Olivier Delalleau
Computer Vision and Pattern Recognition · 2003–2016
A researcher in the Science corpus: 10 papers; trusts 256 researchers and is trusted by 35 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 72 citations into it; #17,944 of 21,575 by trust here, #10,677 by citations
- Artificial Intelligence — 55 citations into it; #13,084 of 35,345 by trust here, #10,207 by citations
- Representation & Self-Supervised Learning — 51 citations into it
- Probabilistic & Bayesian Methods — 36 citations into it; #528 of 1,525 by trust here, #376 by citations
- Optimization & Training — 26 citations into it
- Model Architectures — 22 citations into it; #1,112 of 2,278 by trust here, #235 by citations
- Generative Models — 14 citations into it
- Signal Processing — 11 citations into it
Whom Olivier Delalleau trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Ruslan Salakhutdinov
- Yann LeCun
- Marc'Aurelio Ranzato
- Alex Krizhevsky
- Diederik P. Kingma
- Tijmen Tieleman
- Radford M. Neal
- Ilya Sutskever
Who trusts Olivier Delalleau overall
- Yoshua Bengio
- Kyunghyun Cho
- Matus Telgarsky
- Ruslan Salakhutdinov
- Aaron C. Courville
- Hugo Larochelle
- Sergey Levine
- Quoc V. Le
- Yi Yang
- Trevor Darrell
Papers from Olivier Delalleau's perspective
Ordered by how strongly the researchers Olivier Delalleau 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)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- 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
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Learning Eigenfunctions Links Spectral Embedding and Kernel PCA (2004) — 342 citations
- On the Expressive Power of Deep Architectures (2011) — 312 citations
- Theano: A Python framework for fast computation of mathematical expressions (2016) — 2,380 citations
- Theano: Deep Learning on GPUs with Python (2012) — 280 citations
- Shallow vs. Deep Sum-Product Networks (2011) — 349 citations
- Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines (2010) — 122 citations
- Parallel Tempering for Training of Restricted Boltzmann Machines (2010) — 85 citations
- Convex Neural Networks (2005) — 226 citations
- The Curse of Highly Variable Functions for Local Kernel Machines (2005) — 241 citations
- Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering (2003) — 1,157 citations