Cyprien de Masson d'Autume
Artificial Intelligence · 2018–2022
A researcher in the Science corpus: 11 papers; trusts 303 researchers and is trusted by 32 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 297 citations into it; #7,032 of 35,345 by trust here, #1,706 by citations
- Natural Language Processing — 168 citations into it; #3,143 of 5,688 by trust here, #1,540 by citations
- Computer Vision and Pattern Recognition — 148 citations into it; #7,189 of 21,575 by trust here, #3,280 by citations
- Large Language Models — 75 citations into it
- Evaluation & Benchmarks — 63 citations into it
- Model Architectures — 51 citations into it
- Representation & Self-Supervised Learning — 39 citations into it; #1,288 of 3,145 by trust here, #1,027 by citations
- Generative Models — 24 citations into it
Whom Cyprien de Masson d'Autume trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Sepp Hochreiter
- Ronald J. Williams
- Yann LeCun
- Alex Graves
- Kaiming He
Who trusts Cyprien de Masson d'Autume overall
- Yiduo Guo
- Fan-Keng Sun
- B. Ermiş
- Thomas Scialom
- Nithin Holla
- Angeliki Lazaridou
- Qi Qin
- Xisen Jin
- Wenpeng Hu
- Chengwei Qin
Papers from Cyprien de Masson d'Autume's perspective
Ordered by how strongly the researchers Cyprien de Masson d'Autume 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Scaling Language Models: Methods, Analysis & Insights from Training Gopher (2021) — 1,640 citations
- Learning and Evaluating General Linguistic Intelligence (2019) — 219 citations
- Adaptive Semiparametric Language Models (2021) — 107 citations
- Training language GANs from Scratch (2019) — 91 citations
- Episodic Memory in Lifelong Language Learning (2019) — 341 citations
- Mind the Gap: Assessing Temporal Generalization in Neural Language Models (2021) — 298 citations
- StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models (2022) — 140 citations
- A Mutual Information Maximization Perspective of Language Representation Learning (2019) — 181 citations
- A Systematic Investigation of Commonsense Understanding in Large Language Models (2021) — 8 citations
- Pitfalls of Static Language Modelling (2021) — 54 citations
- Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents (2018) — 82 citations