR abs/2002.02917 (2020) - [i38]Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek, Phil Chen, Amirhossein Kiani, Jeremy Irvin, Andrew Y. Ng, Matthew P. Lungren:
CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting. CoRR abs/2002.11379 (2020) - [i37]Akshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pareek, Andrew Y. Ng, Matthew P. Lungren:
CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT. CoRR abs/2004.09167 (2020) - [i36]Mang Tik Chiu, Xingqian Xu, Kai Wang, Jennifer A. Hobbs, Naira Hovakimyan, Thomas S. Huang, Honghui Shi, Yunchao Wei, Zilong Huang, Alexander G. Schwing, Robert Brunner, Ivan Dozier, Wyatt Dozier, Karen Ghandilyan, David Wilson, Hyunseong Park, Jun Hee Kim, Sungho Kim, Qinghui Liu, Michael C. Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg, Alexandre Barbosa, Rodrigo G. Trevisan, Bingchen Zhao, Shaozuo Yu, Siwei Yang, Yin Wang, Hao Sheng, Xiao Chen, Jingyi Su, Ram Rajagopal, Andrew Y. Ng, Van Thong Huynh, Soo-Hyung Kim, In Seop Na, Ujjwal Baid, Shubham Innani, Prasad Dutande, Bhakti Baheti, Sanjay N. Talbar, Jianyu Tang:
The 1st Agriculture-Vision Challenge: Methods and Results. CoRR abs/2004.09754 (2020) - [i35]Hao Sheng, Xiao Chen, Jingyi Su, Ram Rajagopal, Andrew Y. Ng:
Effective Data Fusion with Generalized Vegetation Index: Evidence from Land Cover Segmentation in Agriculture. CoRR abs/2005.03743 (2020) - [i34]Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Stefano Ermon:
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology. CoRR abs/2006.03680 (2020) - [i33]Nick A. Phillips, Pranav Rajpurkar, Mark Sabini, Rayan Krishnan, Sharon Zhou, Anuj Pareek, Nguyet Minh Phu, Chris Wang, Andrew Y. Ng, Matthew P. Lungren:
CheXphoto: 10, 000+ Smartphone Photos and Synthetic Photographic Transformations of Chest X-rays for Benchmarking Deep Learning Robustness. CoRR abs/2007.06199 (2020) - [i32]Damir Vrabac, Akshay Smit, Rebecca Rojansky, Yasodha Natkunam, Ranjana H. Advani, Andrew Y. Ng, Sebastian Fernandez-Pol, Pranav Rajpurkar:
DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set. CoRR abs/2009.08123 (2020) - [i31]Eric Zelikman, Sharon Zhou, Jeremy Irvin, Cooper Raterink, Hao Sheng, Jack Kelly, Ram Rajagopal, Andrew Y. Ng, David Gagne:
Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models. CoRR abs/2010.04715 (2020) - [i30]Hari Sowrirajan, Jingbo Yang, Andrew Y. Ng, Pranav Rajpurkar:
MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models. CoRR abs/2010.05352 (2020) - [i29]Viswesh Krishna, Anirudh Joshi, Philip L. Bulterys, Eric Yang, Andrew Y. Ng, Pranav Rajpurkar:
GloFlow: Global Image Alignment for Creation of Whole Slide Images for Pathology from Video. CoRR abs/2010.15269 (2020) - [i28]Jeremy Irvin, Hao Sheng, Neel Ramachandran, Sonja Johnson-Yu, Sharon Zhou, Kyle Story, Rose Rustowicz, Cooper Elsworth, Kemen Austin, Andrew Y. Ng:
ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery. CoRR abs/2011.05479 (2020) - [i27]Pranav Rajpurkar, Anirudh Joshi, Anuj Pareek, Jeremy Irvin, Andrew Y. Ng, Matthew P. Lungren:
CheXphotogenic: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays. CoRR abs/2011.06129 (2020) - [i26]Hao Sheng, Jeremy Irvin, Sasankh Munukutla, Shawn Zhang, Christopher Cross, Kyle Story, Rose Rustowicz, Cooper Elsworth, Zutao Yang, Mark Omara, Ritesh Gautam, Robert B. Jackson, Andrew Y. Ng:
OGNet: Towards a Global Oil and Gas Infrastructure Database using Deep Learning on Remotely Sensed Imagery. CoRR abs/2011.07227 (2020) - 2019
- [i25]Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Christopher Chute, Henrik Marklund, Behzad Haghgoo, Robyn L. Ball, Katie S. Shpanskaya, Jayne Seekins, David A. Mong, Safwan S. Halabi, Jesse K. Sandberg, Ricky Jones, David B. Larson, Curtis P. Langlotz, Bhavik N. Patel, Matthew P. Lungren, Andrew Y. Ng:
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. CoRR abs/1901.07031 (2019) - [i24]David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Körding, Carla P. Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer T. Chayes, Yoshua Bengio:
Tackling Climate Change with Machine Learning. CoRR abs/1906.05433 (2019) - [i23]Tony Duan, Anand Avati, Daisy Yi Ding, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler:
NGBoost: Natural Gradient Boosting for Probabilistic Prediction. CoRR abs/1910.03225 (2019) - 2018
- [i22]Anand Avati, Tony Duan, Kenneth Jung, Nigam H. Shah, Andrew Y. Ng:
Countdown Regression: Sharp and Calibrated Survival Predictions. CoRR abs/1806.08324 (2018) - [i21]Anand Avati, Stephen Pfohl, Chris Lin, Thao Nguyen, Meng Zhang, Philip Hwang, Jessica Wetstone, Kenneth Jung, Andrew Y. Ng, Nigam H. Shah:
Predicting Inpatient Discharge Prioritization With Electronic Health Records. CoRR abs/1812.00371 (2018) - 2017
- [i20]Ziang Xie, Sida I. Wang, Jiwei Li, Daniel Lévy, Aiming Nie, Dan Jurafsky, Andrew Y. Ng:
Data Noising as Smoothing in Neural Network Language Models. CoRR abs/1703.02573 (2017) - [i19]Pranav Rajpurkar, Awni Y. Hannun, Masoumeh Haghpanahi, Codie Bourn, Andrew Y. Ng:
Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks. CoRR abs/1707.01836 (2017) - [i18]Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Yi Ding, Aarti Bagul, Curtis P. Langlotz, Katie S. Shpanskaya, Matthew P. Lungren, Andrew Y. Ng:
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. CoRR abs/1711.05225 (2017) - [i17]Anand Avati, Kenneth Jung, Stephanie Harman, Lance Downing, Andrew Y. Ng, Nigam H. Shah:
Improving Palliative Care with Deep Learning. CoRR abs/1711.06402 (2017) - [i16]Pranav Rajpurkar, Jeremy Irvin, Aarti Bagul, Daisy Yi Ding, Tony Duan, Hershel Mehta, Brandon Yang, Kaylie Zhu, Dillon Laird, Robyn L. Ball, Curtis P. Langlotz, Katie S. Shpanskaya, Matthew P. Lungren, Andrew Y. Ng:
MURA Dataset: Towards Radiologist-Level Abnormality Detection in Musculoskeletal Radiographs. CoRR abs/1712.06957 (2017) - 2016
- [i15]Ziang Xie, Anand Avati, Naveen Arivazhagan, Dan Jurafsky, Andrew Y. Ng:
Neural Language Correction with Character-Based Attention. CoRR abs/1603.09727 (2016) - [i14]Sherry Ruan, Jacob O. Wobbrock, Kenny Liou, Andrew Y. Ng, James A. Landay:
Speech Is 3x Faster than Typing for English and Mandarin Text Entry on Mobile Devices. CoRR abs/1608.07323 (2016) - 2015
- [i13]Brody Huval, Tao Wang, Sameep Tandon, Jeff Kiske, Will Song, Joel Pazhayampallil, Mykhaylo Andriluka, Pranav Rajpurkar, Toki Migimatsu, Royce Cheng-Yue, Fernando A. Mujica, Adam Coates, Andrew Y. Ng:
An Empirical Evaluation of Deep Learning on Highway Driving. CoRR abs/1504.01716 (2015) - [i12]Pranav Rajpurkar, Toki Migimatsu, Jeff Kiske, Royce Cheng-Yue, Sameep Tandon, Tao Wang, Andrew Y. Ng:
Driverseat: Crowdstrapping Learning Tasks for Autonomous Driving. CoRR abs/1512.01872 (2015) - [i11]Dario Amodei, Rishita Anubhai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Erich Elsen, Jesse H. Engel, Linxi Fan, Christopher Fougner, Tony Han, Awni Y. Hannun, Billy Jun, Patrick LeGresley, Libby Lin, Sharan Narang, Andrew Y. Ng, Sherjil Ozair, Ryan Prenger, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Yi Wang, Zhiqian Wang, Chong Wang, Bo Xiao, Dani Yogatama, Jun Zhan, Zhenyao Zhu:
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin. CoRR abs/1512.02595 (2015) - 2014
- [i10]Andrew L. Maas, Awni Y. Hannun, Christopher T. Lengerich, Peng Qi, Daniel Jurafsky, Andrew Y. Ng:
Increasing Deep Neural Network Acoustic Model Size for Large Vocabulary Continuous Speech Recognition. CoRR abs/1406.7806 (2014) - [i9]Andrew L. Maas, Awni Y. Hannun, Daniel Jurafsky, Andrew Y. Ng:
First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs. CoRR abs/1408.2873 (2014) - [i8]Awni Y. Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, Andrew Y. Ng:
Deep Speech: Scaling up end-to-end speech recognition. CoRR abs/1412.5567 (2014) - 2013
- [i7]Andrew Y. Ng, Michael I. Jordan:
PEGASUS: A Policy Search Method for Large MDPs and POMDPs. CoRR abs/1301.3878 (2013) - [i6]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng:
An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering. CoRR abs/1302.1552 (2013) - [i5]Chris Piech, Jonathan Huang, Zhenghao Chen, Chuong B. Do, Andrew Y. Ng, Daphne Koller:
Tuned Models of Peer Assessment in MOOCs. CoRR abs/1307.2579 (2013) - 2012
- [i4]Jeff A. Bilmes, Andrew Y. Ng:
Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (2009). CoRR abs/1206.3959 (2012) - [i3]Roger B. Grosse, Rajat Raina, Helen Kwong, Andrew Y. Ng:
Shift-Invariance Sparse Coding for Audio Classification. CoRR abs/1206.5241 (2012) - [i2]Pieter Abbeel, Daphne Koller, Andrew Y. Ng:
Learning Factor Graphs in Polynomial Time & Sample Complexity. CoRR abs/1207.1366 (2012) - 2011
- [i1]Quoc V. Le, Rajat Monga, Matthieu Devin, Greg Corrado, Kai Chen, Marc'Aurelio Ranzato, Jeffrey Dean, Andrew Y. Ng:
Building high-level features using large scale unsupervised learning. CoRR abs/1112.6209 (2011)
Coauthor Index
Daniel Jurafsky
aka: Dan Jurafsky
aka: Dan Jurafsky
Curt Langlotz
aka: Curtis P. Langlotz
aka: Curtis P. Langlotz
[j34] [j33] [j32] [j31] [j30] [j28] [j27] [j26] [j25] [j24] [c194] [c190] [c189] [c188] [c187] [c185] [c184] [c183] [c182] [c181] [c180] [c179] [c178] [c177] [c176] [c173] [c171] [c170] [i60] [i56] [i52] [i51] [i50] [i49] [i48] [i47] [i46] [i45] [i44] [i42] [i41] [i40] [i38] [i37] [i33] [i32] [i30] [i29] [i27] [i25] [i19] [i18] [i16] [i13] [i12]
a service of 
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-07-09 05:44 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
