Roberta Raileanu

Artificial Intelligence · 2018–2024

A researcher in the Science corpus: 14 papers; trusts 486 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 573 citations into it; #19,840 of 35,345 by trust here, #3,111 by citations
• Reinforcement Learning — 278 citations into it; #1,804 of 2,242 by trust here, #1,004 by citations
• Computer Vision and Pattern Recognition — 189 citations into it
• Natural Language Processing — 155 citations into it
• Evaluation & Benchmarks — 50 citations into it
• Management Science and Operations Research — 46 citations into it; #8,808 of 11,946 by trust here, #5,092 by citations
• Control and Systems Engineering — 41 citations into it
• Representation & Self-Supervised Learning — 41 citations into it

Whom Roberta Raileanu trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yann LeCun
4. Yoshua Bengio
5. Alex Krizhevsky
6. J. Urgen Schmidhuber
7. Richard S. Sutton
8. Kaiming He
9. Ilya Sutskever
10. Pieter Abbeel

Who trusts Roberta Raileanu overall

1. Sae-Young Chung
2. Georgios Papoudakis
3. Denis Yarats
4. Minqi Jiang
5. Pablo Hernandez-Leal
6. Rishabh Agarwal
7. Ilya Kostrikov
8. Lin Guan
9. Andrew Kyle Lampinen
10. Filippos Christianos

Papers from Roberta Raileanu's perspective

Ordered by how strongly the researchers Roberta Raileanu 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Toolformer: Language Models Can Teach Themselves to Use Tools (2023) — 4,753 citations
• Open-Ended Learning Leads to Generally Capable Agents (2021) — 230 citations
• The NetHack Learning Environment (2020) — 233 citations
• Automatic Data Augmentation for Generalization in Deep Reinforcement Learning (2020) — 120 citations
• Improving Intrinsic Exploration with Language Abstractions (2022) — 78 citations
• Augmented Language Models: a Survey (2023) — 579 citations
• Chain-of-Verification Reduces Hallucination in Large Language Models (2023) — 515 citations
• Fast Adaptation to New Environments via Policy-Dynamics Value Functions (2020) — 24 citations
• Challenges and Applications of Large Language Models (2023) — 504 citations
• Modeling Others using Oneself in Multi-Agent Reinforcement Learning (2018) — 234 citations
• The Llama 3 Herd of Models (2024) — 17,085 citations
• Understanding the Effects of RLHF on LLM Generalisation and Diversity (2023) — 411 citations
• Building a Subspace of Policies for Scalable Continual Learning (2022) — 41 citations
• Automatic Data Augmentation for Generalization in Reinforcement Learning (2021) — 139 citations