Jade Copet
Artificial Intelligence · 2021–2024
A researcher in the Science corpus: 13 papers; trusts 178 researchers and is trusted by 45 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 456 citations into it; #9,239 of 35,345 by trust here, #3,362 by citations
- Signal Processing — 261 citations into it; #818 of 7,572 by trust here, #388 by citations
- Speech & Audio — 226 citations into it; #213 of 1,076 by trust here, #150 by citations
- Natural Language Processing — 116 citations into it
- Computer Vision and Pattern Recognition — 105 citations into it; #8,292 of 21,575 by trust here, #5,236 by citations
- Representation & Self-Supervised Learning — 84 citations into it; #2,996 of 3,145 by trust here, #1,638 by citations
- Large Language Models — 80 citations into it
- Generative Models — 76 citations into it; #1,597 of 1,956 by trust here, #807 by citations
Whom Jade Copet trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Aäron van den Oord
- Jimmy Ba
- Alex Krizhevsky
- Oriol Vinyals
- Ilya Sutskever
- Alex Graves
- Jean-Marc Valin
Who trusts Jade Copet overall
- Dongchao Yang
- Rongjie Huang
- Tu Nguyen
- Evgeny Kharitonov
- Apoorv Vyas
- Felix Kreuk
- Matt Le
- Xu Tan
- Zalán Borsos
- Dong Zhang
Papers from Jade Copet's perspective
Ordered by how strongly the researchers Jade Copet 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
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- On Generative Spoken Language Modeling from Raw Audio (2021) — 483 citations
- Code Llama: Open Foundation Models for Code (2023) — 3,394 citations
- High Fidelity Neural Audio Compression (2022) — 1,262 citations
- Speech Resynthesis from Discrete Disentangled Self-Supervised Representations (2021) — 398 citations
- Text-Free Prosody-Aware Generative Spoken Language Modeling (2021) — 159 citations
- Generative Spoken Dialogue Language Modeling (2022) — 155 citations
- AudioGen: Textually Guided Audio Generation (2022) — 466 citations
- textless-lib: a Library for Textless Spoken Language Processing (2022) — 41 citations
- Textually Pretrained Speech Language Models (2023) — 112 citations
- The Llama 3 Herd of Models (2024) — 17,085 citations
- Simple and Controllable Music Generation (2023) — 745 citations
- EXPRESSO: A Benchmark and Analysis of Discrete Expressive Speech Resynthesis (2023) — 130 citations
- Textless Speech Emotion Conversion using Decomposed and Discrete Representations (2021) — 42 citations