Devansh Arpit

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 8 papers; trusts 242 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 172 citations into it; #10,499 of 35,345 by trust here, #3,035 by citations
• Optimization & Training — 104 citations into it; #1,079 of 2,601 by trust here, #642 by citations
• Computer Vision and Pattern Recognition — 68 citations into it
• Scaling & Learning Theory — 56 citations into it; #386 of 428 by trust here, #215 by citations
• Model Architectures — 47 citations into it
• Statistics and Probability — 24 citations into it
• Computational Mechanics — 18 citations into it
• Representation & Self-Supervised Learning — 17 citations into it

Whom Devansh Arpit trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Yann LeCun
6. Ian J. Goodfellow
7. Diederik P. Kingma
8. Ilya Sutskever
9. Karen Simonyan
10. Christian Szegedy

Who trusts Devansh Arpit overall

1. Stanislaw Jastrzebski
2. Yoshua Bengio
3. Sitao Xiang
4. Zhi-Qin John Xu
5. Lei Huang
6. Lu Jiang
7. Sergey Ioffe
8. Leslie N. Smith
9. Andrew Brock
10. Kaiming He

Papers from Devansh Arpit's perspective

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

Their papers

• A Closer Look at Memorization in Deep Networks (2017) — 2,230 citations
• Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks (2016) — 131 citations
• Residual Connections Encourage Iterative Inference (2017) — 182 citations
• On the Spectral Bias of Neural Networks (2018) — 2,472 citations
• Fraternal Dropout (2017) — 54 citations
• The Break-Even Point on Optimization Trajectories of Deep Neural Networks (2020) — 203 citations
• Three Factors Influencing Minima in SGD (2017) — 546 citations
• A Walk with SGD (2018) — 145 citations