| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 467 | 2023 |
| learn2learn: A library for meta-learning research SMR Arnold, P Mahajan, D Datta, I Bunner, KS Zarkias arXiv preprint arXiv:2008.12284, 2020 | 78 | 2020 |
| When maml can adapt fast and how to assist when it cannot S Arnold, S Iqbal, F Sha International Conference on Artificial Intelligence and Statistics, 244-252, 2021 | 27 | 2021 |
| Reducing the variance in online optimization by transporting past gradients S Arnold, PA Manzagol, R Babanezhad Harikandeh, I Mitliagkas, ... Advances in Neural Information Processing Systems 32, 2019 | 21 | 2019 |
| A domain-agnostic approach for characterization of lifelong learning systems MM Baker, A New, M Aguilar-Simon, Z Al-Halah, SMR Arnold, ... Neural Networks 160, 274-296, 2023 | 15 | 2023 |
| Roboclip: One demonstration is enough to learn robot policies S Sontakke, J Zhang, S Arnold, K Pertsch, E Býyýk, D Sadigh, C Finn, L Itti Advances in Neural Information Processing Systems 36, 2024 | 11 | 2024 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 8 | 2024 |
| Uniform sampling over episode difficulty S Arnold, G Dhillon, A Ravichandran, S Soatto Advances in Neural Information Processing Systems 34, 1481-1493, 2021 | 8 | 2021 |
| learn2learn, 2019 SM Arnold, P Mahajan, D Datta, I Bunner | 7 | |
| Embedding adaptation is still needed for few-shot learning SMR Arnold, F Sha arXiv preprint arXiv:2104.07255, 2021 | 6* | 2021 |
| Analyzing the variance of policy gradient estimators for the linear-quadratic regulator JA Preiss, SMR Arnold, CY Wei, M Kloft arXiv preprint arXiv:1910.01249, 2019 | 6 | 2019 |
| Decoupling adaptation from modeling with meta-optimizers for meta learning SMR Arnold, S Iqbal, F Sha | 5* | 2019 |
| Can an LLM-Powered Socially Assistive Robot Effectively and Safely Deliver Cognitive Behavioral Therapy? A Study With University Students MJ Kian, M Zong, K Fischer, A Singh, AM Velentza, P Sang, S Upadhyay, ... arXiv preprint arXiv:2402.17937, 2024 | 4 | 2024 |
| Policy learning and evaluation with randomized quasi-Monte Carlo SMR Arnold, P L'Ecuyer, L Chen, Y Chen, F Sha arXiv preprint arXiv:2202.07808, 2022 | 4 | 2022 |
| Writing distributed applications with PyTorch S Arnold | 2 | 2017 |
| An Introduction to Distributed Deep Learning S Arnold | 2 | 2016 |
| Policy-Induced Self-Supervision Improves Representation Finetuning in Visual RL SMR Arnold, F Sha arXiv preprint arXiv:2302.06009, 2023 | | 2023 |
| Accelerating SGD for Distributed Deep-Learning Using Approximated Hessian Matrix SMR Arnold, C Wang arXiv preprint arXiv:1709.05069, 2017 | | 2017 |
| Shapechanger: Environments for Transfer Learning SMR Arnold, TK Pun, TTJ Denisart, FJ Valero-Cuevas arXiv preprint arXiv:1709.05070, 2017 | | 2017 |
| A Greedy Algorithm to Cluster Specialists S Arnold arXiv preprint arXiv:1609.03666, 2016 | | 2016 |
