Remi Piche-Taillefer

Computer Vision and Pattern Recognition · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 170 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 58 citations into it; #7,065 of 21,575 by trust here, #7,421 by citations
• Generative Models — 50 citations into it; #680 of 1,956 by trust here, #843 by citations
• Artificial Intelligence — 36 citations into it
• Probabilistic & Bayesian Methods — 16 citations into it
• Statistical and Nonlinear Physics — 14 citations into it; #6,937 of 10,127 by trust here, #3,010 by citations
• Optimization & Training — 8 citations into it
• Numerical Methods & Scientific Computing — 6 citations into it
• Biophysics — 5 citations into it

Whom Remi Piche-Taillefer trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Yang Song
7. Aapo Hyvärinen
8. Pascal Vincent
9. Kaiming He
10. Max Welling

Who trusts Remi Piche-Taillefer overall

1. Valentin De Bortoli
2. Chenlin Meng
3. Arash Vahdat
4. Qinsheng Zhang
5. Fan Bao
6. Dongjun Kim
7. Jiaming Song
8. Zhisheng Xiao
9. Vikram S. Voleti
10. Karsten Kreis

Papers from Remi Piche-Taillefer's perspective

Ordered by how strongly the researchers Remi Piche-Taillefer 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Adversarial score matching and improved sampling for image generation (2020) — 138 citations
• Gotta Go Fast When Generating Data with Score-Based Models (2021) — 265 citations