Gaëtan Hadjeres

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 208 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it
• Computer Vision and Pattern Recognition — 45 citations into it; #7,170 of 21,575 by trust here, #11,721 by citations
• Signal Processing — 31 citations into it; #1,885 of 7,572 by trust here, #2,911 by citations
• Model Architectures — 28 citations into it
• Generative Models — 21 citations into it; #591 of 1,956 by trust here, #1,044 by citations
• Probabilistic & Bayesian Methods — 15 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Cognitive Neuroscience — 11 citations into it; #2,309 of 10,852 by trust here, #5,162 by citations

Whom Gaëtan Hadjeres trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Yann LeCun
4. Sepp Hochreiter
5. J. Urgen Schmidhuber
6. Alex Krizhevsky
7. Diederik P. Kingma
8. Tomas Mikolov
9. Alex Graves
10. Kaiming He

Who trusts Gaëtan Hadjeres overall

1. Cheng-Zhi Anna Huang
2. Hao-Wen Dong
3. Li-Chia Yang
4. Yi-Hsuan Yang
5. Yu-Siang Huang
6. Jean-Pierre Briot
7. Wen-Yi Hsiao
8. Stefan Lattner
9. Noam Mor
10. François Pachet

Papers from Gaëtan Hadjeres's perspective

Ordered by how strongly the researchers Gaëtan Hadjeres trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
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

• DeepBach: a Steerable Model for Bach Chorales Generation (2016) — 494 citations
• Style Imitation and Chord Invention in Polyphonic Music with Exponential Families (2016) — 18 citations
• Deep Learning Techniques for Music Generation - A Survey (2017) — 330 citations