van der Maaten, Laurens

Densely Connected Convolutional Networks

Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2016) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 48,960

3D Semantic Segmentation with Submanifold Sparse Convolutional Networks

Graham, B., Engelcke, M., & Maaten, L. V. D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

3D Shape Modeling and AnalysisAdvanced Neural Network Applications

Cited by 1,788

Self-Supervised Learning of Pretext-Invariant Representations

Misra, I., & van der Maaten, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 1,435

Feature denoising for improving adversarial robustness

Xie, C., Wu, Y., Maaten, L. V. D., Yuille, A. L., & He, K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 940

The Llama 3 Herd of Models

Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Vaughan, A., et al. (2024) · arXiv

Fault Detection and Control SystemsAnomaly Detection Techniques and Applications

Cited by 505

CrypTen: Secure Multi-Party Computation Meets Machine Learning

Knott, B., Venkataraman, S., Hannun, A., Sengupta, S., Ibrahim, M., & van der Maaten, L. (2021) · arXiv

Cryptography and Data SecurityPrivacy-Preserving Technologies in Data

Cited by 257

SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning

Wang, Y., Chao, W.-L., Weinberger, K. Q., & van der Maaten, L. (2019) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 245

Certified Data Removal from Machine Learning Models

Guo, C., Goldstein, T., Hannun, A., & van der Maaten, L. (2019) · arXiv

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 160

Revisiting Weakly Supervised Pre-Training of Visual Perception Models

Singh, M., Gustafson, L., Adcock, A., De Freitas Reis, V., Gedik, B., Kosaraju, R. P., Mahajan, D., Girshick, R., Dollar, P., & Van Der Maaten, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 99

CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning

Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C. L., & Girshick, R. (2016) · arXiv

Multimodal Machine Learning ApplicationsVisual Attention and Saliency Detection

Cited by 40

PHYRE: A New Benchmark for Physical Reasoning

Bakhtin, A., van der Maaten, L., Johnson, J., Gustafson, L., & Girshick, R. (2019) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 37

A Systematic Study of Bias Amplification

Hall, M., van der Maaten, L., Gustafson, L., Jones, M., & Adcock, A. (2022) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 34