Aditya Golatkar

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 5 papers; trusts 246 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 116 citations into it; #6,774 of 35,345 by trust here, #9,369 by citations
• Computer Vision and Pattern Recognition — 65 citations into it
• Optimization & Training — 45 citations into it
• Safety, Robustness & Interpretability — 28 citations into it
• Privacy & Security — 20 citations into it
• Scaling & Learning Theory — 18 citations into it
• Computational Mechanics — 10 citations into it
• Model Architectures — 10 citations into it

Whom Aditya Golatkar trusts overall

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

1. Alex Krizhevsky
2. James Martens
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Alessandro Achille
6. Kaiming He
7. Yoshua Bengio
8. Yann LeCun
9. Cynthia Dwork
10. Ilya Sutskever

Who trusts Aditya Golatkar overall

1. Alessandro Achille
2. Meghdad Kurmanji
3. Ayush Kumar Tarun
4. Yinjun Wu
5. Jonathan Brophy
6. Vikram S Chundawat
7. Avinash Ravichandran
8. Bo Liu
9. Stefano Soatto
10. Thomas Baumhauer

Papers from Aditya Golatkar's perspective

Ordered by how strongly the researchers Aditya Golatkar 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. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Mixed-Privacy Forgetting in Deep Networks (2020) — 233 citations
• Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks (2019) — 845 citations
• Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence (2019) — 110 citations
• Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations (2020) — 257 citations
• LQF: Linear Quadratic Fine-Tuning (2020) — 33 citations