Samarth Sinha

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 8 papers; trusts 938 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 218 citations into it; #19,902 of 35,345 by trust here, #9,760 by citations
• Computer Vision and Pattern Recognition — 177 citations into it; #20,377 of 21,575 by trust here, #8,205 by citations
• Representation & Self-Supervised Learning — 92 citations into it; #2,953 of 3,145 by trust here, #1,811 by citations
• Reinforcement Learning — 77 citations into it
• Evaluation & Benchmarks — 37 citations into it
• Generative Models — 36 citations into it
• Optimization & Training — 28 citations into it
• Radiology, Nuclear Medicine and Imaging — 27 citations into it

Whom Samarth Sinha trusts overall

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

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

Who trusts Samarth Sinha overall

1. Dongjun Kim
2. Sayna Ebrahimi
3. Yan Luo
4. Prateek Munjal
5. Moloud Abdar
6. Jaehong Yoon
7. Youngmin Oh
8. Peter R. Florence
9. Pengzhen Ren
10. Jiarui Xu

Papers from Samarth Sinha's perspective

Ordered by how strongly the researchers Samarth Sinha 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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

• Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders (2018) — 693 citations
• Revisiting Training Strategies and Generalization Performance in Deep Metric Learning (2020) — 191 citations
• Variational Adversarial Active Learning (2019) — 680 citations
• Consistency Regularization for Variational Auto-Encoders (2021) — 80 citations
• Experience Replay with Likelihood-free Importance Weights (2020) — 74 citations
• S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning (2021) — 132 citations
• Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning (2021) — 25 citations
• Generalized Zero-Shot Learning via Aligned Variational Autoencoders (2019) — 24 citations