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2020 – today
- 2024
[j13]Xi Fang, Weijie Xu, Fiona Anting Tan, Ziqing Hu, Jiani Zhang, Yanjun Qi, Srinivasan H. Sengamedu, Christos Faloutsos:
Large Language Models (LLMs) on Tabular Data: Prediction, Generation, and Understanding - A Survey. Trans. Mach. Learn. Res. 2024 (2024)- [c70]Tong Wang, Ninad Kulkarni, Yanjun Qi:
Less is More for Improving Automatic Evaluation of Factual Consistency. NAACL (Industry Track) 2024: 324-334 - [i54]Xi Fang, Weijie Xu, Fiona Anting Tan, Jiani Zhang, Ziqing Hu, Yanjun Qi, Scott Nickleach, Diego Socolinsky, Srinivasan H. Sengamedu, Christos Faloutsos:
Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding - A Survey. CoRR abs/2402.17944 (2024) - [i53]Tong Wang, Ninad Kulkarni, Yanjun Qi:
Less is More for Improving Automatic Evaluation of Factual Consistency. CoRR abs/2404.06579 (2024) - [i52]Dmitriy Bespalov, Sourav Bhabesh, Yi Xiang, Liutong Zhou, Yanjun Qi:
Towards Building a Robust Toxicity Predictor. CoRR abs/2404.08690 (2024) - [i51]Mihai Christodorescu, Ryan Craven, Soheil Feizi, Neil Gong, Mia Hoffmann, Somesh Jha, Zhengyuan Jiang, Mehrdad Saberi Kamarposhti, John C. Mitchell, Jessica Newman, Emelia Probasco, Yanjun Qi, Khawaja Shams, Matthew Turek:
Securing the Future of GenAI: Policy and Technology. CoRR abs/2407.12999 (2024) - [i50]Mihai Christodorescu, Ryan Craven, Soheil Feizi, Neil Zhenqiang Gong, Mia Hoffmann, Somesh Jha, Zhengyuan Jiang, Mehrdad Saberi Kamarposhti, John C. Mitchell, Jessica Newman, Emelia Probasco, Yanjun Qi, Khawaja Shams, Matthew Turek:
Securing the Future of GenAI: Policy and Technology. IACR Cryptol. ePrint Arch. 2024: 855 (2024) - 2023
- [c69]Arshdeep Sekhon, Hanjie Chen, Aman Shrivastava, Zhe Wang, Yangfeng Ji, Yanjun Qi:
Improving Interpretability via Explicit Word Interaction Graph Layer. AAAI 2023: 13528-13537 - [c68]Dmitriy Bespalov, Sourav Bhabesh, Yi Xiang, Liutong Zhou, Yanjun Qi:
Towards Building a Robust Toxicity Predictor. ACL (industry) 2023: 581-598 - [c67]Aman Shrivastava, Yanjun Qi, Vicente Ordonez:
Estimating and Maximizing Mutual Information for Knowledge Distillation. CVPR Workshops 2023: 48-57 - [c66]Zhe Wang, Jake Grigsby, Yanjun Qi:
PGrad: Learning Principal Gradients For Domain Generalization. ICLR 2023 - [i49]Arshdeep Sekhon, Hanjie Chen, Aman Shrivastava, Zhe Wang, Yangfeng Ji, Yanjun Qi:
Improving Interpretability via Explicit Word Interaction Graph Layer. CoRR abs/2302.02016 (2023) - [i48]Zhe Wang, Jake Grigsby, Yanjun Qi:
PGrad: Learning Principal Gradients For Domain Generalization. CoRR abs/2305.01134 (2023) - [i47]Hanyu Liu, Chengyuan Cai, Yanjun Qi:
Expanding Scope: Adapting English Adversarial Attacks to Chinese. CoRR abs/2306.04874 (2023) - [i46]Zifan Xu, Haozhu Wang, Dmitriy Bespalov, Peter Stone, Yanjun Qi:
Latent Skill Discovery for Chain-of-Thought Reasoning. CoRR abs/2312.04684 (2023) - 2022
- [j12]In Kee Kim
, Wei Wang, Yanjun Qi, Marty Humphrey:
Forecasting Cloud Application Workloads With CloudInsight for Predictive Resource Management. IEEE Trans. Cloud Comput. 10(3): 1848-1863 (2022) - [c65]Arshdeep Sekhon, Zhe Wang, Yanjun Qi:
Beyond Data Samples: Aligning Differential Networks Estimation with Scientific Knowledge. AISTATS 2022: 10881-10923 - [c64]Vanamala Venkataswamy, Jake Grigsby, Andrew Grimshaw, Yanjun Qi:
RARE: Renewable Energy Aware Resource Management in Datacenters. JSSPP 2022: 108-130 - [c63]Arshdeep Sekhon, Yangfeng Ji, Matthew B. Dwyer, Yanjun Qi:
White-box Testing of NLP models with Mask Neuron Coverage. NAACL-HLT (Findings) 2022: 1547-1558 - [c62]Zhe Wang, Jake Grigsby, Arshdeep Sekhon, Yanjun Qi:
ST-MAML : A stochastic-task based method for task-heterogeneous meta-learning. UAI 2022: 2066-2074 - [i45]Arshdeep Sekhon, Yangfeng Ji, Matthew B. Dwyer, Yanjun Qi:
White-box Testing of NLP models with Mask Neuron Coverage. CoRR abs/2205.05050 (2022) - [i44]Vanamala Venkataswamy, Jake Grigsby, Andrew Grimshaw, Yanjun Qi:
RARE: Renewable Energy Aware Resource Management in Datacenters. CoRR abs/2211.05346 (2022) - [i43]Paola Cascante-Bonilla, Leonid Karlinsky, James Seale Smith, Yanjun Qi, Vicente Ordonez:
On the Transferability of Visual Features in Generalized Zero-Shot Learning. CoRR abs/2211.12494 (2022) - [i42]Vanamala Venkataswamy, Jake Grigsby, Andrew Grimshaw, Yanjun Qi:
Launchpad: Learning to Schedule Using Offline and Online RL Methods. CoRR abs/2212.00639 (2022) - 2021
- [c61]Paola Cascante-Bonilla, Fuwen Tan, Yanjun Qi, Vicente Ordonez:
Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning. AAAI 2021: 6912-6920 - [c60]Jack Lanchantin, Tom Weingarten, Arshdeep Sekhon, Clint Miller, Yanjun Qi:
Transfer learning for predicting virus-host protein interactions for novel virus sequences. BCB 2021: 36:1-36:10 - [c59]Sanchit Sinha, Hanjie Chen, Arshdeep Sekhon, Yangfeng Ji, Yanjun Qi:
Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing. BlackboxNLP@EMNLP 2021: 420-434 - [c58]Paola Cascante-Bonilla, Arshdeep Sekhon, Yanjun Qi, Vicente Ordonez:
Evolving Image Compositions for Feature Representation Learning. BMVC 2021: 199 - [c57]Jack Lanchantin, Tianlu Wang, Vicente Ordonez, Yanjun Qi:
General Multi-Label Image Classification With Transformers. CVPR 2021: 16478-16488 - [c56]Jin Yong Yoo, Yanjun Qi:
Towards Improving Adversarial Training of NLP Models. EMNLP (Findings) 2021: 945-956 - [i41]Arshdeep Sekhon, Zhe Wang, Yanjun Qi:
Relate and Predict: Structure-Aware Prediction with Jointly Optimized Neural DAG. CoRR abs/2103.02405 (2021) - [i40]Jake Grigsby, Jin Yong Yoo, Yanjun Qi:
Towards Automatic Actor-Critic Solutions to Continuous Control. CoRR abs/2106.08918 (2021) - [i39]Paola Cascante-Bonilla, Arshdeep Sekhon, Yanjun Qi, Vicente Ordonez:
Evolving Image Compositions for Feature Representation Learning. CoRR abs/2106.09011 (2021) - [i38]Sanchit Sinha, Hanjie Chen, Arshdeep Sekhon, Yangfeng Ji, Yanjun Qi:
Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing. CoRR abs/2108.04990 (2021) - [i37]Jin Yong Yoo, Yanjun Qi:
Towards Improving Adversarial Training of NLP Models. CoRR abs/2109.00544 (2021) - [i36]Jake Grigsby, Zhe Wang, Yanjun Qi:
Long-Range Transformers for Dynamic Spatiotemporal Forecasting. CoRR abs/2109.12218 (2021) - [i35]Zhe Wang, Jake Grigsby, Arshdeep Sekhon, Yanjun Qi:
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning. CoRR abs/2109.13305 (2021) - [i34]Jake Grigsby, Yanjun Qi:
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise Datasets. CoRR abs/2110.04698 (2021) - [i33]Aman Shrivastava, Yanjun Qi, Vicente Ordonez:
Estimating and Maximizing Mutual Information for Knowledge Distillation. CoRR abs/2110.15946 (2021) - 2020
- [c55]Jin Yong Yoo, John X. Morris, Eli Lifland, Yanjun Qi:
Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples. BlackboxNLP@EMNLP 2020: 323-332 - [c54]John X. Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, Yanjun Qi:
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP. EMNLP (Demos) 2020: 119-126 - [c53]John X. Morris, Eli Lifland, Jack Lanchantin, Yangfeng Ji, Yanjun Qi:
Reevaluating Adversarial Examples in Natural Language. EMNLP (Findings) 2020: 3829-3839 - [i32]Paola Cascante-Bonilla, Fuwen Tan, Yanjun Qi, Vicente Ordonez:
Curriculum Labeling: Self-paced Pseudo-Labeling for Semi-Supervised Learning. CoRR abs/2001.06001 (2020) - [i31]Arshdeep Sekhon, Beilun Wang, Zhe Wang, Yanjun Qi:
Differential Network Learning Beyond Data Samples. CoRR abs/2004.11494 (2020) - [i30]John X. Morris, Eli Lifland, Jack Lanchantin, Yangfeng Ji, Yanjun Qi:
Reevaluating Adversarial Examples in Natural Language. CoRR abs/2004.14174 (2020) - [i29]John X. Morris, Eli Lifland, Jin Yong Yoo, Yanjun Qi:
TextAttack: A Framework for Adversarial Attacks in Natural Language Processing. CoRR abs/2005.05909 (2020) - [i28]Jin Yong Yoo, John X. Morris, Eli Lifland, Yanjun Qi:
Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples. CoRR abs/2009.06368 (2020) - [i27]John X. Morris, Jin Yong Yoo, Yanjun Qi:
TextAttack: Lessons learned in designing Python frameworks for NLP. CoRR abs/2010.01724 (2020) - [i26]Jake Grigsby, Yanjun Qi:
Measuring Visual Generalization in Continuous Control from Pixels. CoRR abs/2010.06740 (2020) - [i25]Jack Lanchantin, Tianlu Wang, Vicente Ordonez, Yanjun Qi:
General Multi-label Image Classification with Transformers. CoRR abs/2011.14027 (2020)
2010 – 2019
- 2019
- [j11]Ritambhara Singh, Jack Lanchantin, Gabriel Robins, Yanjun Qi:
Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction. IEEE ACM Trans. Comput. Biol. Bioinform. 16(5): 1524-1536 (2019) - [c52]Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi:
Neural Message Passing for Multi-label Classification. ECML/PKDD (2) 2019: 138-163 - [i24]Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi:
Neural Message Passing for Multi-Label Classification. CoRR abs/1904.08049 (2019) - 2018
- [j10]Arshdeep Sekhon, Ritambhara Singh, Yanjun Qi:
DeepDiff: DEEP-learning for predicting DIFFerential gene expression from histone modifications. Bioinform. 34(17): i891-i900 (2018) - [c51]In Kee Kim, Wei Wang, Yanjun Qi, Marty Humphrey:
CloudInsight: Utilizing a Council of Experts to Predict Future Cloud Application Workloads. IEEE CLOUD 2018: 41-48 - [c50]Beilun Wang, Arshdeep Sekhon, Yanjun Qi:
Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure. AISTATS 2018: 1691-1700 - [c49]Yanjun Qi:
ACM-BCB'18 Tutorial: Making Deep Learning Understandable for Analyzing Sequential Data about Gene Regulation. BCB 2018: 556 - [c48]Beilun Wang, Arshdeep Sekhon, Yanjun Qi:
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models. ICML 2018: 5148-5157 - [c47]Weilin Xu, David Evans, Yanjun Qi:
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks. NDSS 2018 - [c46]Ji Gao, Jack Lanchantin, Mary Lou Soffa, Yanjun Qi:
Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers. IEEE Symposium on Security and Privacy Workshops 2018: 50-56 - [i23]Ji Gao, Jack Lanchantin, Mary Lou Soffa, Yanjun Qi:
Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers. CoRR abs/1801.04354 (2018) - [i22]Beilun Wang, Arshdeep Sekhon, Yanjun Qi:
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models. CoRR abs/1806.00548 (2018) - [i21]Arshdeep Sekhon, Ritambhara Singh, Yanjun Qi:
DeepDiff: Deep-learning for predicting Differential gene expression from histone modifications. CoRR abs/1807.03878 (2018) - 2017
- [j9]Beilun Wang, Ritambhara Singh, Yanjun Qi:
A constrained $$\ell $$ ℓ 1 minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models. Mach. Learn. 106(9-10): 1381-1417 (2017) - [c45]Beilun Wang, Ji Gao, Yanjun Qi:
A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models. AISTATS 2017: 1168-1177 - [c44]Ji Gao, Beilun Wang, Zeming Lin, Weilin Xu, Yanjun Qi:
DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples. ICLR (Workshop) 2017 - [c43]Jack Lanchantin, Ritambhara Singh, Yanjun Qi:
Memory Matching Networks for Genomic Sequence Classification. ICLR (Workshop) 2017 - [c42]Beilun Wang, Ji Gao, Yanjun Qi:
A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples. ICLR (Workshop) 2017 - [c41]Ritambhara Singh, Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi:
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin. NIPS 2017: 6785-6795 - [c40]Ritambhara Singh, Arshdeep Sekhon, Kamran Kowsari, Jack Lanchantin, Beilun Wang, Yanjun Qi:
GaKCo: A Fast Gapped k-mer String Kernel Using Counting. ECML/PKDD (1) 2017: 356-373 - [c39]Jack Lanchantin, Ritambhara Singh, Beilun Wang, Yanjun Qi:
Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks. PSB 2017: 254-265 - [c38]Andrew P. Norton, Yanjun Qi:
Adversarial-Playground: A visualization suite showing how adversarial examples fool deep learning. VizSEC 2017: 1-4 - [i20]Beilun Wang, Ji Gao, Yanjun Qi:
A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models. CoRR abs/1702.02715 (2017) - [i19]Jack Lanchantin, Ritambhara Singh, Yanjun Qi:
Memory Matching Networks for Genomic Sequence Classification. CoRR abs/1702.06760 (2017) - [i18]Muthuraman Chidambaram, Yanjun Qi:
Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently. CoRR abs/1702.06762 (2017) - [i17]Ji Gao, Beilun Wang, Yanjun Qi:
DeepMask: Masking DNN Models for robustness against adversarial samples. CoRR abs/1702.06763 (2017) - [i16]Weilin Xu, David Evans, Yanjun Qi:
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks. CoRR abs/1704.01155 (2017) - [i15]Ritambhara Singh, Arshdeep Sekhon, Kamran Kowsari, Jack Lanchantin, Beilun Wang, Yanjun Qi:
GaKCo: a Fast GApped k-mer string Kernel using COunting. CoRR abs/1704.07468 (2017) - [i14]Weilin Xu, David Evans, Yanjun Qi:
Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples. CoRR abs/1705.10686 (2017) - [i13]Andrew P. Norton, Yanjun Qi:
Adversarial-Playground: A Visualization Suite for Adversarial Sample Generation. CoRR abs/1706.01763 (2017) - [i12]Ritambhara Singh, Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi:
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin. CoRR abs/1708.00339 (2017) - [i11]Andrew P. Norton, Yanjun Qi:
Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning. CoRR abs/1708.00807 (2017) - [i10]Chandan Singh, Beilun Wang, Yanjun Qi:
A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs. CoRR abs/1709.04090 (2017) - [i9]Beilun Wang, Arshdeep Sekhon, Yanjun Qi:
Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure. CoRR abs/1710.11223 (2017) - [i8]Jack Lanchantin, Arshdeep Sekhon, Ritambhara Singh, Yanjun Qi:
Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification. CoRR abs/1710.11238 (2017) - 2016
- [j8]Ritambhara Singh, Jack Lanchantin, Gabriel Robins, Yanjun Qi:
DeepChrome: deep-learning for predicting gene expression from histone modifications. Bioinform. 32(17): 639-648 (2016) - [j7]Jiaqi Gong, Philip Asare, Yanjun Qi, John C. Lach:
Piecewise Linear Dynamical Model for Action Clustering from Real-World Deployments of Inertial Body Sensors. IEEE Trans. Affect. Comput. 7(3): 231-242 (2016) - [j6]Jiaqi Gong, Yanjun Qi, Myla D. Goldman, John C. Lach:
Causality Analysis of Inertial Body Sensors for Multiple Sclerosis Diagnostic Enhancement. IEEE J. Biomed. Health Informatics 20(5): 1273-1280 (2016) - [j5]Feiyu Xiong, Moshe Kam, Leonid Hrebien, Beilun Wang, Yanjun Qi:
Kernelized Information-Theoretic Metric Learning for Cancer Diagnosis Using High-Dimensional Molecular Profiling Data. ACM Trans. Knowl. Discov. Data 10(4): 38:1-38:23 (2016) - [c37]In Kee Kim, Wei Wang, Yanjun Qi, Marty Humphrey:
Empirical Evaluation of Workload Forecasting Techniques for Predictive Cloud Resource Scaling. CLOUD 2016: 1-10 - [c36]Zeming Lin, Jack Lanchantin, Yanjun Qi:
MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-Based Protein Structure Prediction. AAAI 2016: 27-34 - [c35]Ritambhara Singh, Yanjun Qi:
Character based String Kernels for Bio-Entity Relation Detection. BioNLP@ACL 2016: 66-71 - [c34]Weilin Xu, Yanjun Qi, David Evans:
Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers. NDSS 2016 - [i7]Jack Lanchantin, Ritambhara Singh, Zeming Lin, Yanjun Qi:
Deep Motif: Visualizing Genomic Sequence Classifications. CoRR abs/1605.01133 (2016) - [i6]Zeming Lin, Jack Lanchantin, Yanjun Qi:
MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction. CoRR abs/1605.03004 (2016) - [i5]Beilun Wang, Ritambhara Singh, Yanjun Qi:
A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models. CoRR abs/1605.03468 (2016) - [i4]Ritambhara Singh, Jack Lanchantin, Gabriel Robins, Yanjun Qi:
DeepChrome: Deep-learning for predicting gene expression from histone modifications. CoRR abs/1607.02078 (2016) - [i3]Jack Lanchantin, Ritambhara Singh, Beilun Wang, Yanjun Qi:
Deep GDashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks. CoRR abs/1608.03644 (2016) - [i2]Ritambhara Singh, Jack Lanchantin, Gabriel Robins, Yanjun Qi:
Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction. CoRR abs/1609.03490 (2016) - [i1]Beilun Wang, Ji Gao, Yanjun Qi:
A Theoretical Framework for Robustness of (Deep) Classifiers Under Adversarial Noise. CoRR abs/1612.00334 (2016) - 2015
- [c33]Jiaqi Gong, John C. Lach, Yanjun Qi, Myla D. Goldman:
Causal analysis of inertial body sensors for enhancing gait assessment separability towards multiple sclerosis diagnosis. BSN 2015: 1-6 - [c32]Sarah Masud Preum, John A. Stankovic, Yanjun Qi:
MAPer: A Multi-scale Adaptive Personalized Model for Temporal Human Behavior Prediction. CIKM 2015: 433-442 - [c31]Ke Wang, Yanjun Qi, Jeffrey J. Fox, Mircea R. Stan, Kevin Skadron:
Association Rule Mining with the Micron Automata Processor. IPDPS 2015: 689-699 - [c30]Öznur Tastan, Yanjun Qi, Jaime G. Carbonell, Judith Klein-Seetharaman:
Refining Literature Curated Protein Interactions Using Expert Opinions. Pacific Symposium on Biocomputing 2015: 318-329 - 2014
- [c29]Jiaqi Gong, Philip Asare, John C. Lach, Yanjun Qi:
Piecewise Linear Dynamical Model for Actions Clustering from Inertial Body Sensors with Considerations of Human Factors. BODYNETS 2014 - [c28]Yanjun Qi, Sujatha G. Das, Ronan Collobert, Jason Weston:
Deep Learning for Character-Based Information Extraction. ECIR 2014: 668-674 - [c27]Martin Renqiang Min, Salim A. Chowdhury, Yanjun Qi, Alex Stewart, Rachel Ostroff:
An Integrated Approach To Blood-Based Cancer Diagnosis And Biomarker Discovery. Pacific Symposium on Biocomputing 2014: 87-98 - [c26]Sujatha Das Gollapalli, Yanjun Qi, Prasenjit Mitra, C. Lee Giles:
Extracting Researcher Metadata with Labeled Features. SDM 2014: 740-748 - [c25]Yunlong He, Koray Kavukcuoglu, Yun Wang, Arthur Szlam, Yanjun Qi:
Unsupervised Feature Learning by Deep Sparse Coding. SDM 2014: 902-910 - [c24]In Kee Kim, Jacob Steele, Yanjun Qi, Marty Humphrey:
Comprehensive Elastic Resource Management to Ensure Predictable Performance for Scientific Applications on Public IaaS Clouds. UCC 2014: 355-362 - [c23]Yunlong He, Koray Kavukcuoglu, Yun Wang, Arthur Szlam, Yanjun Qi:
Unsupervised Feature Learning by Deep Sparse Coding. ICLR (Workshop) 2014 - 2012
- [c22]Dmitriy Bespalov, Yanjun Qi, Bing Bai, Ali Shokoufandeh:
Large-scale image classification using supervised spatial encoder. ICPR 2012: 581-584 - [c21]Yunlong He, Yanjun Qi, Koray Kavukcuoglu, Haesun Park:
Learning the Dependency Structure of Latent Factors. NIPS 2012: 2375-2383 - [c20]Dmitriy Bespalov, Yanjun Qi, Bing Bai, Ali Shokoufandeh:
Sentiment Classification with Supervised Sequence Embedding. ECML/PKDD (1) 2012: 159-174 - [c19]Ilia Nouretdinov, Alex Gammerman, Yanjun Qi, Judith Klein-Seetharaman:
Determining Confidence of Predicted Interactions Between HIV-1 and Human Proteins Using Conformal Method. Pacific Symposium on Biocomputing 2012: 311-322 - [c18]Yanjun Qi, Pierre-François Laquerre:
Retrieving Medical Records with "sennamed": NEC Labs America at TREC 2012 Medical Record Track. TREC 2012 - 2011
- [c17]Dmitriy Bespalov, Bing Bai, Yanjun Qi, Ali Shokoufandeh:
Sentiment classification based on supervised latent n-gram analysis. CIKM 2011: 375-382 - [c16]Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin, Jaime G. Carbonell:
Sparse Latent Semantic Analysis. SDM 2011: 474-485 - [c15]