Suraj Srinivas

Artificial Intelligence · 2015–2023

A researcher in the Science corpus: 7 papers; trusts 696 researchers and is trusted by 95 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 130 citations into it; #2,581 of 35,345 by trust here, #6,064 by citations
• Computer Vision and Pattern Recognition — 90 citations into it; #3,650 of 21,575 by trust here, #6,371 by citations
• Efficiency & Compression — 56 citations into it; #126 of 1,264 by trust here, #573 by citations
• Safety, Robustness & Interpretability — 41 citations into it
• Optimization & Training — 30 citations into it
• Model Architectures — 23 citations into it
• Signal Processing — 16 citations into it
• Computational Mechanics — 8 citations into it

Whom Suraj Srinivas trusts overall

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

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

Who trusts Suraj Srinivas overall

1. Jonathan Frankle
2. Frederick Tung
3. Charles Herrmann
4. Michael Carbin
5. Yu Cheng
6. Yihui He
7. Byeongho Heo
8. Shir Gur
9. Tyler L. Hayes
10. Lin Wang

Papers from Suraj Srinivas's perspective

Ordered by how strongly the researchers Suraj Srinivas 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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Data-free Parameter Pruning for Deep Neural Networks (2015) — 580 citations
• Knowledge Transfer with Jacobian Matching (2018) — 181 citations
• Training Sparse Neural Networks (2016) — 226 citations
• Full-Gradient Representation for Neural Network Visualization (2019) — 366 citations
• Certifying LLM Safety against Adversarial Prompting (2023) — 333 citations
• Data-Efficient Structured Pruning via Submodular Optimization (2022) — 27 citations
• Generalized Dropout (2016) — 49 citations