Laurens van der Maaten bio photo

Laurens van der Maaten

Research scientist in artificial intelligence.

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Publications

Overview

For an overview of citations of my papers, please view my Google Scholar profile.

Selected Publications

  • Llama Team, AI @ Meta. The Llama 3 Herd of Models. arXiv:2407.21783, 2024. Paper [Model]
  • B. Knott, S. Venkataraman, A.Y. Hannun, S. Sengupta, M. Ibrahim, and L.J.P. van der Maaten. CrypTen: Secure Multi-Party Computation Meets Machine Learning. To appear in Advances of Neural Information Processing Systems (NeurIPS), 2021. PDF [Code] [Talk]
  • A. Hannun, C. Guo, and L.J.P. van der Maaten. Measuring Data Leakage in Machine-Learning Models with Fisher Information. In Proceedings of Uncertainty in Artificial Intelligence (UAI), 2021. PDF [Code] [Best Paper Award]
  • D. Mahajan, R.B. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L.J.P. van der Maaten. Exploring the Limits of Weakly Supervised Pretraining. In European Conference on Computer Vision (ECCV), pages 185-201, 2018. PDF [Blog post] [Project page] [Models]
  • G. Huang, D. Chen, T. Li, F. Wu, L.J.P. van der Maaten, and K.Q. Weinberger. Multi-Scale Dense Convolutional Networks for Resource Efficient Image Classification. In International Conference on Learning Representations (ICLR), 2018. PDF [Code]
  • L.J.P. van der Maaten. Accelerating t-SNE using Tree-Based Algorithms. Journal of Machine Learning Research 15(Oct):3221-3245, 2014. PDF [Supplemental material] [Code]
  • L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9(Nov):2579-2605, 2008. PDF [Supplemental material] [Talk] [Code]

2024

  1. Llama Team, AI @ Meta. The Llama 3 Herd of Models. arXiv:2407.21783, 2024. Paper [Model]
  2. K. Chaudhuri, C. Guo, L.J.P. van der Maaten, S. Mahloujifar, and M. Tygert. Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds. To appear in Transactions on Machine Learning Research, 2024. PDF

2023

  1. V.V. Ramaswamy, S.Y. Lin, D. Zhao, A.B. Adcock, L.J.P. van der Maaten, D. Ghadiyaram, and O. Russakovsky. Beyond web-scraping: Crowd-sourcing a geographically diverse image dataset. To appear in Advances of Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks, 2023. PDF [Project page]

2022

  1. K. Desai, I. Misra, J.C. Johnson, and L.J.P. van der Maaten. Scaling up Instance Segmentation using Approximately Localized Phrases. To appear in British Machine Vision Conference (BMVC), 2022. PDF
  2. A. Roy Chowdhury, C. Guo, S. Jha, and L.J.P. van der Maaten. EIFFeL: Ensuring Integrity for Federated Learning. To appear in ACM Conference on Computer and Communications Security (ACM CCS), 2022. PDF
  3. C. Guo, B. Karrer, K. Chaudhuri, and L.J.P. van der Maaten. Bounding Training Data Reconstruction in Private (Deep) Learning. To appear in International Conference on Machine Learning (ICML), 2022. PDF [Code]
  4. M. Hall, L.J.P. van der Maaten, L. Gustafson, and A. Adcock. A Systematic Study of Bias Amplification. To appear in NeurIPS 2022 Workshop on Trustworthy and Socially Responsible Machine Learning, 2022. PDF [Code]
  5. M. Singh, L. Gustafson, A. Adcock, V. de Freitas Reis, B. Gedik, R. Prateek Kosaraju, D. Mahajan, R.B. Girshick, P. Dollár, and L.J.P. van der Maaten. Revisiting Weakly Supervised Pre-Training of Visual Perception Models. To appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. PDF [Code]
  6. R. Girdhar*, M. Singh*, N. Ravi*, L.J.P. van der Maaten, A. Joulin, and I. Misra*. Omnivore: A Single Model for Many Visual Modalities. To appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. PDF [Code]
  7. A.A. Ginart, L.J.P. van der Maaten, J. Zou, and C. Guo. Submix: Practical Private Prediction for Large-Scale Language Models. arXiv:2201.00971, 2022. PDF [Code]
  8. M. Xu, L.J.P. van der Maaten, A.Y. Hannun. Data Appraisal Without Data Sharing. To appear in Proceedings of Artificial Intelligence and Statistics Conference (AISTATS), 2022. PDF

2021

  1. B. Knott, S. Venkataraman, A.Y. Hannun, S. Sengupta, M. Ibrahim, and L.J.P. van der Maaten. CrypTen: Secure Multi-Party Computation Meets Machine Learning. To appear in Advances of Neural Information Processing Systems (NeurIPS), 2021. PDF [Code] [Talk]
  2. R. Wu, C. Guo, A. Hannun, and L.J.P. van der Maaten. Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems. To appear in Advances of Neural Information Processing Systems (NeurIPS), 2021. PDF [Code]
  3. A. Hannun, C. Guo, and L.J.P. van der Maaten. Measuring Data Leakage in Machine-Learning Models with Fisher Information. In Proceedings of Uncertainty in Artificial Intelligence (UAI), 2021. PDF [Code] [Best Paper Award]
  4. R. Wu, C. Guo, F. Wu, R. Kidambi, L.J.P. van der Maaten, and K.Q. Weinberger. Making Paper Reviewing Robust to Bid Manipulation Attacks. In Proceedings of the International Conference on Machine Learning (ICML), 2021. PDF [Code]
  5. C. Guo, A. Hannun, B. Knott, L.J.P. van der Maaten, M. Tygert, and R. Zhu. Secure Multiparty Computations in Floating-Point Arithmetic. In Information and Inference: A Journal of the IMA, 2021. PDF
  6. E. Ahmed, A. Bakhtin, L.J.P. van der Maaten, and R. Girdhar. Physical Reasoning Using Dynamics-Aware Models. arXiv:2102.10366, 2021. PDF [Project page] [Code]

2020

  1. L.J.P. van der Maaten* and A.Y. Hannun*. The Trade-Offs of Private Prediction. arXiv:2007.05089, 2020. PDF [Code]
  2. M. Xu, L.J.P. van der Maaten, and A.Y. Hannun. Data Appraisal Without Data Sharing. In NeurIPS Workshop on Privacy-Preserving Machine Learning, 2020. PDF
  3. K.R. Allen*, A. Bakhtin*, K. Smith, J. Tenenbaum, and L.J.P. van der Maaten. OGRE: An Object-Based Generalization for Reasoning Environment. In NeurIPS Workshop on Object Representations for Learning and Reasoning, 2020. PDF [Code]
  4. R. Girdhar, L. Gustafson, A. Adcock, and L.J.P. van der Maaten. Forward Prediction for Physical Reasoning. arXiv:2006.10734, 2020. PDF
  5. C. Guo, T. Goldstein, A. Hannun, and L.J.P. van der Maaten. Certified Data Removal from Machine Learning Models. In International Conference on Machine Learning (ICML), 2020. PDF [Code]
  6. I. Misra and L.J.P. van der Maaten. Self-Supervised Learning of Pretext-Invariant Representations. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. PDF

2019

  1. Y. Wang, W.L. Chao, K.Q. Weinberger, L.J.P. van der Maaten. SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning. arXiv:1911.04623, 2019. PDF [Code]
  2. A. Hannun*, B. Knott*, S. Sengupta, and L.J.P. van der Maaten. Privacy-Preserving Multi-Party Contextual Bandits. arXiv:1901.06595, 2019. PDF [Code]
  3. Y. Cui, Z. Gu, D. Mahajan, L.J.P. van der Maaten, S. Belongie, and S.-N. Lim. Measuring Dataset Granularity. arXiv:1912.10154, 2019. PDF
  4. A. Bakhtin, L.J.P. van der Maaten, J.C. Johnson, L. Gustafson, and R.B. Girshick. PHYRE: A New Benchmark for Physical Reasoning. In Advances of Neural Information Processing (NeurIPS), 2019. PDF [Website] [Demo] [Blog post] [Code]
  5. T. DeVries*, I. Misra*, C. Wang*, and L.J.P. van der Maaten. Does Object Recognition Work for Everyone? In CVPR Workshop on Computer Vision for Global Challenges, 2019. PDF [Blog post]
  6. H. Hu, I. Misra, and L.J.P. van der Maaten. Evaluating Text-to-Image Matching using Binary Image Selection (BISON). In ICCV Workshop on Closing the Loop Between Vision and Language, 2019. PDF [Project website] [Code]
  7. Y. Wang, Z. Lai, G. Huang, B.H. Wang, L.J.P. van der Maaten, M. Campbell, and K.Q. Weinberger. Anytime Stereo Image Depth Estimation on Mobile Devices. In International Conference on Robotics and Automation (ICRA), 2019. PDF [Code]
  8. A. Dubey, L.J.P. van der Maaten, Z. Yalniz, Y. Li, and D. Mahajan. Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search. In International Conference on Computer Vision and Pattern Recognition (CVPR), pages 8767-8776, 2019. PDF
  9. C. Xie, Y. Wu, L.J.P. van der Maaten, A. Yuille, and K. He. Feature Denoising for Improving Adversarial Robustness. In International Conference on Computer Vision and Pattern Recognition (CVPR), pages 501-509, 2019. PDF [Code]
  10. G. Huang, Z. Liu, G. Pleiss, L.J.P. van der Maaten, and K.Q. Weinberger. Convolutional Networks with Dense Connectivity. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019. PDF

2018

  1. D. Mahajan, R.B. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L.J.P. van der Maaten. Exploring the Limits of Weakly Supervised Pretraining. In European Conference on Computer Vision (ECCV), pages 185-201, 2018. PDF [Blog post] [Project page] [Models]
  2. I. Misra, R.B. Girshick, R. Fergus, M. Hebert, A. Gupta, and L.J.P. van der Maaten. Learning by Asking Questions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 11-20, 2018. PDF [Project page]
  3. A. Veit, M. Nickel, S. Belongie, and L.J.P. van der Maaten. Separating Self-Expression and Visual Content in Hashtag Supervision. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5919-5927, 2018. PDF
  4. G. Huang, S. Liu, L.J.P. van der Maaten, and K.Q. Weinberger. CondenseNet: An Efficient DenseNet using Learned Group Convolutions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2752-2761, 2018. PDF [Code]
  5. B. Graham, M. Engelcke, and L.J.P. van der Maaten. 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 9224-9232, 2018. PDF
  6. C. Guo, M. Rana, M. Cisse, and L.J.P. van der Maaten. Countering Adversarial Images using Input Transformations. In International Conference on Learning Representations (ICLR), 2018. PDF [Code]
  7. G. Huang, D. Chen, T. Li, F. Wu, L.J.P. van der Maaten, and K.Q. Weinberger. Multi-Scale Dense Convolutional Networks for Resource Efficient Image Classification. In International Conference on Learning Representations (ICLR), 2018. PDF [Code]
  8. K. van Hecke, G. de Croon, L.J.P. van der Maaten, D. Hennes, and D. Izzo. Persistent Self-Supervised Learning Principle: From Stereo to Monocular Vision for Obstacle Avoidance. In International Journal of Micro Air Vehicles 10(2):186-206, 2018. PDF

2017

  1. G. Huang, Z. Liu, L.J.P. van der Maaten, and K.Q. Weinberger. Densely Connected Convolutional Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. PDF [Talk by Gao Huang] [Best Paper Award]
  2. J. Johnson, B. Hariharan, L.J.P van der Maaten, J. Hoffman, L. Fei-Fei, C.L. Zitnick, and R.B. Girshick. Inferring and Executing Programs for Visual Reasoning. In International Conference on Computer Vision (ICCV), 2017. PDF [Project Page]
  3. J. Johnson, B. Hariharan, L.J.P. van der Maaten, L. Fei-Fei, C.L. Zitnick, and R.B. Girshick. CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. PDF [Dataset]
  4. A. Li, A. Jabri, A. Joulin, and L.J.P. van der Maaten. Learning Visual N-Grams from Web Data. In International Conference on Computer Vision (ICCV), 2017. PDF
  5. B. Graham and L.J.P. van der Maaten. Submanifold Sparse Convolutional Networks. arXiv 1706.01307, 2017. PDF [Code]
  6. G. Pleiss, D. Chen, G. Huang, T. Li, L.J.P. van der Maaten, and K.Q. Weinberger. Memory-Efficient Implementation of DenseNets. arXiv 1707.06990, 2017. PDF
  7. H. Fayek, L.J.P. van der Maaten, G. Romigh, and R. Mehra. On Data-Driven Approaches to Head-Related-Transfer Function Personalization. In Audioengineering, 2017. PDF
  8. W. Pei, H. Dibeklioglu, D.M.J. Tax, and L.J.P. van der Maaten. Multivariate Time-Series Classification Using the Hidden-Unit Logistic Model. IEEE Transactions on Neural Networks and Learning Systems, 2017. PDF [Code]
  9. N. Pezzotti, B.P.F. Lelieveldt, L.J.P. van der Maaten, T. Hollt, E. Eisemann, and A. Vilanova. Approximated and User Steerable tSNE for Progressive Visual Analytics. IEEE Transactions on Visualization and Computer Graphics 23(7), 2017. PDF

2016

  1. A. Jabri, A. Joulin, and L.J.P. van der Maaten. Revisiting Visual Question Answering Baselines. In Proceedings of the European Conference on Computer Vision (ECCV), pages 727-739, 2016. PDF
  2. A. Joulin*, L.J.P. van der Maaten*, A. Jabri, and N. Vasilache (*both authors contributed equally). Learning Visual Features from Large Weakly Supervised Data. In Proceedings of the European Conference on Computer Vision (ECCV), pages 67-84, 2016. PDF [Supplemental material (64 MB)]
  3. W.M. Kouw, J.H. Krijthe, M. Loog, and L.J.P. van der Maaten. Feature-Level Domain Adaptation. In Journal of Machine Learning Research 17(171):1−32, 2016. PDF
  4. R. Collobert, L.J.P. van der Maaten, and A. Joulin. Torchnet: An Open-Source Platform for (Deep) Learning Research. ICML Machine Learning Systems Workshop, 2016. PDF
  5. W. Pei, D.M.J. Tax, and L.J.P. van der Maaten. Modeling Time Series Similarity with Siamese Recurrent Networks. arXiv 1603.04713, 2016. PDF
  6. Y. Cheng, M.T. Wong, L.J.P. van der Maaten, and E.W. Newell. Categorical Analysis of Human T Cell Heterogeneity with One-SENSE. Journal of Immunology 196(2):924-932, 2016. PDF

2015

  1. M. van Sebille, L.J.P. van der Maaten, L. Xie, K. Jarolimek, R. Santbergen, R.A.C.M.M. van Swaaij, K. Leifer, and M. Zemana. Nanocrystal Size Distribution Analysis from Transmission Electron Microscopy Images. In Nanoscale 7(48):20593-20606, 2015. PDF
  2. B.M. Hoonhout, M. Radermacher, F. Baart, and L.J.P. van der Maaten. An Automated Method for Semantic Classification of Regions in Coastal Images. Coastal Engineering 105:1-12, 2015. PDF
  3. L.J.P. van der Maaten and R.G. Erdmann. Automatic Thread-Level Canvas Analysis. IEEE Signal Processing Magazine 32(4):38-45, 2015. PDF [Code] [The National Gallery changed their attribution of Triumph of Silenus, in part, based on evidence presented in this paper.]
  4. G. Saygili, L.J.P. van der Maaten, and E.A. Hendriks. Adaptive Stereo Similarity Fusion using Confidence Measures. Computer Vision and Image Understanding 135:95-108, 2015. PDF
  5. C.C. Laczny, T. Sternal, V. Plugaru, P. Gawron, A. Atashpendar, H.H. Margossian, S. Coronado, L.J.P. van der Maaten, N. Vlassis, and P. Wilmes. VizBin - An Application for Reference-Independent Visualization and Human-Augmented Binning of Metagenomic Data. Microbiome 3(1), 2015. PDF
  6. A. Mahfouz, M. van de Giessen, L.J.P. van der Maaten, S. Huisman, M.J.T. Reinders, M.J. Hawrylycz, and B.P.F. Lelieveldt. Visualizing the spatial gene expression organization in the brain through non-linear similarity embeddings. In Methods 73:79-89, 2015. PDF

2014

  1. L.J.P. van der Maaten. Accelerating t-SNE using Tree-Based Algorithms. Journal of Machine Learning Research 15(Oct):3221-3245, 2014. PDF [Supplemental material] [Code]
  2. W. Abdelmoula, K. Škrášková, B. Balluff, R. Carreira, E. Tolner, B.P.F. Lelieveldt, L.J.P. van der Maaten, H. Morreau, A. van den Maagdenberg, R. Heeren, L. McDonnell, and J. Dijkstra. Automatic Generic Registration of Mass Spectrometry Imaging Data to Histology using Nonlinear Stochastic Embedding. Analytical Chemistry 86(18):9204-9211, 2014. PDF
  3. C.R. Johnson, Jr., P. Messier, W.A. Sethares, A.G. Klein, C. Brown, A. Hoang Do, P. Klausmeyer, P. Abry, S. Jaffard, H. Wendt, S. Roux, N. Pustelnik, N. van Noord, L.J.P. van der Maaten, E.O. Postma, J. Coddington, L.A. Daffner, H. Murata, H. Wilhelm, S. Wood, and M. Messier. Pursuing Automated Classification of Historic Photographic Papers from Raking Light Images. Journal of the American Institute for Conservation 53(3):159-170, 2014. PDF
  4. L. Zhang, H. Dibeklioglu, and L.J.P. van der Maaten. Speeding Up Tracking by Ignoring Features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1266-1273, 2014. PDF
  5. L.J.P. van der Maaten, M. Chen, S. Tyree, and K.Q. Weinberger. Marginalizing Corrupted Features. Arxiv 1402.7001, 2014. PDF [Code]
  6. L. Zhang and L.J.P. van der Maaten. Preserving Structure in Model-Free Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(4):756-769, 2014. PDF [Code]
  7. Y. Guo, H. Dibeklioglu, and L.J.P. van der Maaten. Graph-Based Kinship Recognition. In Proceedings of the International Conference on Pattern Recognition (ICPR), pages 4287-4292, 2014. PDF
  8. L. Zhang and L.J.P. van der Maaten. Improving Object Tracking by Adapting Detectors. In Proceedings of the International Conference on Pattern Recognition (ICPR), pages 1218-1223, 2014. PDF
  9. G. Saygili, L.J.P. van der Maaten, and E.A. Hendriks. Stereo Similarity Metric Fusion Using Stereo Confidence. In Proceedings of the International Conference on Pattern Recognition (ICPR), pages 2161-2166, 2014. PDF
  10. G. Saygili, L.J.P. van der Maaten, and E.A. Hendriks. Hybrid Kinect Depth Map Refinement For Transparent Objects. In Proceedings of the International Conference on Pattern Recognition (ICPR), pages 2751-2756, 2014. PDF
  11. M. Mehu and L.J.P. van der Maaten. Multimodal Integration of Audio-Visual Cues in the Communication of Agreement and Disagreement. In Journal of Nonverbal Behavior 38(4):569-597, 2014. PDF

2013

  1. J.M. Lewis, V.R. de Sa, and L.J.P. van der Maaten. Divvy: Fast and Intuitive Exploratory Data Analysis. Journal of Machine Learning Research 14(Oct):3159-5163, 2013. PDF [Code]
  2. P. Messier, C.R. Johnson, H. Wilhelm, W.A. Sethares, A.G. Klein, P. Abry, S. Jaffard, H. Wendt, S. Roux, N. Pustelni, N. van Noord, L.J.P. van der Maaten, and E.O. Postma. Automated Surface Texture Classification of Inkjet and Photographic Media. In Proceedings of the International Conference on Digital Printing Technologies, 2013. PDF
  3. L. Zhang and L.J.P. van der Maaten. Structure Preserving Object Tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1838-1845, 2013. PDF [Code]
  4. L.J.P. van der Maaten. Barnes-Hut-SNE. In Proceedings of the International Conference on Learning Representations, 2013. PDF [Talk] [Code]
  5. L.J.P. van der Maaten, M. Chen, S. Tyree, and K.Q. Weinberger. Learning with Marginalized Corrupted Features. In Proceedings of the International Conference on Machine Learning (ICML), JMLR W&CP 28:410-418, 2013. PDF [Talk] [Code]
  6. M. Grimes, W.J. Lee, L.J.P. van der Maaten, and P. Shannon. Wrangling Phosphoproteomic Data to Elucidate Cancer Signaling Pathways. PLoS One 8(1):e52884, 2013. PDF

2012

  1. L.J.P. van der Maaten and G.E. Hinton. Visualizing Non-Metric Similarities in Multiple Maps. Machine Learning, 87(1):33-35, 2012. PDF [Code and demo]
  2. L.J.P. van der Maaten and K.Q. Weinberger. Stochastic Triplet Embedding. In Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2012. PDF [Code]
  3. J. Fang, A.L. Varbanescu, J. Shen, H. Sips, G. Saygili, and L.J.P. van der Maaten. Accelerating Cost Aggregation for Real-Time Stereo Matching. In Proceedings of the IEEE International Conference on Parallel and Distributed Systems (ICPADS), pages 472-481, 2012. PDF
  4. L.J.P. van der Maaten. Audio-Visual Emotion Challenge 2012: A Simple Approach. In Proceedings of the International Conference on Multimodal Interaction (ICMI), pages 473-476, 2012. PDF
  5. J.M. Lewis, L.J.P. van der Maaten, and V.R. de Sa. A Behavioral Investigation of Dimensionality Reduction. In Proceedings of the Cognitive Science Society (CSS), pages 671-676, 2012. PDF
  6. L.J.P. van der Maaten, M. Mahecha, and S. Schmidtlein. Analyzing Floristic Inventories with Multiple Maps. Ecological Informatics 9:1-10, 2012. PDF
  7. L.J.P. van der Maaten and E.A. Hendriks. Action Unit Classification using Active Appearance Models and Conditional Random Fields. Cognitive Processing 13:507-518, 2012. PDF [Data (283 MB)]
  8. G. Saygili, L.J.P. van der Maaten, and E.A. Hendriks. Improving Segment-based Stereo Matching using SURF Key Points. In Proceedings of the IEEE International Conference on Image Processing (ICIP), 2012. PDF

2011

  1. O. Brinkkemper, L.J.P. van der Maaten, and P.J. Boon. Identification of Myosotis seeds by means of digital image analysis. Vegetation History and Archaeobotany 20(5):435-445, 2011. PDF
  2. L.J.P. van der Maaten. Learning Discriminative Fisher Kernels. In Proceedings of the International Conference on Machine Learning (ICML), pages 217-224, 2011. PDF [Code]
  3. L.J.P. van der Maaten, M. Welling, and L.K. Saul. Hidden-Unit Conditional Random Fields. In Proceedings of the International Conference on Artificial Intelligence & Statistics (AI-STATS), JMLR W&CP 15:479-488, 2011. PDF [Code]
  4. L.J.P. van der Maaten. Discussion on “Spectral Dimensionality Reduction via Maximum Entropy” (invited discussion paper). In Proceedings of the International Conference on Artificial Intelligence & Statistics (AI-STATS), JMLR W&CP 15:60-62, 2011. PDF [Talk]
  5. L.J.P. van der Maaten. Discriminative Restricted Boltzmann Machines are Universal Approximators for Discrete Data. Technical Report EWI-PRB 2011-001, Delft University of Technology, The Netherlands, 2011. PDF

2010

  1. L.J.P. van der Maaten. Fast Optimization for t-SNE. In Neural Information Processing Systems (NeurIPS) Workshop on Challenges in Data Visualization, 2010. PDF
  2. L.J.P. van der Maaten and E.O. Postma. Texton-Based Analysis of Paintings. In SPIE Optical Engineering and Applications, volume 7798-16, 2010. PDF
  3. D.J. Hu, L.J.P. van der Maaten, Y. Cho, L.K. Saul, and S. Lerner. Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development. In Advances of Neural Information Processing Systems (NeurIPS), volume 23, pages 865-873, 2010. PDF
  4. A. Gelfand, L.J.P. van der Maaten, Y. Chen, and M. Welling. On Herding and the Perceptron Cycling Theorem. In Advances of Neural Information Processing Systems (NeurIPS), volume 23, pages 694-702, 2010. PDF
  5. R. Min, L.J.P. van der Maaten, Z. Yuan, A. Bonner, and Z. Zhang. Deep Supervised t-Distributed Embedding. In Proceedings of the International Conference on Machine Learning (ICML), pages 791-798, 2010. PDF [Supplemental material]
  6. L.J.P. van der Maaten and E.A. Hendriks. Capturing Appearance Variation in Active Appearance Models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pages 34-41, 2010. PDF
  7. L.J.P. van der Maaten. Capturing Appearance Variation in Active Appearance Models. Technical Report ICT-EWI 2010-002, Delft University of Technology, The Netherlands, 2010. (Outdated by 2010 CVPR paper.)
  8. L.J.P. van der Maaten. On Herding in Deep Networks. Technical Report ICT-EWI 2010-001, Delft University of Technology, The Netherlands, 2010. PDF
  9. L.J.P. van der Maaten, A.G. Lange, and P.J. Boon. Visualization and Automatic Typology Construction of Pottery Profiles. In Proceedings of the CAA, 2010. PDF
  10. L.J.P. van der Maaten. Bayesian Mixtures of Bernoulli Distributions. Technical Report, Department of Computer Science and Engineering, University of California, San Diego, 2010. PDF

2009

  1. L.J.P. van der Maaten. Feature Extraction from Visual Data. PhD Thesis (cum laude), Tilburg University, The Netherlands, June 23rd 2009. PDF (102MB)
  2. L.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik. Dimensionality Reduction: A Comparative Review. Tilburg University Technical Report, TiCC-TR 2009-005, 2009. PDF [Code]
  3. L.J.P. van der Maaten and G.E. Hinton. Modeling Semantic Similarities in Multiple Maps. Delft University of Technology Technical Report EWI-ICT 2009-001, 2009. PDF
  4. P.J. Boon, A.G. Lange, L.J.P. van der Maaten, J.J. Paijmans, and E.O. Postma. Digital Support for Archaeology. Interdisciplinary Science Reviews 34(2-3):189-205, 2009. PDF
  5. L.J.P. van der Maaten. Preserving Local Structure in Gaussian Process Latent Variable Models. Proceedings of Benelearn-09, pages 81-88, 2009. PDF
  6. L.J.P. van der Maaten. Learning a Parametric Embedding by Preserving Local Structure. In Proceedings of the Twelfth International Conference on Artificial Intelligence & Statistics (AI-STATS), JMLR W&CP 5:384-391, 2009. PDF
  7. L.J.P. van der Maaten and E.O. Postma. Identifying the Real Van Gogh with Brushstroke Textons. Tilburg University Technical Report, TiCC TR 2009-001, 2009. (Outdated by 2010 SPIE paper.)
  8. L.J.P. van der Maaten. A New Benchmark Dataset for Handwritten Character Recognition. Tilburg University Technical Report, TiCC TR 2009-002, 2009. PDF [Dataset (42MB)]

2008

  1. L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9(Nov):2579-2605, 2008. PDF [Supplemental material] [Talk] [Code]
  2. L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. University of Toronto Technical Report, UTML TR 2008-001, 2008. (Outdated by 2008 JMLR paper.)

2007

  1. L.J.P. van der Maaten and E.O. Postma. Texton-based Texture Classification. In Proceedings of the Belgian-Dutch Artificial Intelligence Conference (BNAIC), pages 213-220. Utrecht, The Netherlands, 2007. PDF
  2. L.J.P. van der Maaten. An Introduction to Dimensionality Reduction Using Matlab. Technical Report MICC 07-07. Maastricht University, Maastricht, The Netherlands, 2007. (Outdated by 2009 Technical Report.)
  3. S. Vanderlooy, L.J.P. van der Maaten, and I. Sprinkhuizen-Kuyper. Off-line Learning with Transductive Confidence Machines: An Empirical Evaluation. In Proceedings of the International Conference on Machine Learning and Data Mining, pages 310-323. Leipzig, Germany, 2007. PDF
  4. S. Vanderlooy, L.J.P. van der Maaten, and I. Sprinkhuizen-Kuyper. Off-line learning with Transductive Confidence Machines: An Empirical Evaluation. Technical Report MICC-IKAT 07-03. Maastricht University, Maastricht, The Netherlands, 2007. (Outdated by 2007 ICML-DM paper.)

2006

  1. L.J.P. van der Maaten and P.J. Boon. COIN-O-MATIC: A Fast and Reliable System for Coin Classification. In Proceedings of the MUSCLE Coin Workshop 2006, pages 7-17. Berlin, Germany, 2006. PDF
  2. L.J.P. van der Maaten and E.O. Postma. Towards Automatic Coin Classification. In Proceedings of the EVA-Vienna, pages 19-26. Vienna, Austria, 2006. PDF
  3. L.J.P. van der Maaten, P.J. Boon, J.J. Paijmans, A.G. Lange, and E.O. Postma. Computer Vision and Machine Learning for Archaeology. In Proceedings of the CAA, pages 361-367. Fargo, ND, USA, 2006. PDF

2005

  1. L.J.P. van der Maaten and E.O. Postma. Improving Automatic Writer Identification. In Proceedings of the BNAIC, pages 260-266. Brussels, Belgium, 2005. PDF [Code]
  2. L.J.P. van der Maaten. Improving Automatic Writer Identification. MSc thesis, Maastricht University, The Netherlands, 2005.