Alexandre D'efossez
Artificial Intelligence · 2015–2023
A researcher in the Science corpus: 11 papers; trusts 552 researchers and is trusted by 39 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 233 citations into it; #9,922 of 35,345 by trust here, #9,092 by citations
- Signal Processing — 173 citations into it; #905 of 7,572 by trust here, #592 by citations
- Computer Vision and Pattern Recognition — 131 citations into it; #10,838 of 21,575 by trust here, #7,421 by citations
- Speech & Audio — 118 citations into it; #288 of 1,076 by trust here, #289 by citations
- Optimization & Training — 67 citations into it
- Generative Models — 57 citations into it
- Model Architectures — 39 citations into it
- Computational Mechanics — 37 citations into it; #4,650 of 10,001 by trust here, #3,463 by citations
Whom Alexandre D'efossez trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Yoshua Bengio
- Geoffrey E. Hinton
- Francis R. Bach
- Kaiming He
- Alex Krizhevsky
- Jimmy Ba
- L. Eon Bottou
- Ilya Sutskever
- Yann LeCun
Who trusts Alexandre D'efossez overall
- Prateek Jain
- Yossi Adi
- Dongchao Yang
- Sham M. Kakade
- Jade Copet
- Gabriel Synnaeve
- Rongjie Huang
- Fangyu Zou
- Rahul Kidambi
- Jesse Engel
Papers from Alexandre D'efossez's perspective
Ordered by how strongly the researchers Alexandre D'efossez 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
- 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
- Real Time Speech Enhancement in the Waveform Domain (2020) — 636 citations
- High Fidelity Neural Audio Compression (2022) — 1,262 citations
- SING: Symbol-to-Instrument Neural Generator (2018) — 63 citations
- AudioGen: Textually Guided Audio Generation (2022) — 466 citations
- Differentiable Model Compression via Pseudo Quantization Noise (2021) — 64 citations
- A Simple Convergence Proof of Adam and Adagrad (2020) — 246 citations
- AdaBatch: Efficient Gradient Aggregation Rules for Sequential and Parallel Stochastic Gradient Methods (2017) — 17 citations
- Simple and Controllable Music Generation (2023) — 745 citations
- Hybrid Transformers for Music Source Separation (2022) — 298 citations
- Music Source Separation in the Waveform Domain (2019) — 324 citations
- Averaged Least-Mean-Squares: Bias-Variance Trade-offs and Optimal Sampling Distributions (2015) — 88 citations