Corentin Tallec
Artificial Intelligence · 2017–2025
A researcher in the Science corpus: 10 papers; trusts 352 researchers and is trusted by 117 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 243 citations into it; #5,222 of 35,345 by trust here, #2,338 by citations
- Computer Vision and Pattern Recognition — 215 citations into it; #3,478 of 21,575 by trust here, #1,904 by citations
- Representation & Self-Supervised Learning — 128 citations into it; #3,013 of 3,145 by trust here, #337 by citations
- Optimization & Training — 53 citations into it; #1,148 of 2,601 by trust here, #2,162 by citations
- Generative Models — 43 citations into it
- Model Architectures — 38 citations into it
- Statistical and Nonlinear Physics — 26 citations into it
- Graph Learning — 25 citations into it
Whom Corentin Tallec trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Graves
- Diederik P. Kingma
- Yoshua Bengio
- Kaiming He
- Alex Krizhevsky
- Ilya Sutskever
- Jimmy Ba
- Yann LeCun
- Tomas Mikolov
Who trusts Corentin Tallec overall
- Luke Metz
- Yoshua Bengio
- Nan Rosemary Ke
- Benjamin Scellier
- Stephen Merity
- Anirudh Goyal
- Ricky T. Q. Chen
- C. Freeman
- Luca Franceschi
- Rami Al-Rfou
Papers from Corentin Tallec's perspective
Ordered by how strongly the researchers Corentin Tallec trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (2020) — 8,797 citations
- Unbiased Online Recurrent Optimization (2017) — 107 citations
- Unbiasing Truncated Backpropagation Through Time (2017) — 88 citations
- BYOL works even without batch statistics (2020) — 131 citations
- Broaden Your Views for Self-Supervised Video Learning (2021) — 146 citations
- Self-conditioned Embedding Diffusion for Text Generation (2022) — 134 citations
- Large-Scale Representation Learning on Graphs via Bootstrapping (2021) — 329 citations
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
- Bootstrapped Representation Learning on Graphs (2021) — 315 citations
- Shaking the foundations: delusions in sequence models for interaction and control (2021) — 75 citations