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ORCIDLaurens van der Maaten
Laurens J. P. van der Maaten
Person information
- affiliation: Facebook AI Research, New York, NY, USA
- affiliation: Delft University of Technology, Netherlands
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Journal Articles
- 2022
[j13]Gao Huang
, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten, Kilian Q. Weinberger:
Convolutional Networks with Dense Connectivity. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 8704-8716 (2022)- 2018
- [j12]Wenjie Pei, Hamdi Dibeklioglu, David M. J. Tax, Laurens van der Maaten:
Multivariate Time-Series Classification Using the Hidden-Unit Logistic Model. IEEE Trans. Neural Networks Learn. Syst. 29(4): 920-931 (2018) - 2017
- [j11]Nicola Pezzotti, Boudewijn P. F. Lelieveldt, Laurens van der Maaten, Thomas Höllt, Elmar Eisemann, Anna Vilanova:
Approximated and User Steerable tSNE for Progressive Visual Analytics. IEEE Trans. Vis. Comput. Graph. 23(7): 1739-1752 (2017) - 2016
- [j10]Wouter M. Kouw, Laurens J. P. van der Maaten, Jesse H. Krijthe, Marco Loog:
Feature-Level Domain Adaptation. J. Mach. Learn. Res. 17: 171:1-171:32 (2016) - 2015
- [j9]Gorkem Saygili, Laurens van der Maaten, Emile A. Hendriks:
Adaptive stereo similarity fusion using confidence measures. Comput. Vis. Image Underst. 135: 95-108 (2015) - [j8]Michaël Aupetit, Laurens van der Maaten:
Introduction to the special issue on visual analytics using multidimensional projections. Neurocomputing 150: 543-545 (2015) - [j7]Laurens van der Maaten, Robert G. Erdmann:
Automatic Thread-Level Canvas Analysis: A machine-learning approach to analyzing the canvas of paintings. IEEE Signal Process. Mag. 32(4): 38-45 (2015) - 2014
- [j6]Laurens van der Maaten:
Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15(1): 3221-3245 (2014) - [j5]Lu Zhang, Laurens van der Maaten:
Preserving Structure in Model-Free Tracking. IEEE Trans. Pattern Anal. Mach. Intell. 36(4): 756-769 (2014) - 2013
- [j4]Joshua M. Lewis, Virginia R. de Sa, Laurens van der Maaten:
Divvy: fast and intuitive exploratory data analysis. J. Mach. Learn. Res. 14(1): 3159-3163 (2013) - 2012
- [j3]Laurens van der Maaten, Emile A. Hendriks:
Action unit classification using active appearance models and conditional random fields. Cogn. Process. 13(Supplement-2): 507-518 (2012) - [j2]Laurens van der Maaten, Sebastian Schmidtlein, Miguel D. Mahecha:
Analyzing floristic inventories with multiple maps. Ecol. Informatics 9: 1-10 (2012) - [j1]Laurens van der Maaten, Geoffrey E. Hinton:
Visualizing non-metric similarities in multiple maps. Mach. Learn. 87(1): 33-55 (2012)
Conference and Workshop Papers
- 2023
- [c58]Vikram V. Ramaswamy, Sing Yu Lin, Dora Zhao, Aaron Adcock, Laurens van der Maaten, Deepti Ghadiyaram, Olga Russakovsky:
GeoDE: a Geographically Diverse Evaluation Dataset for Object Recognition. NeurIPS 2023 - 2022
- [c57]Xinlei Xu, Awni Y. Hannun, Laurens van der Maaten:
Data Appraisal Without Data Sharing. AISTATS 2022: 11422-11437 - [c56]Karan Desai, Ishan Misra, Justin Johnson, Laurens van der Maaten:
Scaling up Instance Segmentation using Approximately Localized Phrases. BMVC 2022: 648 - [c55]Amrita Roy Chowdhury, Chuan Guo, Somesh Jha, Laurens van der Maaten:
EIFFeL: Ensuring Integrity for Federated Learning. CCS 2022: 2535-2549 - [c54]Mannat Singh, Laura Gustafson, Aaron Adcock, Vinicius de Freitas Reis, Bugra Gedik, Raj Prateek Kosaraju, Dhruv Mahajan, Ross B. Girshick, Piotr Dollár, Laurens van der Maaten:
Revisiting Weakly Supervised Pre-Training of Visual Perception Models. CVPR 2022: 794-804 - [c53]Rohit Girdhar, Mannat Singh, Nikhila Ravi, Laurens van der Maaten, Armand Joulin, Ishan Misra:
Omnivore: A Single Model for Many Visual Modalities. CVPR 2022: 16081-16091 - [c52]Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten:
Bounding Training Data Reconstruction in Private (Deep) Learning. ICML 2022: 8056-8071 - [c51]Awni Y. Hannun, Chuan Guo, Laurens van der Maaten:
Measuring Data Leakage in Machine-Learning Models with Fisher Information (Extended Abstract). IJCAI 2022: 5284-5288 - 2021
- [c50]Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger:
Making Paper Reviewing Robust to Bid Manipulation Attacks. ICML 2021: 11240-11250 - [c49]Brian Knott, Shobha Venkataraman, Awni Y. Hannun, Shubho Sengupta, Mark Ibrahim, Laurens van der Maaten:
CrypTen: Secure Multi-Party Computation Meets Machine Learning. NeurIPS 2021: 4961-4973 - [c48]Ruihan Wu, Chuan Guo, Awni Y. Hannun, Laurens van der Maaten:
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems. NeurIPS 2021: 11745-11756 - [c47]Awni Y. Hannun, Chuan Guo, Laurens van der Maaten:
Measuring data leakage in machine-learning models with Fisher information. UAI 2021: 760-770 - 2020
- [c46]Ishan Misra, Laurens van der Maaten:
Self-Supervised Learning of Pretext-Invariant Representations. CVPR 2020: 6706-6716 - [c45]Chuan Guo, Tom Goldstein, Awni Y. Hannun, Laurens van der Maaten:
Certified Data Removal from Machine Learning Models. ICML 2020: 3832-3842 - 2019
- [c44]Terrance DeVries, Ishan Misra, Changhan Wang, Laurens van der Maaten:
Does Object Recognition Work for Everyone? CVPR Workshops 2019: 52-59 - [c43]Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan L. Yuille, Kaiming He:
Feature Denoising for Improving Adversarial Robustness. CVPR 2019: 501-509 - [c42]Abhimanyu Dubey, Laurens van der Maaten, Zeki Yalniz, Yixuan Li, Dhruv Mahajan:
Defense Against Adversarial Images Using Web-Scale Nearest-Neighbor Search. CVPR 2019: 8767-8776 - [c41]Hexiang Hu, Ishan Misra, Laurens van der Maaten:
Evaluating Text-to-Image Matching using Binary Image Selection (BISON). ICCV Workshops 2019: 1887-1890 - [c40]Yan Wang, Zihang Lai, Gao Huang, Brian H. Wang, Laurens van der Maaten, Mark E. Campbell, Kilian Q. Weinberger:
Anytime Stereo Image Depth Estimation on Mobile Devices. ICRA 2019: 5893-5900 - [c39]Anton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson, Ross B. Girshick:
PHYRE: A New Benchmark for Physical Reasoning. NeurIPS 2019: 5083-5094 - 2018
- [c38]Ishan Misra, Ross B. Girshick, Rob Fergus, Martial Hebert, Abhinav Gupta, Laurens van der Maaten:
Learning by Asking Questions. CVPR 2018: 11-20 - [c37]Gao Huang, Shichen Liu, Laurens van der Maaten, Kilian Q. Weinberger:
CondenseNet: An Efficient DenseNet Using Learned Group Convolutions. CVPR 2018: 2752-2761 - [c36]Andreas Veit, Maximilian Nickel, Serge J. Belongie, Laurens van der Maaten:
Separating Self-Expression and Visual Content in Hashtag Supervision. CVPR 2018: 5919-5927 - [c35]Benjamin Graham, Martin Engelcke, Laurens van der Maaten:
3D Semantic Segmentation With Submanifold Sparse Convolutional Networks. CVPR 2018: 9224-9232 - [c34]Dhruv Mahajan, Ross B. Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, Laurens van der Maaten:
Exploring the Limits of Weakly Supervised Pretraining. ECCV (2) 2018: 185-201 - [c33]Taygun Kekeç, Laurens van der Maaten, David M. J. Tax:
PAWE: Polysemy Aware Word Embeddings. ICISDM 2018: 7-13 - [c32]Chuan Guo, Mayank Rana, Moustapha Cissé, Laurens van der Maaten:
Countering Adversarial Images using Input Transformations. ICLR (Poster) 2018 - [c31]Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, Kilian Q. Weinberger:
Multi-Scale Dense Networks for Resource Efficient Image Classification. ICLR 2018 - 2017
- [c30]Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, Ross B. Girshick:
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning. CVPR 2017: 1988-1997 - [c29]Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger:
Densely Connected Convolutional Networks. CVPR 2017: 2261-2269 - [c28]Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, C. Lawrence Zitnick, Ross B. Girshick:
Inferring and Executing Programs for Visual Reasoning. ICCV 2017: 3008-3017 - [c27]Ang Li, Allan Jabri, Armand Joulin, Laurens van der Maaten:
Learning Visual N-Grams from Web Data. ICCV 2017: 4193-4202 - 2016
- [c26]Armand Joulin, Laurens van der Maaten, Allan Jabri, Nicolas Vasilache:
Learning Visual Features from Large Weakly Supervised Data. ECCV (7) 2016: 67-84 - [c25]Allan Jabri, Armand Joulin, Laurens van der Maaten:
Revisiting Visual Question Answering Baselines. ECCV (8) 2016: 727-739 - 2014
- [c24]Lu Zhang, Hamdi Dibeklioglu, Laurens van der Maaten:
On Fast Trackers that are Robust to Partial Occlusions. CVPR Workshops 2014: 718-719 - [c23]Lu Zhang, Hamdi Dibeklioglu, Laurens van der Maaten:
Speeding Up Tracking by Ignoring Features. CVPR 2014: 1266-1273 - [c22]Lu Zhang, Laurens van der Maaten:
Improving Object Tracking by Adapting Detectors. ICPR 2014: 1218-1223 - [c21]Gorkem Saygili, Laurens van der Maaten, Emile A. Hendriks:
Stereo Similarity Metric Fusion Using Stereo Confidence. ICPR 2014: 2161-2166 - [c20]Gorkem Saygili, Laurens van der Maaten, Emile A. Hendriks:
Hybrid Kinect Depth Map Refinement for Transparent Objects. ICPR 2014: 2751-2756 - [c19]Yuanhao Guo, Hamdi Dibeklioglu, Laurens van der Maaten:
Graph-Based Kinship Recognition. ICPR 2014: 4287-4292 - 2013
- [c18]Lu Zhang, Laurens van der Maaten:
Structure Preserving Object Tracking. CVPR 2013: 1838-1845 - [c17]Laurens van der Maaten, Minmin Chen, Stephen Tyree, Kilian Q. Weinberger:
Learning with Marginalized Corrupted Features. ICML (1) 2013: 410-418 - [c16]Laurens van der Maaten:
Barnes-Hut-SNE. ICLR 2013 - 2012
- [c15]Joshua M. Lewis, Laurens van der Maaten, Virginia R. de Sa:
A Behavioral Investigation of Dimensionality Reduction. CogSci 2012 - [c14]Gorkem Saygili, Laurens van der Maaten, Emile A. Hendriks:
Improving segment based stereo matching using SURF key points. ICIP 2012: 2973-2976 - [c13]Laurens van der Maaten:
Audio-visual emotion challenge 2012: a simple approach. ICMI 2012: 473-476 - [c12]Jianbin Fang, Ana Lucia Varbanescu, Jie Shen, Henk J. Sips, Gorkem Saygili, Laurens van der Maaten:
Accelerating Cost Aggregation for Real-Time Stereo Matching. ICPADS 2012: 472-481 - [c11]Laurens van der Maaten, Kilian Q. Weinberger:
Stochastic triplet embedding. MLSP 2012: 1-6 - 2011
- [c10]Laurens van der Maaten:
Learning Discriminative Fisher Kernels. ICML 2011: 217-224 - [c9]Laurens van der Maaten:
Discussion of "Spectral Dimensionality Reduction via Maximum Entropy". AISTATS 2011: 60-62 - [c8]Laurens van der Maaten, Max Welling, Lawrence K. Saul:
Hidden-Unit Conditional Random Fields. AISTATS 2011: 479-488 - 2010
- [c7]Laurens van der Maaten, Emile A. Hendriks:
Capturing appearance variation in active appearance models. CVPR Workshops 2010: 34-41 - [c6]Martin Renqiang Min, Laurens van der Maaten, Zineng Yuan, Anthony J. Bonner, Zhaolei Zhang:
Deep Supervised t-Distributed Embedding. ICML 2010: 791-798 - [c5]Andrew Gelfand, Yutian Chen, Laurens van der Maaten, Max Welling:
On Herding and the Perceptron Cycling Theorem. NIPS 2010: 694-702 - [c4]Diane Hu, Laurens van der Maaten, Youngmin Cho, Lawrence K. Saul, Sorin Lerner:
Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development. NIPS 2010: 865-873 - 2009
- [c3]Laurens van der Maaten:
Learning a Parametric Embedding by Preserving Local Structure. AISTATS 2009: 384-391 - 2007
- [c2]Stijn Vanderlooy, Laurens van der Maaten, Ida G. Sprinkhuizen-Kuyper:
Off-Line Learning with Transductive Confidence Machines: An Empirical Evaluation. MLDM 2007: 310-323 - 2005
- [c1]Laurens van der Maaten, Eric O. Postma:
Improving automatic writer identification. BNAIC 2005: 260-266
Editorship
- 2013
- [e1]Michaël Aupetit, Laurens van der Maaten:
1st International Workshop on Visual Analytics Using Multidimensional Projections, VAMP@EuroVis 2013, Leipzig, Germany, June 19, 2013. Eurographics Association 2013, ISBN 978-3-905674-53-8 [contents]
Informal and Other Publications
- 2023
- [i48]Vikram V. Ramaswamy, Sing Yu Lin, Dora Zhao, Aaron B. Adcock, Laurens van der Maaten, Deepti Ghadiyaram, Olga Russakovsky:
Beyond web-scraping: Crowd-sourcing a geographically diverse image dataset. CoRR abs/2301.02560 (2023) - 2022
- [i47]Antonio Ginart, Laurens van der Maaten, James Zou, Chuan Guo:
Submix: Practical Private Prediction for Large-Scale Language Models. CoRR abs/2201.00971 (2022) - [i46]Mannat Singh, Laura Gustafson, Aaron Adcock, Vinicius de Freitas Reis, Bugra Gedik, Raj Prateek Kosaraju, Dhruv Mahajan, Ross B. Girshick, Piotr Dollár, Laurens van der Maaten:
Revisiting Weakly Supervised Pre-Training of Visual Perception Models. CoRR abs/2201.08371 (2022) - [i45]Rohit Girdhar, Mannat Singh, Nikhila Ravi, Laurens van der Maaten, Armand Joulin, Ishan Misra:
Omnivore: A Single Model for Many Visual Modalities. CoRR abs/2201.08377 (2022) - [i44]Melissa Hall, Laurens van der Maaten, Laura Gustafson, Aaron Adcock:
A Systematic Study of Bias Amplification. CoRR abs/2201.11706 (2022) - [i43]Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten:
Bounding Training Data Reconstruction in Private (Deep) Learning. CoRR abs/2201.12383 (2022) - 2021
- [i42]Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger:
Making Paper Reviewing Robust to Bid Manipulation Attacks. CoRR abs/2102.06020 (2021) - [i41]Eltayeb Ahmed, Anton Bakhtin, Laurens van der Maaten, Rohit Girdhar:
Physical Reasoning Using Dynamics-Aware Models. CoRR abs/2102.10336 (2021) - [i40]Awni Y. Hannun, Chuan Guo, Laurens van der Maaten:
Measuring Data Leakage in Machine-Learning Models with Fisher Information. CoRR abs/2102.11673 (2021) - [i39]Ruihan Wu, Chuan Guo, Awni Y. Hannun, Laurens van der Maaten:
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems. CoRR abs/2103.11766 (2021) - [i38]Brian Knott, Shobha Venkataraman, Awni Y. Hannun, Shubho Sengupta, Mark Ibrahim, Laurens van der Maaten:
CrypTen: Secure Multi-Party Computation Meets Machine Learning. CoRR abs/2109.00984 (2021) - [i37]Amrita Roy Chowdhury, Chuan Guo, Somesh Jha, Laurens van der Maaten:
EIFFeL: Ensuring Integrity for Federated Learning. CoRR abs/2112.12727 (2021) - 2020
- [i36]Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten, Kilian Q. Weinberger:
Convolutional Networks with Dense Connectivity. CoRR abs/2001.02394 (2020) - [i35]Chuan Guo, Awni Y. Hannun, Brian Knott, Laurens van der Maaten, Mark Tygert, Ruiyu Zhu:
Secure multiparty computations in floating-point arithmetic. CoRR abs/2001.03192 (2020) - [i34]Rohit Girdhar, Laura Gustafson, Aaron Adcock, Laurens van der Maaten:
Forward Prediction for Physical Reasoning. CoRR abs/2006.10734 (2020) - [i33]Laurens van der Maaten, Awni Y. Hannun:
The Trade-Offs of Private Prediction. CoRR abs/2007.05089 (2020) - [i32]Mimee Xu, Laurens van der Maaten, Awni Y. Hannun:
Data Appraisal Without Data Sharing. CoRR abs/2012.06430 (2020) - 2019
- [i31]Hexiang Hu, Ishan Misra, Laurens van der Maaten:
Binary Image Selection (BISON): Interpretable Evaluation of Visual Grounding. CoRR abs/1901.06595 (2019) - [i30]Abhimanyu Dubey, Laurens van der Maaten, Zeki Yalniz, Yixuan Li, Dhruv Mahajan:
Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search. CoRR abs/1903.01612 (2019) - [i29]Terrance DeVries, Ishan Misra, Changhan Wang, Laurens van der Maaten:
Does Object Recognition Work for Everyone? CoRR abs/1906.02659 (2019) - [i28]Anton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson, Ross B. Girshick:
PHYRE: A New Benchmark for Physical Reasoning. CoRR abs/1908.05656 (2019) - [i27]Awni Y. Hannun, Brian Knott, Shubho Sengupta, Laurens van der Maaten:
Privacy-Preserving Contextual Bandits. CoRR abs/1910.05299 (2019) - [i26]Chuan Guo, Tom Goldstein, Awni Y. Hannun, Laurens van der Maaten:
Certified Data Removal from Machine Learning Models. CoRR abs/1911.03030 (2019) - [i25]Yan Wang, Wei-Lun Chao, Kilian Q. Weinberger, Laurens van der Maaten:
SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning. CoRR abs/1911.04623 (2019) - [i24]Ishan Misra, Laurens van der Maaten:
Self-Supervised Learning of Pretext-Invariant Representations. CoRR abs/1912.01991 (2019) - [i23]Yin Cui, Zeqi Gu, Dhruv Mahajan, Laurens van der Maaten, Serge J. Belongie, Ser-Nam Lim:
Measuring Dataset Granularity. CoRR abs/1912.10154 (2019) - 2018
- [i22]Dhruv Mahajan, Ross B. Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, Laurens van der Maaten:
Exploring the Limits of Weakly Supervised Pretraining. CoRR abs/1805.00932 (2018) - [i21]Yan Wang, Zihang Lai, Gao Huang, Brian H. Wang, Laurens van der Maaten, Mark Campbell, Kilian Q. Weinberger:
Anytime Stereo Image Depth Estimation on Mobile Devices. CoRR abs/1810.11408 (2018) - [i20]Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan L. Yuille, Kaiming He:
Feature Denoising for Improving Adversarial Robustness. CoRR abs/1812.03411 (2018) - 2017
- [i19]Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, Kilian Q. Weinberger:
Multi-Scale Dense Convolutional Networks for Efficient Prediction. CoRR abs/1703.09844 (2017) - [i18]Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, C. Lawrence Zitnick, Ross B. Girshick:
Inferring and Executing Programs for Visual Reasoning. CoRR abs/1705.03633 (2017) - [i17]Benjamin Graham, Laurens van der Maaten:
Submanifold Sparse Convolutional Networks. CoRR abs/1706.01307 (2017) - [i16]Geoff Pleiss, Danlu Chen, Gao Huang, Tongcheng Li, Laurens van der Maaten, Kilian Q. Weinberger:
Memory-Efficient Implementation of DenseNets. CoRR abs/1707.06990 (2017) - [i15]Chuan Guo, Mayank Rana, Moustapha Cissé, Laurens van der Maaten:
Countering Adversarial Images using Input Transformations. CoRR abs/1711.00117 (2017) - [i14]Gao Huang, Shichen Liu, Laurens van der Maaten, Kilian Q. Weinberger:
CondenseNet: An Efficient DenseNet using Learned Group Convolutions. CoRR abs/1711.09224 (2017) - [i13]Andreas Veit, Maximilian Nickel, Serge J. Belongie, Laurens van der Maaten:
Separating Self-Expression and Visual Content in Hashtag Supervision. CoRR abs/1711.09825 (2017) - [i12]Benjamin Graham, Martin Engelcke, Laurens van der Maaten:
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks. CoRR abs/1711.10275 (2017) - [i11]Ishan Misra, Ross B. Girshick, Rob Fergus, Martial Hebert, Abhinav Gupta, Laurens van der Maaten:
Learning by Asking Questions. CoRR abs/1712.01238 (2017) - 2016
- [i10]Wenjie Pei, David M. J. Tax, Laurens van der Maaten:
Modeling Time Series Similarity with Siamese Recurrent Networks. CoRR abs/1603.04713 (2016) - [i9]Kevin van Hecke, Guido de Croon, Laurens van der Maaten, Daniel Hennes, Dario Izzo:
Persistent self-supervised learning principle: from stereo to monocular vision for obstacle avoidance. CoRR abs/1603.08047 (2016) - [i8]Allan Jabri, Armand Joulin, Laurens van der Maaten:
Revisiting Visual Question Answering Baselines. CoRR abs/1606.08390 (2016) - [i7]Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, Ross B. Girshick:
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning. CoRR abs/1612.06890 (2016) - [i6]Ang Li, Allan Jabri, Armand Joulin, Laurens van der Maaten:
Learning Visual N-Grams from Web Data. CoRR abs/1612.09161 (2016) - 2015
- [i5]Wenjie Pei, Hamdi Dibeklioglu, David M. J. Tax, Laurens van der Maaten:
Time Series Classification using the Hidden-Unit Logistic Model. CoRR abs/1506.05085 (2015) - [i4]Armand Joulin, Laurens van der Maaten, Allan Jabri, Nicolas Vasilache:
Learning Visual Features from Large Weakly Supervised Data. CoRR abs/1511.02251 (2015) - [i3]Nicola Pezzotti, Boudewijn P. F. Lelieveldt, Laurens van der Maaten, Thomas Höllt, Elmar Eisemann, Anna Vilanova:
Approximated and User Steerable tSNE for Progressive Visual Analytics. CoRR abs/1512.01655 (2015) - [i2]Wouter M. Kouw, Jesse H. Krijthe, Marco Loog, Laurens J. P. van der Maaten:
Feature-Level Domain Adaptation. CoRR abs/1512.04829 (2015) - 2014
- [i1]Laurens van der Maaten, Minmin Chen, Stephen Tyree, Kilian Q. Weinberger:
Marginalizing Corrupted Features. CoRR abs/1402.7001 (2014)
Coauthor Index
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