A. P. Sarath Chandar

Artificial Intelligence · 2014–2021

A researcher in the Science corpus: 14 papers; trusts 775 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 442 citations into it; #7,411 of 35,345 by trust here, #3,009 by citations
• Natural Language Processing — 259 citations into it; #2,489 of 5,688 by trust here, #799 by citations
• Computer Vision and Pattern Recognition — 180 citations into it; #10,046 of 21,575 by trust here, #3,848 by citations
• Reinforcement Learning — 93 citations into it
• Model Architectures — 62 citations into it; #1,187 of 2,278 by trust here, #1,246 by citations
• Representation & Self-Supervised Learning — 57 citations into it
• Evaluation & Benchmarks — 53 citations into it
• Safety, Robustness & Interpretability — 42 citations into it

Whom A. P. Sarath Chandar trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alex Graves
6. Alex Krizhevsky
7. Jason Aaron Edward Weston
8. Yann LeCun
9. Jimmy Ba
10. Antoine Bordes

Who trusts A. P. Sarath Chandar overall

1. Kyunghyun Cho
2. Felix Stahlberg
3. Nikolaos Pappas
4. Yoshua Bengio
5. Lukasz Kaiser
6. Caglar Gulcehre
7. Sebastian Ruder
8. Jiatao Gu
9. Ryan Kiros
10. Felix Hill

Papers from A. P. Sarath Chandar's perspective

Ordered by how strongly the researchers A. P. Sarath Chandar 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• GuessWhat?! Visual Object Discovery through Multi-modal Dialogue (2016) — 444 citations
• Hierarchical Memory Networks (2016) — 88 citations
• An Autoencoder Approach to Learning Bilingual Word Representations (2014) — 348 citations
• Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes (2016) — 66 citations
• A Deep Reinforcement Learning Chatbot (2017) — 245 citations
• Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph (2018) — 212 citations
• Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus (2016) — 300 citations
• The Hanabi Challenge: A New Frontier for AI Research (2019) — 429 citations
• Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study (2019) — 125 citations
• Dynamic Neural Turing Machine with Continuous and Discrete Addressing Schemes (2018) — 53 citations
• Environments for Lifelong Reinforcement Learning (2018) — 8 citations
• Post-hoc Interpretability for Neural NLP: A Survey (2021) — 321 citations
• IIRC: Incremental Implicitly-Refined Classification (2020) — 32 citations
• On Training Recurrent Neural Networks for Lifelong Learning (2018) — 19 citations