Varun Raj Kompella

Cognitive Neuroscience · 2012–2013

A researcher in the Science corpus: 2 papers; trusts 19 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 21 citations into it; #2,725 of 10,852 by trust here, #7,860 by citations
• Artificial Intelligence — 18 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Reinforcement Learning — 11 citations into it
• Cellular and Molecular Neuroscience — 10 citations into it; #1,582 of 2,998 by trust here, #2,876 by citations
• Computer Vision and Pattern Recognition — 10 citations into it
• Analytical Chemistry — 6 citations into it

Whom Varun Raj Kompella trusts overall

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

1. J. Urgen Schmidhuber
2. Geoffrey E. Hinton
3. Erkki Oja
4. Laurenz Wiskott
5. Terence D. Sanger
6. H. Barlow
7. Peter Földiák
8. Robert M. Gray
9. Peter S. Dayan
10. Richard S. Sutton

Who trusts Varun Raj Kompella overall

1. J. Urgen Schmidhuber
2. David Pfau
3. Dilip Ravi Kiran
4. Wendelin Böhmer
5. Stig Petersen
6. Yoshua Bengio
7. Ashish Agarwal
8. J. Krichmar
9. Sergey Levine
10. Mathew Thomas

Papers from Varun Raj Kompella's perspective

Ordered by how strongly the researchers Varun Raj Kompella 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Reinforcement Learning: An Introduction
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• An intrinsic value system for developing multiple invariant representations with incremental slowness learning (2013) — 35 citations
• Incremental Slow Feature Analysis: Adaptive Low-Complexity Slow Feature Updating from High-Dimensional Input Streams (2012) — 60 citations