Marc Dymetman

Artificial Intelligence · 2012–2023

A researcher in the Science corpus: 9 papers; trusts 472 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 247 citations into it; #5,581 of 35,345 by trust here, #7,797 by citations
• Natural Language Processing — 163 citations into it; #1,669 of 5,688 by trust here, #2,422 by citations
• Computer Vision and Pattern Recognition — 80 citations into it
• Reinforcement Learning — 74 citations into it; #1,367 of 2,242 by trust here, #2,063 by citations
• Large Language Models — 43 citations into it
• Probabilistic & Bayesian Methods — 43 citations into it
• Generative Models — 29 citations into it; #1,520 of 1,956 by trust here, #1,926 by citations
• Evaluation & Benchmarks — 28 citations into it

Whom Marc Dymetman trusts overall

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

1. Geoffrey E. Hinton
2. Ronald J. Williams
3. Yoshua Bengio
4. Ilya Sutskever
5. Yann LeCun
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Oriol Vinyals
9. Marc'Aurelio Ranzato
10. Richard S. Sutton

Who trusts Marc Dymetman overall

1. Tomasz Korbak
2. Bryan Eikema
3. Hady ElSahar
4. Germán Kruszewski
5. Dongyoung Go
6. Aishwarya Kamath
7. Bo Chen
8. Ben Bogin
9. Byeong-Hoon So
10. Andrew Chisholm

Papers from Marc Dymetman's perspective

Ordered by how strongly the researchers Marc Dymetman 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Sequence-based Structured Prediction for Semantic Parsing (2016) — 122 citations
• A Distributional Approach to Controlled Text Generation (2020) — 140 citations
• On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting (2022) — 94 citations
• Aligning Language Models with Preferences through f-divergence Minimization (2023) — 126 citations
• Global Autoregressive Models for Data-Efficient Sequence Learning (2019) — 23 citations
• Controlling Conditional Language Models without Catastrophic Forgetting (2021) — 43 citations
• Distributional Reinforcement Learning for Energy-Based Sequential Models (2019) — 23 citations
• Energy-Based Models for Code Generation under Compilability Constraints (2021) — 14 citations
• The OS* Algorithm: a Joint Approach to Exact Optimization and Sampling (2012) — 13 citations