| PaLM 2 Technical Report R Anil, AM Dai, O Firat, M Johnson, D Lepikhin, A Passos, S Shakeri, ... arXiv preprint arXiv:2305.10403, 2023 | 753 | 2023 |
| 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 | 407 | 2023 |
| A Large Self-Annotated Corpus for Sarcasm M Khodak, N Saunshi, K Vodrahalli LREC 2018, 2017 | 253 | 2017 |
| Mapping between fMRI responses to movies and their natural language annotations K Vodrahalli, PH Chen, Y Liang, C Baldassano, J Chen, E Yong, C Honey, ... NeuroImage, 2017 | 89 | 2017 |
| A compressed sensing view of unsupervised text embeddings, bag-of-n-grams, and LSTMs S Arora, M Khodak, N Saunshi, K Vodrahalli International Conference on Learning Representations (ICLR) 2018, 2018 | 49 | 2018 |
| Privacy accounting and quality control in the sage differentially private ML platform M Lécuyer, R Spahn, K Vodrahalli, R Geambasu, D Hsu Proceedings of the 27th ACM Symposium on Operating Systems Principles, 181-195, 2019 | 41 | 2019 |
| The logical options framework B Araki, X Li, K Vodrahalli, J DeCastro, M Fry, D Rus International Conference on Machine Learning, 307-317, 2021 | 23 | 2021 |
| Learning to Plan with Logical Automata B Araki, K Vodrahalli, T Leech, CI Vasile, M Donahue, D Rus Robotics: Systems and Science (RSS) 2019, 2019 | 22 | 2019 |
| Deep Bayesian Nonparametric Learning of Rules and Plans from Demonstrations with a Learned Automaton Prior B Araki, K Vodrahalli, T Leech, CI Vasile, M Donahue, D Rus AAAI 2020, 2020 | 7 | 2020 |
| Is Learning in Games Good for the Learners? W Brown, J Schneider, K Vodrahalli Advances in Neural Information Processing Systems 36, 2024 | 6 | 2024 |
| Attribute-efficient learning of monomials over highly-correlated variables A Andoni, R Dudeja, D Hsu, K Vodrahalli Algorithmic Learning Theory (ALT) 2019, 127-161, 2019 | 5 | 2019 |
| Estimating Trending Topics on Twitter with Small Subsets of the Total Data E Miller, K Vodrahalli, A Lee Technical Report, 2015 | 5 | 2015 |
| Nonlinear Initialization Methods for Low-Rank Neural Networks K Vodrahalli, R Shivanna, M Sathiamoorthy, S Jain, E Chi arXiv preprint arXiv:2202.00834, 2022 | 3 | 2022 |
| The Platform Design Problem C Papadimitriou, K Vodrahalli, M Yannakakis WINE 2021, 2021 | 3 | 2021 |
| Depth gradient based tracking KN Vodrahalli US Patent 9,367,731, 2016 | 3 | 2016 |
| Learning and planning with logical automata B Araki, K Vodrahalli, T Leech, CI Vasile, M Donahue, D Rus Autonomous Robots 45 (7), 1013-1028, 2021 | 1 | 2021 |
| History-Restricted Online Learning J Schneider, K Vodrahalli arXiv preprint arXiv:2205.14519, 2022 | | 2022 |
| Resource-Efficient Methods in Machine Learning KN Vodrahalli Columbia University, 2022 | | 2022 |
| A Temporal Decay Model for Mapping between fMRI and Natural Language Annotations K Vodrahalli, C Chen, V Mocz, C Baldassano, U Hasson, S Arora, ... Computational and Cognitive Neuroscience (CCN 2017), 2018 | | 2018 |
| Temporally Dependent Mappings Between fMRI Responses and Natural Language Descriptions of Natural Stimuli KN Vodrahalli Princeton University, 2017 | | 2017 |
Mikhail KhodakCMUcs.princeton.edu의 이메일 확인됨
