Neel Kant

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 163 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 72 citations into it; #29,995 of 35,345 by trust here, #25,040 by citations
• Natural Language Processing — 30 citations into it; #4,883 of 5,688 by trust here, #4,032 by citations
• Computer Vision and Pattern Recognition — 26 citations into it
• Reinforcement Learning — 17 citations into it
• Safety, Robustness & Interpretability — 13 citations into it
• Model Architectures — 11 citations into it
• Signal Processing — 7 citations into it
• Optimization & Training — 5 citations into it

Whom Neel Kant trusts overall

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

1. Alex Graves
2. Ilya Sutskever
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Dzmitry Bahdanau
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Richard Socher
9. Oriol Vinyals
10. Yann LeCun

Who trusts Neel Kant overall

1. Sergey I. Nikolenko
2. Kārlis Freivalds
3. Miltiadis Allamanis

Papers from Neel Kant's perspective

Ordered by how strongly the researchers Neel Kant 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. 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Adversarial Policies: Attacking Deep Reinforcement Learning (2019) — 437 citations
• End-to-End Training of Neural Retrievers for Open-Domain Question Answering (2021) — 110 citations
• Recent Advances in Neural Program Synthesis (2018) — 39 citations