Ashish Bora

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 194 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 42 citations into it; #8,593 of 21,575 by trust here, #9,187 by citations
• Generative Models — 24 citations into it; #805 of 1,956 by trust here, #878 by citations
• Computational Mechanics — 22 citations into it; #3,376 of 10,001 by trust here, #2,447 by citations
• Artificial Intelligence — 16 citations into it
• Optimization & Training — 10 citations into it
• Applied Mathematics — 5 citations into it
• Computer Graphics and Computer-Aided Design — 5 citations into it

Whom Ashish Bora trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Yann LeCun
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Alec Radford
7. Kaiming He
8. Christian Szegedy
9. Individualized Treat
10. Gitta Kutyniok

Who trusts Ashish Bora overall

1. Ajil Jalal
2. Giannis Daras
3. A. Dimakis
4. Greg Ongie
5. Reinhard Heckel
6. Varun A. Kelkar
7. Viraj Shah
8. Shady Abu Hussein
9. Aviad Aberdam
10. Lucy Chai

Papers from Ashish Bora's perspective

Ordered by how strongly the researchers Ashish Bora 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. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Compressed Sensing using Generative Models (2017) — 934 citations
• AmbientGAN: Generative models from lossy measurements (2018) — 223 citations