Kshitij Bansal

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 5 papers; trusts 26 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it; #16,145 of 35,345 by trust here, #19,439 by citations
• Natural Language Processing — 24 citations into it; #3,283 of 5,688 by trust here, #3,977 by citations
• Large Language Models — 18 citations into it
• Program Analysis & Verification — 14 citations into it
• Reinforcement Learning — 13 citations into it
• Computational Theory and Mathematics — 12 citations into it; #9,602 of 12,182 by trust here, #6,798 by citations
• Evaluation & Benchmarks — 9 citations into it
• Model Architectures — 6 citations into it

Whom Kshitij Bansal trusts overall

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

1. Diederik P. Kingma
2. Kaiyu Yang
3. Jimmy Ba
4. Jia Deng
5. Christian Szegedy
6. Geoffrey E. Hinton
7. Ilya Sutskever
8. Daniel Huang
9. Yoshua Bengio
10. Alex Krizhevsky

Who trusts Kshitij Bansal overall

1. Markus Norman Rabe
2. Yuhuai Wu
3. Jesse Michael Han
4. Stanislas Polu
5. Mingzhe Wang
6. Wenda Li
7. Jia Deng
8. Kaiyu Yang
9. Albert Qiaochu Jiang
10. Maxwell Crouse

Papers from Kshitij Bansal's perspective

Ordered by how strongly the researchers Kshitij Bansal 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• HOList: An Environment for Machine Learning of Higher-Order Theorem Proving (extended version) (2019) — 59 citations
• Learning to Reason in Large Theories without Imitation (2019) — 45 citations
• Mathematical Reasoning via Self-supervised Skip-tree Training (2020) — 65 citations
• Language Modeling for Formal Mathematics (2020)
• HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving (2019) — 145 citations