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ORCIDKimberly L. Stachenfeld
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- affiliation: Google DeepMind, London, UK
- affiliation: Princeton University, Department of Psychology, NJ, USA
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2020 – today
- 2024
[c9]Ching Fang, Kim Stachenfeld:
Predictive auxiliary objectives in deep RL mimic learning in the brain. ICLR 2024- [c8]William F. Whitney, Tatiana Lopez-Guevara, Tobias Pfaff, Yulia Rubanova, Thomas Kipf, Kim Stachenfeld, Kelsey R. Allen:
Learning 3D Particle-based Simulators from RGB-D Videos. ICLR 2024 - [i15]Benjamin Peters, James J. DiCarlo, Todd M. Gureckis, Ralf Haefner, Leyla Isik, Joshua B. Tenenbaum, Talia Konkle, Thomas Naselaris, Kimberly L. Stachenfeld, Zenna Tavares, Doris Tsao, Ilker Yildirim, Nikolaus Kriegeskorte:
How does the primate brain combine generative and discriminative computations in vision? CoRR abs/2401.06005 (2024) - [i14]Tatiana Lopez-Guevara, Yulia Rubanova, William F. Whitney, Tobias Pfaff, Kimberly L. Stachenfeld, Kelsey R. Allen:
Scaling Face Interaction Graph Networks to Real World Scenes. CoRR abs/2401.11985 (2024) - [i13]Yulia Rubanova, Tatiana Lopez-Guevara, Kelsey R. Allen, William F. Whitney, Kimberly L. Stachenfeld, Tobias Pfaff:
Learning rigid-body simulators over implicit shapes for large-scale scenes and vision. CoRR abs/2405.14045 (2024) - [i12]Samuel Lippl, Kim Stachenfeld:
When does compositional structure yield compositional generalization? A kernel theory. CoRR abs/2405.16391 (2024) - [i11]Cynthia R. Steinhardt, Menoua Keshishian, Nima Mesgarani, Kim Stachenfeld:
DeepSpeech models show Human-like Performance and Processing of Cochlear Implant Inputs. CoRR abs/2407.20535 (2024) - 2023
- [c7]Tom M. George, Kimberly L. Stachenfeld, Caswell Barry, Claudia Clopath, Tomoki Fukai:
A generative model of the hippocampal formation trained with theta driven local learning rules. NeurIPS 2023 - [i10]Marin Vlastelica, Tatiana Lopez-Guevara, Kelsey R. Allen, Peter W. Battaglia, Arnaud Doucet, Kimberly L. Stachenfeld:
Diffusion Generative Inverse Design. CoRR abs/2309.02040 (2023) - [i9]Ching Fang, Kimberly L. Stachenfeld:
Predictive auxiliary objectives in deep RL mimic learning in the brain. CoRR abs/2310.06089 (2023) - [i8]William F. Whitney, Tatiana Lopez-Guevara, Tobias Pfaff, Yulia Rubanova, Thomas Kipf, Kimberly L. Stachenfeld, Kelsey R. Allen:
Learning 3D Particle-based Simulators from RGB-D Videos. CoRR abs/2312.05359 (2023) - 2022
- [j2]Daniel C. McNamee
, Kimberly L. Stachenfeld, Matthew M. Botvinick, Samuel J. Gershman:
Compositional Sequence Generation in the Entorhinal-Hippocampal System. Entropy 24(12): 1791 (2022) - [c6]Kelsey R. Allen, Tatiana Lopez-Guevara, Yulia Rubanova, Kimberly L. Stachenfeld, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Tobias Pfaff:
Graph network simulators can learn discontinuous, rigid contact dynamics. CoRL 2022: 1157-1167 - [c5]Kimberly L. Stachenfeld, Drummond Buschman Fielding, Dmitrii Kochkov, Miles D. Cranmer, Tobias Pfaff, Jonathan Godwin, Can Cui, Shirley Ho, Peter W. Battaglia, Alvaro Sanchez-Gonzalez:
Learned Simulators for Turbulence. ICLR 2022 - [c4]Kelsey R. Allen, Tatiana Lopez-Guevara, Kimberly L. Stachenfeld, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Jessica B. Hamrick, Tobias Pfaff:
Inverse Design for Fluid-Structure Interactions using Graph Network Simulators. NeurIPS 2022 - [i7]Kelsey R. Allen, Tatiana Lopez-Guevara, Kimberly L. Stachenfeld, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Jessica B. Hamrick, Tobias Pfaff:
Physical Design using Differentiable Learned Simulators. CoRR abs/2202.00728 (2022) - 2021
- [i6]Kimberly L. Stachenfeld, Jonathan Godwin, Peter W. Battaglia:
Graph Networks with Spectral Message Passing. CoRR abs/2101.00079 (2021) - [i5]Kimberly L. Stachenfeld, Drummond B. Fielding, Dmitrii Kochkov, Miles D. Cranmer, Tobias Pfaff, Jonathan Godwin, Can Cui, Shirley Ho, Peter W. Battaglia, Alvaro Sanchez-Gonzalez:
Learned Coarse Models for Efficient Turbulence Simulation. CoRR abs/2112.15275 (2021)
2010 – 2019
- 2019
- [c3]David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld:
Spectral Inference Networks: Unifying Deep and Spectral Learning. ICLR (Poster) 2019 - [c2]Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter W. Battaglia, Jessica B. Hamrick:
Structured agents for physical construction. ICML 2019: 464-474 - [i4]Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter W. Battaglia, Jessica B. Hamrick:
Structured agents for physical construction. CoRR abs/1904.03177 (2019) - [i3]Jesse P. Geerts, Kimberly L. Stachenfeld, Neil Burgess:
Probabilistic Successor Representations with Kalman Temporal Differences. CoRR abs/1910.02532 (2019) - [i2]Victor Bapst, Alvaro Sanchez-Gonzalez, Omar Shams, Kimberly L. Stachenfeld, Peter W. Battaglia, Satinder Singh, Jessica B. Hamrick:
Object-oriented state editing for HRL. CoRR abs/1910.14361 (2019) - 2018
- [j1]Jeremy R. Manning, Xia Zhu, Theodore L. Willke, Rajesh Ranganath, Kimberly L. Stachenfeld, Uri Hasson, David M. Blei, Kenneth A. Norman:
A probabilistic approach to discovering dynamic full-brain functional connectivity patterns. NeuroImage 180(Part): 243-252 (2018) - [i1]David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld:
Spectral Inference Networks: Unifying Spectral Methods With Deep Learning. CoRR abs/1806.02215 (2018) - 2014
- [c1]Kimberly L. Stachenfeld, Matthew M. Botvinick, Samuel Gershman:
Design Principles of the Hippocampal Cognitive Map. NIPS 2014: 2528-2536
Coauthor Index
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