Manik Dhar

Computer Vision and Pattern Recognition · 2017

A researcher in the Science corpus: 2 papers; trusts 197 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 48 citations into it; #5,657 of 21,575 by trust here, #9,872 by citations
• Generative Models — 47 citations into it; #733 of 1,956 by trust here, #1,271 by citations
• Artificial Intelligence — 38 citations into it; #7,736 of 35,345 by trust here, #14,438 by citations
• Probabilistic & Bayesian Methods — 19 citations into it
• Statistical and Nonlinear Physics — 11 citations into it; #2,590 of 10,127 by trust here, #3,382 by citations
• Optimization & Training — 9 citations into it
• Signal Processing — 9 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Manik Dhar trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Radford M. Neal
6. Tim Salimans
7. Laurent Dinh
8. Ian J. Goodfellow
9. Sergey Ioffe
10. Danilo Jimenez Rezende

Who trusts Manik Dhar overall

1. Ethan Fetaya
2. Lynton Ardizzone
3. Pengchuan Zhang
4. Garrett Wilson
5. Changyou Chen
6. Eitan Richardson
7. Diederik P. Kingma
8. Ivan Kobyzev
9. Jörn-Henrik Jacobsen
10. Augustus Odena

Papers from Manik Dhar's perspective

Ordered by how strongly the researchers Manik Dhar 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models (2017) — 226 citations
• Flow-GAN: Bridging implicit and prescribed learning in generative models (2017) — 24 citations