Mohammad Emtiyaz Khan

Artificial Intelligence · 2010–2020

A researcher in the Science corpus: 12 papers; trusts 181 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 171 citations into it; #6,188 of 35,345 by trust here, #6,266 by citations
• Probabilistic & Bayesian Methods — 119 citations into it; #927 of 1,525 by trust here, #531 by citations
• Optimization & Training — 65 citations into it; #2,046 of 2,601 by trust here, #1,468 by citations
• Statistics and Probability — 59 citations into it; #7,767 of 12,932 by trust here, #8,740 by citations
• Computer Vision and Pattern Recognition — 49 citations into it
• Representation & Self-Supervised Learning — 30 citations into it
• Management Science and Operations Research — 29 citations into it
• Computational Theory and Mathematics — 15 citations into it

Whom Mohammad Emtiyaz Khan trusts overall

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

1. Radford M. Neal
2. Diederik P. Kingma
3. Michael I. Jordan
4. Tom Minka
5. Geoffrey E. Hinton
6. David M. Blei
7. Ronald J. Williams
8. Zoubin Ghahramani
9. Yoshua Bengio
10. Tommi S. Jaakkola

Who trusts Mohammad Emtiyaz Khan overall

1. Laurence Aitchison
2. Sayna Ebrahimi
3. Frederik Kunstner
4. Alexander Immer
5. Aaron Mishkin
6. Laurent Valentin Jospin
7. Hanna Tseran
8. Siddharth Swaroop
9. Pingbo Pan
10. Shakir Mohamed

Papers from Mohammad Emtiyaz Khan's perspective

Ordered by how strongly the researchers Mohammad Emtiyaz Khan 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam (2018) — 296 citations
• Practical Deep Learning with Bayesian Principles (2019) — 280 citations
• Continual Deep Learning by Functional Regularisation of Memorable Past (2020) — 174 citations
• Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models (2018) — 81 citations
• Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions (2015) — 58 citations
• AI for social good: unlocking the opportunity for positive impact (2020) — 296 citations
• Variational learning for latent Gaussian model of discrete data (2012) — 16 citations
• Approximate Inference Turns Deep Networks into Gaussian Processes (2019) — 141 citations
• SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient (2018) — 67 citations
• Kullback-Leibler Proximal Variational Inference (2015) — 52 citations
• Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models (2011) — 34 citations
• Variational bounds for mixed-data factor analysis (2010) — 66 citations