Paroma Varma

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 8 papers; trusts 316 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 115 citations into it; #16,319 of 35,345 by trust here, #11,192 by citations
• Computer Vision and Pattern Recognition — 65 citations into it; #18,541 of 21,575 by trust here, #18,266 by citations
• Natural Language Processing — 64 citations into it; #3,806 of 5,688 by trust here, #2,955 by citations
• Evaluation & Benchmarks — 33 citations into it
• Representation & Self-Supervised Learning — 28 citations into it; #2,228 of 3,145 by trust here, #1,658 by citations
• Information Retrieval & Search — 17 citations into it
• Molecular Biology — 14 citations into it
• Model Architectures — 11 citations into it

Whom Paroma Varma trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Alexander J. Ratner
4. Yoshua Bengio
5. Ilya Sutskever
6. Karen Simonyan
7. Kaiming He
8. Diederik P. Kingma
9. Yann LeCun
10. Christian Szegedy

Who trusts Paroma Varma overall

1. Alexander J. Ratner
2. Braden Hancock
3. Benedikt Boecking
4. Toki Migimatsu
5. Christopher R'e
6. Andrei Paleyes
7. Xi Ye
8. Jieyu Zhang
9. Arghya Pal
10. Willie Neiswanger

Papers from Paroma Varma's perspective

Ordered by how strongly the researchers Paroma Varma 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Training Classifiers with Natural Language Explanations (2018) — 160 citations
• Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data (2016) — 26 citations
• Parameterizing neural power spectra into periodic and aperiodic components (2020) — 1,825 citations
• Snuba: Automating Weak Supervision to Label Training Data (2018) — 161 citations
• Parameterizing neural power spectra (2018)
• Weakly supervised classification of rare aortic valve malformations using unlabeled cardiac MRI sequences (2018) — 20 citations
• Inferring Generative Model Structure with Static Analysis (2017) — 61 citations
• Socratic Learning (2016)