Daniel Freeman

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Luke MetzOpenAIEmail yang diverifikasi di openai.com
Jascha Sohl-DicksteinAnthropicEmail yang diverifikasi di anthropic.com
Niru MaheswaranathanMeta Reality LabsEmail yang diverifikasi di fb.com
Ben PooleResearch Scientist at Google DeepMindEmail yang diverifikasi di stanford.edu
K. Birgitta WHALEYUniversity of California, BerkeleyEmail yang diverifikasi di berkeley.edu
Norman TubmanNASA AmesEmail yang diverifikasi di u.northwestern.edu
Joan BrunaProfessor of Computer Science, Data Science & Mathematics (aff), Courant Institute and CDS, NYUEmail yang diverifikasi di cims.nyu.edu
Ruoxi SunColumbia University, GoogleEmail yang diverifikasi di columbia.edu
Martin HEAD-GORDONKenneth S. Pitzer Distinguished Professor, University of California, BerkeleyEmail yang diverifikasi di cchem.berkeley.edu
Daniel S LevineResearch Scientist, Fundamental AI Research - Chemistry at MetaEmail yang diverifikasi di meta.com
Diptarka HaitStanford Science Fellow, Department of Chemistry, Stanford UniversityEmail yang diverifikasi di stanford.edu
Dylan GormanUC Berkeley, Department of PhysicsEmail yang diverifikasi di berkeley.edu
Brian SwingleAssociate Professor, Brandeis UniversityEmail yang diverifikasi di brandeis.edu
Raghu MahajanInternational Center for Theoretical SciencesEmail yang diverifikasi di icts.res.in
Edwin Miles StoudenmireResearch Scientist, Flatiron Institute, Center for Computational Quantum PhysicsEmail yang diverifikasi di flatironinstitute.org
William J. HugginsGoogle Quantum AIEmail yang diverifikasi di google.com
Mohan SarovarDistinguished Member of Technical Staff, Sandia National LaboratoriesEmail yang diverifikasi di sandia.gov
Kirk A PetersonProfessor of Chemistry, Washington State UniversityEmail yang diverifikasi di wsu.edu

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Daniel Freeman

Anthropic

Email yang diverifikasi di berkeley.edu

Metalearning Reinforcement Learning Bayesian Inference Quantum Information


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022	1390	2022
Brax--a differentiable physics engine for large scale rigid body simulation CD Freeman, E Frey, A Raichuk, S Girgin, I Mordatch, O Bachem arXiv preprint arXiv:2106.13281, 2021	361*	2021
Topology and geometry of half-rectified network optimization CD Freeman, J Bruna arXiv preprint arXiv:1611.01540, 2016	289	2016
Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet, 2024 A Templeton, T Conerly, J Marcus, J Lindsey, T Bricken, B Chen, ... URL https://transformer-circuits. pub/2024/scaling-monosemanticity/index. html, 2024	269*	2024
Multi-game decision transformers KH Lee, O Nachum, MS Yang, L Lee, D Freeman, S Guadarrama, ... Advances in Neural Information Processing Systems 35, 27921-27936, 2022	254	2022
Modern approaches to exact diagonalization and selected configuration interaction with the adaptive sampling CI method NM Tubman, CD Freeman, DS Levine, D Hait, M Head-Gordon, ... Journal of chemical theory and computation 16 (4), 2139-2159, 2020	180	2020
Understanding and correcting pathologies in the training of learned optimizers L Metz, N Maheswaranathan, J Nixon, D Freeman, J Sohl-Dickstein International Conference on Machine Learning, 4556-4565, 2019	180	2019
Gradients are not all you need L Metz, CD Freeman, SS Schoenholz, T Kachman arXiv preprint arXiv:2111.05803, 2021	95	2021
Velo: Training versatile learned optimizers by scaling up L Metz, J Harrison, CD Freeman, A Merchant, L Beyer, J Bradbury, ... arXiv preprint arXiv:2211.09760, 2022	77	2022
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves L Metz, N Maheswaranathan, CD Freeman, B Poole, J Sohl-Dickstein arXiv preprint arXiv:2009.11243, 2020	62	2020
Barkour: Benchmarking animal-level agility with quadruped robots K Caluwaerts, A Iscen, JC Kew, W Yu, T Zhang, D Freeman, KH Lee, ... arXiv preprint arXiv:2305.14654, 2023	53	2023
Learning to predict without looking ahead: World models without forward prediction D Freeman, D Ha, L Metz Advances in Neural Information Processing Systems 32, 2019	48	2019
Using a thousand optimization tasks to learn hyperparameter search strategies L Metz, N Maheswaranathan, R Sun, CD Freeman, B Poole, ... arXiv preprint arXiv:2002.11887, 2020	40	2020
Improving large language model fine-tuning for solving math problems Y Liu, A Singh, CD Freeman, JD Co-Reyes, PJ Liu arXiv preprint arXiv:2310.10047, 2023	39	2023
Practical tradeoffs between memory, compute, and performance in learned optimizers L Metz, CD Freeman, J Harrison, N Maheswaranathan, J Sohl-Dickstein Conference on Lifelong Learning Agents, 142-164, 2022	34	2022
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models, 2022 A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... URL https://arxiv. org/abs/2206.04615, 2022	34	2022
A method for the determination of speed-dependent semi-classical vector correlations from sliced image anisotropies MP Grubb, ML Warter, CD Freeman, NA West, KM Usakoski, KM Johnson, ... The Journal of chemical physics 135 (9), 2011	33*	2011
Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning S Kamyar Seyed Ghasemipour, D Freeman, B David, SS Gu, S Kataoka, ... arXiv e-prints, arXiv: 2203.13733, 2022	28*	2022
Transformer-based learned optimization E Gärtner, L Metz, M Andriluka, CD Freeman, C Sminchisescu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023	23	2023
Relaxation dynamics of the toric code in contact with a thermal reservoir: Finite-size scaling in a low-temperature regime CD Freeman, CM Herdman, DJ Gorman, KB Whaley Physical Review B 90 (13), 134302, 2014	22	2014

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