Eric Anthony Mitchell

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Chelsea FinnStanford University, Physical IntelligencePatvirtintas el. paštas cs.stanford.edu
Christopher D ManningProfessor of Computer Science and Linguistics, Stanford UniversityPatvirtintas el. paštas stanford.edu
Archit SharmaPhD Student, Stanford UniversityPatvirtintas el. paštas stanford.edu
Rafael Mitkov RafailovGraduate Student, Stanford UniversityPatvirtintas el. paštas stanford.edu
Stefano ErmonStanford UniversityPatvirtintas el. paštas cs.stanford.edu
Antoine BosselutEPFLPatvirtintas el. paštas epfl.ch
Yoonho LeePhD Student, Stanford UniversityPatvirtintas el. paštas stanford.edu
H Sebastian SeungProfessor, Princeton Neuroscience Institute and Computer Science Dept.Patvirtintas el. paštas princeton.edu
Huaxiu YaoAssistant Professor of Computer Science and Data Science, UNC Chapel HillPatvirtintas el. paštas cs.unc.edu
Volkan IslerProfessor, The University of Texas at AustinPatvirtintas el. paštas cs.utexas.edu
Ben EisnerML/Robotics Researcher, PhD Student at Carnegie MellonPatvirtintas el. paštas andrew.cmu.edu
Daniel D. LeeTisch University Professor of ECE, Cornell UniversityPatvirtintas el. paštas alum.mit.edu
Riley Simmons-EdlerPhD Student, Princeton UniversityPatvirtintas el. paštas princeton.edu
Sergey LevineUC Berkeley, Physical IntelligencePatvirtintas el. paštas eecs.berkeley.edu
Allan ZhouGoogle DeepMindPatvirtintas el. paštas stanford.edu
Selim EnginSonyPatvirtintas el. paštas umn.edu
Kevin ChenApplePatvirtintas el. paštas apple.com
Silvio SavareseAssociate Professor of Computer Science at Stanford UniversityPatvirtintas el. paštas stanford.edu
Suraj NairPhysical IntelligencePatvirtintas el. paštas physicalintelligence.company
Xue Bin PengAssistant Professor at Simon Fraser University, Research Scientist at NVIDIAPatvirtintas el. paštas sfu.ca

Stebėti

Eric Anthony Mitchell

PhD Candidate, Stanford University

Patvirtintas el. paštas stanford.edu - Pagrindinis puslapis

meta-learning continual learning nlp


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Direct preference optimization: Your language model is secretly a reward model R Rafailov, A Sharma, E Mitchell, S Ermon, CD Manning, C Finn Neural Information Processing Systems (NeurIPS), 2023	2580	2023
Detectgpt: Zero-shot machine-generated text detection using probability curvature E Mitchell, Y Lee, A Khazatsky, CD Manning, C Finn International Conference on Machine Learning (ICML), 2023	590	2023
Fast model editing at scale E Mitchell, C Lin, A Bosselut, C Finn, CD Manning International Conference on Learning Representations (ICLR), 2021	452	2021
Memory-Based Model Editing at Scale E Mitchell, C Lin, A Bosselut, CD Manning, C Finn International Conference on Machine Learning (ICML), 2022	313	2022
Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback K Tian, E Mitchell, A Zhou, A Sharma, R Rafailov, H Yao, C Finn, ... Empirical Methods in Natural Language Processing (EMNLP), 2023	249	2023
FlyWire: online community for whole-brain connectomics S Dorkenwald, CE McKellar, T Macrina, N Kemnitz, K Lee, R Lu, J Wu, ... Nature methods 19 (1), 119-128, 2022	215	2022
Neuronal wiring diagram of an adult brain S Dorkenwald, A Matsliah, AR Sterling, P Schlegel, SC Yu, CE McKellar, ... Nature 634 (8032), 124-138, 2024	209	2024
Functional connectomics spanning multiple areas of mouse visual cortex MICrONS Consortium, JA Bae, M Baptiste, CA Bishop, AL Bodor, ... BioRxiv, 2021.07. 28.454025, 2021	173	2021
Learning language-conditioned robot behavior from offline data and crowd-sourced annotation S Nair, E Mitchell, K Chen, S Savarese, C Finn Conference on Robot Learning, 1303-1315, 2022	160	2022
Fine-tuning language models for factuality K Tian, E Mitchell, H Yao, CD Manning, C Finn The Twelfth International Conference on Learning Representations, 2023	141	2023
Offline Meta-Reinforcement Learning with Advantage Weighting E Mitchell, R Rafailov, XB Peng, S Levine, C Finn International Conference on Machine Learning (ICML), 2021	126	2021
On the opportunities and risks of foundation models. arXiv 2021 R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S Von Arx, ... arXiv preprint arXiv:2108.07258, 2023	112	2023
Identifying and mitigating the security risks of generative ai C Barrett, B Boyd, E Bursztein, N Carlini, B Chen, J Choi, AR Chowdhury, ... Foundations and Trends® in Privacy and Security 6 (1), 1-52, 2023	91	2023
Direct preference optimization: Your language model is secretly a reward model, 2024 R Rafailov, A Sharma, E Mitchell, S Ermon, CD Manning, C Finn URL https://arxiv. org/abs/2305.18290 2305, 2023	87	2023
Openai o1 system card A Jaech, A Kalai, A Lerer, A Richardson, A El-Kishky, A Low, A Helyar, ... arXiv preprint arXiv:2412.16720, 2024	73	2024
Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex CM Schneider-Mizell, AL Bodor, D Brittain, JA Buchanan, DJ Bumbarger, ... bioRxiv, 2023.01. 23.525290, 2024	43	2024
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference E Mitchell, JJ Noh, S Li, WS Armstrong, A Agarwal, P Liu, C Finn, ... Empirical Methods in Natural Language Processing (EMNLP), 2022	42	2022
An emulator for fine-tuning large language models using small language models E Mitchell, R Rafailov, A Sharma, C Finn, CD Manning arXiv preprint arXiv:2310.12962, 2023	41	2023
Self-destructing models: Increasing the costs of harmful dual uses of foundation models P Henderson, E Mitchell, C Manning, D Jurafsky, C Finn Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 287-296, 2023	39	2023
Petascale neural circuit reconstruction: automated methods T Macrina, K Lee, R Lu, NL Turner, J Wu, S Popovych, W Silversmith, ... bioRxiv, 2021.08. 04.455162, 2021	38	2021

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