Apratim Bhattacharyya

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 399 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 71 citations into it; #15,699 of 21,575 by trust here, #11,721 by citations
• Artificial Intelligence — 69 citations into it; #25,606 of 35,345 by trust here, #19,873 by citations
• Safety, Robustness & Interpretability — 29 citations into it
• Generative Models — 26 citations into it
• Model Architectures — 10 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it
• Information Systems — 9 citations into it
• Optimization & Training — 9 citations into it

Whom Apratim Bhattacharyya trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yann LeCun
4. Yoshua Bengio
5. Alex Krizhevsky
6. Kaiming He
7. Ilya Sutskever
8. Ruslan Salakhutdinov
9. Karen Simonyan
10. Jimmy Ba

Who trusts Apratim Bhattacharyya overall

1. Michihiro Yasunaga
2. Lixu Wang
3. Nicholas Watters
4. Sergiu Oprea
5. Percy Liang
6. Maria Rigaki
7. Jessica B. Hamrick
8. Shichao Xu
9. Daniel Zoran

Papers from Apratim Bhattacharyya's perspective

Ordered by how strongly the researchers Apratim Bhattacharyya 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. 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

• Long-Term Image Boundary Extrapolation (2016) — 12 citations
• Long-Term Image Boundary Prediction (2016) — 15 citations
• Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods (2018) — 47 citations
• Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning (2019) — 298 citations
• SampleFix: Learning to Correct Programs by Sampling Diverse Fixes (2019) — 14 citations