Theofanis Karaletsos

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 9 papers; trusts 548 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 159 citations into it; #5,014 of 35,345 by trust here, #6,796 by citations
• Probabilistic & Bayesian Methods — 108 citations into it; #396 of 1,525 by trust here, #409 by citations
• Computer Vision and Pattern Recognition — 74 citations into it; #4,797 of 21,575 by trust here, #5,711 by citations
• Representation & Self-Supervised Learning — 36 citations into it; #2,305 of 3,145 by trust here, #2,094 by citations
• Generative Models — 32 citations into it
• Statistics and Probability — 20 citations into it; #6,453 of 12,932 by trust here, #7,914 by citations
• Reinforcement Learning — 19 citations into it
• Statistical and Nonlinear Physics — 18 citations into it; #9,289 of 10,127 by trust here, #8,564 by citations

Whom Theofanis Karaletsos trusts overall

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

1. Diederik P. Kingma
2. Radford M. Neal
3. Geoffrey E. Hinton
4. Max Welling
5. Yoshua Bengio
6. Alex Krizhevsky
7. Yann LeCun
8. Michalis K. Titsias
9. Michael I. Jordan
10. Kaiming He

Who trusts Theofanis Karaletsos overall

1. Sebastian Nowozin
2. Michalis K. Titsias
3. Shakir Mohamed
4. Yunus Saatci
5. Francesco Locatello
6. Balaji Lakshminarayanan
7. Dustin Tran
8. Andrew Gordon Wilson
9. Ferenc Huszár
10. Christian Henning

Papers from Theofanis Karaletsos's perspective

Ordered by how strongly the researchers Theofanis Karaletsos 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

• Bayesian representation learning with oracle constraints (2015) — 94 citations
• Pyro: Deep Universal Probabilistic Programming (2018) — 1,301 citations
• Adversarial Message Passing For Graphical Models (2016) — 29 citations
• Generalized Hidden Parameter MDPs Transferable Model-based RL in a Handful of Trials (2020) — 40 citations
• Probabilistic Meta-Representations Of Neural Networks (2018) — 29 citations
• Likelihood-free inference with emulator networks (2018) — 155 citations
• Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights (2020) — 29 citations
• Variational Auto-Regressive Gaussian Processes for Continual Learning (2020) — 33 citations
• Disentangling Nonlinear Perceptual Embeddings With Multi-Query Triplet Networks (2016) — 8 citations