Arjun K. Bansal

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 4 papers; trusts 38 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it; #9,873 of 21,575 by trust here, #8,021 by citations
• Reinforcement Learning — 22 citations into it
• Representation & Self-Supervised Learning — 14 citations into it; #2,252 of 3,145 by trust here, #1,527 by citations
• Probabilistic & Bayesian Methods — 5 citations into it
• Signal Processing — 5 citations into it

Whom Arjun K. Bansal trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Yann LeCun
5. Diederik P. Kingma
6. Kaiming He
7. Ilya Sutskever
8. J. Urgen Schmidhuber
9. Ruslan Salakhutdinov
10. Pascal Vincent

Who trusts Arjun K. Bansal overall

1. Hsin-Ying Lee
2. Francesco Locatello
3. Divya Saxena

Papers from Arjun K. Bansal's perspective

Ordered by how strongly the researchers Arjun K. 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Going deeper with convolutions
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Discovering Hidden Factors of Variation in Deep Networks (2014) — 199 citations
• Deep Semantic Protein Representation for Annotation, Discovery, and Engineering (2018) — 21 citations
• Hierarchical Policy Learning is Sensitive to Goal Space Design (2019) — 15 citations
• Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks (2017) — 278 citations