Gobinda Saha

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 598 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it; #10,483 of 35,345 by trust here, #12,387 by citations
• Computer Vision and Pattern Recognition — 87 citations into it; #7,177 of 21,575 by trust here, #8,612 by citations
• Meta & Continual Learning — 56 citations into it; #345 of 1,041 by trust here, #470 by citations
• Representation & Self-Supervised Learning — 35 citations into it
• Reinforcement Learning — 21 citations into it
• Optimization & Training — 13 citations into it
• Model Architectures — 12 citations into it
• Safety, Robustness & Interpretability — 10 citations into it

Whom Gobinda Saha trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Kaiming He
5. Marc'Aurelio Ranzato
6. Yoshua Bengio
7. Karen Simonyan
8. Diederik P. Kingma
9. Anthony V. Robins
10. David Lopez-Paz

Who trusts Gobinda Saha overall

1. Kaushik Roy
2. Yan-Shuo Liang
3. Yiduo Guo
4. Zhenyi Wang
5. Yabin Wang
6. Gyuhak Kim
7. Xikun Zhang
8. Julio Hurtado
9. Sen Lin
10. Qi Qin

Papers from Gobinda Saha's perspective

Ordered by how strongly the researchers Gobinda Saha trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Gradient Projection Memory for Continual Learning (2021) — 459 citations
• SPACE: Structured Compression and Sharing of Representational Space for Continual Learning (2020) — 26 citations
• Saliency Guided Experience Packing for Replay in Continual Learning (2021) — 30 citations