Senthil Purushwalkam

Computer Vision and Pattern Recognition · 2014–2023

A researcher in the Science corpus: 7 papers; trusts 1,368 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 189 citations into it; #4,686 of 21,575 by trust here, #6,671 by citations
• Artificial Intelligence — 138 citations into it; #14,569 of 35,345 by trust here, #7,531 by citations
• Representation & Self-Supervised Learning — 90 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Generative Models — 21 citations into it
• Model Architectures — 16 citations into it
• Optimization & Training — 14 citations into it
• Meta & Continual Learning — 12 citations into it

Whom Senthil Purushwalkam trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Diederik P. Kingma
6. Yann LeCun
7. Yoshua Bengio
8. Ilya Sutskever
9. Andrew Zisserman
10. Karen Simonyan

Who trusts Senthil Purushwalkam overall

1. Christoph Feichtenhofer
2. Joshua Robinson
3. Matthew Wallingford
4. Jie Gui
5. Haoqi Fan
6. Yixiong Zou
7. Liang-Chieh Chen
8. Ricardo F. Alvear-Sandoval
9. Balaji Lakshminarayanan
10. Ching-Yao Chuang

Papers from Senthil Purushwalkam's perspective

Ordered by how strongly the researchers Senthil Purushwalkam trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases (2020) — 238 citations
• Task-Driven Modular Networks for Zero-Shot Compositional Learning (2019) — 227 citations
• Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles (2016) — 199 citations
• Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks (2015) — 343 citations
• Combining the Best of Graphical Models and ConvNets for Semantic Segmentation (2014) — 16 citations
• Diffusion Model Alignment Using Direct Preference Optimization (2023) — 819 citations
• The Challenges of Continuous Self-Supervised Learning (2022) — 59 citations