Patrick Forr'e

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 6 papers; trusts 215 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 112 citations into it
• Computer Vision and Pattern Recognition — 78 citations into it
• Generative Models — 57 citations into it; #1,840 of 1,956 by trust here, #1,315 by citations
• Probabilistic & Bayesian Methods — 42 citations into it
• Statistical and Nonlinear Physics — 24 citations into it
• Causal Inference & Discovery — 21 citations into it
• Signal Processing — 20 citations into it
• Representation & Self-Supervised Learning — 17 citations into it

Whom Patrick Forr'e trusts overall

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

1. Thomas S. Richardson
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Peter Spirtes
5. Yoshua Bengio
6. Alex Krizhevsky
7. Judea Pearl
8. Joris M. Mooij
9. Radford M. Neal
10. Danilo Jimenez Rezende

Who trusts Patrick Forr'e overall

1. Sara Magliacane
2. Joris M. Mooij
3. Stephan Bongers
4. Tom Claassen
5. Thijs van Ommen
6. Jan Peters
7. Matthew James Vowels
8. Philip Versteeg
9. Eliya Nachmani

Papers from Patrick Forr'e's perspective

Ordered by how strongly the researchers Patrick Forr'e 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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. GENERATIVE ADVERSARIAL NETS

Their papers

• Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions (2021) — 710 citations
• Explorations in Homeomorphic Variational Auto-Encoding (2018) — 131 citations
• Learning Robust Representations via Multi-View Information Bottleneck (2020) — 345 citations
• Foundations of structural causal models with cycles and latent variables (2016) — 235 citations
• Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models (2021) — 79 citations
• Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders (2018) — 68 citations