Tonmoy Saikia

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 747 researchers and is trusted by 212 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 172 citations into it; #2,618 of 21,575 by trust here, #2,966 by citations
• Artificial Intelligence — 113 citations into it; #19,828 of 35,345 by trust here, #15,542 by citations
• Neural Architecture Search & AutoML — 43 citations into it; #270 of 283 by trust here, #164 by citations
• Optimization & Training — 37 citations into it
• Model Architectures — 27 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Media Technology — 15 citations into it

Whom Tonmoy Saikia trusts overall

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

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

Who trusts Tonmoy Saikia overall

1. Sergey I. Nikolenko
2. Carl Doersch
3. Arber Zela
4. Wei-Hong Li
5. Junhwa Hur
6. Florian Wenzel
7. Thomas Brox
8. Lu Liu
9. Pengzhen Ren
10. Haofei Xu

Papers from Tonmoy Saikia's perspective

Ordered by how strongly the researchers Tonmoy Saikia 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks (2016) — 3,400 citations
• Optimized Generic Feature Learning for Few-shot Classification across Domains (2020) — 51 citations
• Understanding and Robustifying Differentiable Architecture Search (2019) — 415 citations
• Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation (2018) — 224 citations
• AutoDispNet: Improving Disparity Estimation With AutoML (2019) — 85 citations