Florent Altch'e
Computer Vision and Pattern Recognition · 2017–2025
A researcher in the Science corpus: 9 papers; trusts 310 researchers and is trusted by 102 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 204 citations into it; #3,315 of 21,575 by trust here, #1,857 by citations
- Artificial Intelligence — 203 citations into it; #24,596 of 35,345 by trust here, #2,368 by citations
- Representation & Self-Supervised Learning — 108 citations into it; #3,009 of 3,145 by trust here, #324 by citations
- Reinforcement Learning — 57 citations into it
- Optimization & Training — 34 citations into it
- Model Architectures — 30 citations into it
- Cognitive Neuroscience — 15 citations into it
- Control and Systems Engineering — 15 citations into it
Whom Florent Altch'e trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Kaiming He
- Alex Krizhevsky
- Diederik P. Kingma
- Yoshua Bengio
- Sepp Hochreiter
- J. Urgen Schmidhuber
- Yann LeCun
- Ilya Sutskever
- Ross B. Girshick
Who trusts Florent Altch'e overall
- Edouard Leurent
- Kaouther Messaoud
- Wei Zhan
- Yicheng Liu
- Sajjad Mozaffari
- Yanjun Huang
- Xinlei Chen
- Szilárd Aradi
- Geoffrey E. Hinton
- Kaiming He
Papers from Florent Altch'e's perspective
Ordered by how strongly the researchers Florent Altch'e trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (2020) — 8,797 citations
- BYOL works even without batch statistics (2020) — 131 citations
- Broaden Your Views for Self-Supervised Video Learning (2021) — 146 citations
- World Discovery Models (2019) — 28 citations
- The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles? (2017) — 421 citations
- Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning (2020) — 158 citations
- Self-conditioned Embedding Diffusion for Text Generation (2022) — 134 citations
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
- An LSTM network for highway trajectory prediction (2017) — 609 citations