Yamini Bansal

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 35 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it
• Scaling & Learning Theory — 33 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it
• Optimization & Training — 16 citations into it
• Model Architectures — 9 citations into it
• Computational Mechanics — 8 citations into it
• Statistical and Nonlinear Physics — 8 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Yamini Bansal trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Yann LeCun
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Ilya Sutskever
7. Karen Simonyan
8. Andrew M. Saxe
9. Kaiming He
10. Xavier Glorot

Who trusts Yamini Bansal overall

1. Mario Geiger
2. Stefano Spigler
3. Rana Ali Amjad
4. Fatemehsadat Mireshghallah
5. Adar Elad
6. Alessandro Achille
7. Matthew E. Peters
8. Zhi-Qin John Xu
9. Ivan Chelombiev

Papers from Yamini Bansal's perspective

Ordered by how strongly the researchers Yamini 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• On the information bottleneck theory of deep learning (2018) — 692 citations
• Deep double descent: where bigger models and more data hurt (2019) — 1,162 citations
• Minnorm training: an algorithm for training over-parameterized deep neural networks (2018) — 18 citations