Abhishek Sinha

Computer Vision and Pattern Recognition · with Ermon · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 619 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 154 citations into it
• Artificial Intelligence — 118 citations into it
• Generative Models — 80 citations into it
• Representation & Self-Supervised Learning — 60 citations into it
• Probabilistic & Bayesian Methods — 21 citations into it
• Optimization & Training — 17 citations into it
• Signal Processing — 15 citations into it
• Statistical and Nonlinear Physics — 12 citations into it

Whom Abhishek Sinha trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Ilya Sutskever
7. Ross B. Girshick
8. Yann LeCun
9. Karen Simonyan
10. Max Welling

Who trusts Abhishek Sinha overall

1. Konpat Preechakul
2. Alexandros Graikos
3. Robin Rombach
4. Chenlin Meng
5. Yen-Chi Cheng
6. Nicholas Carlini
7. Nattanat Chatthee
8. Esmeralda S. Whitammer
9. Andreas Blattmann
10. Hsin-Ying Lee

Papers from Abhishek Sinha's perspective

Ordered by how strongly the researchers Abhishek Sinha 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation (2021) — 146 citations
• Charting the Right Manifold: Manifold Mixup for Few-shot Learning (2019) — 386 citations
• Negative Data Augmentation (2021) — 83 citations