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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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2020 – today
- 2024
[i50]Kamalika Chaudhuri, Chuan Guo, Laurens van der Maaten, Saeed Mahloujifar, Mark Tygert:
Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds. CoRR abs/2404.02866 (2024)- [i49]Ming Zhong, Aston Zhang, Xuewei Wang, Rui Hou, Wenhan Xiong, Chenguang Zhu, Zhengxing Chen, Liang Tan, Chloe Bi, Mike Lewis, Sravya Popuri, Sharan Narang, Melanie Kambadur, Dhruv Mahajan, Sergey Edunov, Jiawei Han, Laurens van der Maaten:
Law of the Weakest Link: Cross Capabilities of Large Language Models. CoRR abs/2409.19951 (2024) - 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 - [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
- [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) - [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 - [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
- [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 - [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
- [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 - [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)
2010 – 2019
- 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 - [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
- [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) - [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 - [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
- [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) - [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 - [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
- [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) - [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 - [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
- [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) - [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
- [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) - [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 - [i1]Laurens van der Maaten, Minmin Chen, Stephen Tyree, Kilian Q. Weinberger:
Marginalizing Corrupted Features. CoRR abs/1402.7001 (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) - [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 - [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] - 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) - [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
2000 – 2009
- 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
Coauthor Index
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