Siddharth Gopal

Artificial Intelligence · 2012–2016

A researcher in the Science corpus: 4 papers; trusts 85 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it
• Optimization & Training — 33 citations into it
• Computational Mechanics — 15 citations into it
• Information Systems — 15 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Statistics and Probability — 10 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Siddharth Gopal trusts overall

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

1. L. Eon Bottou
2. Radford M. Neal
3. Jakub Konecný
4. Matthew D. Zeiler
5. Yiming Yang
6. Paul Tseng
7. Susan T. Dumais
8. Tong Zhang
9. Lijuan Cai
10. Thomas Hofmann

Who trusts Siddharth Gopal overall

1. Tyler B. Johnson
2. Haw-Shiuan Chang
3. Carlos Guestrin
4. Hao Peng
5. Eric Learned-Miller
6. Yiming Yang
7. Andrew McCallum
8. Bhavana Dalvi
9. Xin Wang
10. Jianxin Li

Papers from Siddharth Gopal's perspective

Ordered by how strongly the researchers Siddharth Gopal 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Adaptive Sampling for SGD by Exploiting Side Information (2016) — 67 citations
• Hierarchical Bayesian Inference and Recursive Regularization for Large-Scale Classification (2015) — 32 citations
• Recursive regularization for large-scale classification with hierarchical and graphical dependencies (2013) — 158 citations
• Bayesian models for Large-scale Hierarchical Classification (2012) — 50 citations