Soumya Ghosh

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 95 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 92 citations into it; #13,769 of 35,345 by trust here, #9,275 by citations
• Probabilistic & Bayesian Methods — 55 citations into it; #999 of 1,525 by trust here, #531 by citations
• Optimization & Training — 37 citations into it
• Computer Vision and Pattern Recognition — 28 citations into it
• Statistics and Probability — 18 citations into it; #10,868 of 12,932 by trust here, #2,693 by citations
• Efficiency & Compression — 17 citations into it
• Reinforcement Learning — 9 citations into it
• Model Architectures — 8 citations into it

Whom Soumya Ghosh trusts overall

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

1. Diederik P. Kingma
2. Max Welling
3. Radford M. Neal
4. Geoffrey E. Hinton
5. Ronald J. Williams
6. José Miguel Hernández-Lobato
7. Alex Krizhevsky
8. Christos Louizos
9. Tom Minka
10. Yann LeCun

Who trusts Soumya Ghosh overall

1. Daniel Hsu
2. Danijar Hafner
3. Moloud Abdar
4. Tameem Adel
5. Hippolyt Ritter
6. Antonio Loquercio
7. Anqi Wu
8. Qinbin Li
9. Jiayu Yao
10. Hongyi Wang

Papers from Soumya Ghosh's perspective

Ordered by how strongly the researchers Soumya Ghosh 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. 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

• Model Selection in Bayesian Neural Networks via Horseshoe Priors (2017) — 133 citations
• Bayesian Nonparametric Federated Learning of Neural Networks (2019) — 912 citations
• Assumed Density Filtering Methods for Learning Bayesian Neural Networks (2016) — 56 citations
• Statistical Model Aggregation via Parameter Matching (2019) — 38 citations
• Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors (2018) — 85 citations