Sarath Chandar

Artificial Intelligence · 2013–2025

A researcher in the Science corpus: 11 papers; trusts 727 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 431 citations into it; #19,393 of 35,345 by trust here, #8,999 by citations
• Computer Vision and Pattern Recognition — 260 citations into it; #12,167 of 21,575 by trust here, #6,542 by citations
• Natural Language Processing — 169 citations into it; #1,647 of 5,688 by trust here, #3,107 by citations
• Representation & Self-Supervised Learning — 105 citations into it
• Meta & Continual Learning — 69 citations into it; #591 of 1,041 by trust here, #421 by citations
• Reinforcement Learning — 65 citations into it
• Optimization & Training — 54 citations into it
• Evaluation & Benchmarks — 44 citations into it

Whom Sarath Chandar trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. Chih-Jen Lin
9. Marc'Aurelio Ranzato
10. Alex Graves

Who trusts Sarath Chandar overall

1. Karl Moritz Hermann
2. Phil Blunsom
3. Tomás Kociský
4. Andreas Ruckl'e
5. Jia Liu
6. Holger Schwenk
7. Steffen Eger
8. Maoguo Gong
9. Maxime Peyrard
10. Kai Qin

Papers from Sarath Chandar's perspective

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

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

Their papers

• An Empirical Investigation of the Role of Pre-training in Lifelong Learning (2021) — 180 citations
• A Survey of Data Augmentation Approaches for NLP (2021) — 1,000 citations
• PatchUp: A Regularization Technique for Convolutional Neural Networks (2020) — 39 citations
• Toward Training Recurrent Neural Networks for Lifelong Learning (2019) — 101 citations
• Continuous Coordination As a Realistic Scenario for Lifelong Learning (2021) — 52 citations
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning (2025) — 527 citations
• TAG: Task-based Accumulated Gradients for Lifelong learning (2021) — 8 citations
• Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers (2021) — 12 citations
• The Octopus Approach to the Alexa Competition : A Deep Ensemble-based Socialbot (2017)
• Clustering is Efficient for Approximate Maximum Inner Product Search (2015) — 47 citations
• Multilingual Deep Learning (2013)