Varun Manjunatha

Artificial Intelligence · with Iyyer · 2015–2021

A researcher in the Science corpus: 3 papers; trusts 253 researchers and is trusted by 57 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it; #4,365 of 35,345 by trust here, #5,749 by citations
• Natural Language Processing — 47 citations into it; #1,114 of 5,688 by trust here, #1,558 by citations
• Computer Vision and Pattern Recognition — 32 citations into it; #17,152 of 21,575 by trust here, #15,529 by citations
• Representation & Self-Supervised Learning — 16 citations into it
• Safety, Robustness & Interpretability — 12 citations into it
• Model Architectures — 10 citations into it
• Multimodal Learning — 10 citations into it; #933 of 1,211 by trust here, #1,033 by citations
• Evaluation & Benchmarks — 7 citations into it

Whom Varun Manjunatha trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Sepp Hochreiter
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Ilya Sutskever
8. Oriol Vinyals
9. Yann LeCun
10. J. Urgen Schmidhuber

Who trusts Varun Manjunatha overall

1. Yoav Goldberg
2. Eliyahu Kiperwasser
3. Tao Lei
4. Diego Marcheggiani
5. John Wieting
6. Aman Chadha
7. Taku Kudo
8. Daniel Matthew Cer
9. Dongyeop Kang
10. Felix Hill

Papers from Varun Manjunatha's perspective

Ordered by how strongly the researchers Varun Manjunatha 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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Unordered Composition Rivals Syntactic Methods for Text Classification (2015) — 842 citations
• Explicit Bias Discovery in Visual Question Answering Models (2018) — 99 citations
• TABBIE: Pretrained Representations of Tabular Data (2021) — 228 citations