Ted Moskovitz
Ted Moskovitz
Gatsby Unit, UCL
Verified email at - Homepage
Cited by
Cited by
Feedback alignment in deep convolutional networks
TH Moskovitz, A Litwin-Kumar, LF Abbott
arXiv preprint arXiv:1812.06488, 2018
Tactical Optimism and Pessimism for Deep Reinforcement Learning
T Moskovitz, J Parker-Holder, A Pacchiano, M Arbel, MI Jordan
Neural Information Processing Systems (NeurIPS) 2021, 2021
Efficient Wasserstein Natural Gradients for Reinforcement Learning
T Moskovitz, M Arbel, F Huszar, A Gretton
International Conference on Learning Representations (ICLR) 2021, 2020
A First-Occupancy Representation for Reinforcement Learning
T Moskovitz, SR Wilson, M Sahani
International Conference on Learning Representations (ICLR) 2022, 2021
A comparison of deep learning and linear-nonlinear cascade approaches to neural encoding
TH Moskovitz, NA Roy, JW Pillow
BioRxiv, 463422, 2018
Towards an Understanding of Default Policies in Multitask Policy Optimization
T Moskovitz, M Arbel, J Parker-Holder, A Pacchiano
Conference on Artificial Intelligence and Statistics (AISTATS) 2022, 2021
First-Order Preconditioning via Hypergradient Descent
T Moskovitz, R Wang, J Lan, S Kapoor, T Miconi, J Yosinski, A Rawal
NeurIPS 2019, Beyond First Order Methods in ML Workshop, 2019
ReLOAD: Reinforcement learning with optimistic ascent-descent for last-iterate convergence in constrained mdps
T Moskovitz, B O’Donoghue, V Veeriah, S Flennerhag, S Singh, T Zahavy
International Conference on Machine Learning, 25303-25336, 2023
Action prediction error: a value-free dopaminergic teaching signal that drives stable learning
F Greenstreet, HM Vergara, S Pati, L Schwarz, M Wisdom, F Marbach, ...
bioRxiv, 2022
Amortised Learning by Wake-Sleep
LK Wenliang, T Moskovitz, H Kanagawa, M Sahani
International Conference on Machine Learning (ICML) 2020, 2020
A unified theory of dual-process control
T Moskovitz, K Miller, M Sahani, MM Botvinick
arXiv preprint arXiv:2211.07036, 2022
Understanding the functional and structural differences across excitatory and inhibitory neurons
S Minni, L Ji-An, T Moskovitz, G Lindsay, K Miller, M Dipoppa, GR Yang
bioRxiv, 680439, 2019
Do Biologically-Realistic Recurrent Architectures Produce Biologically-Realistic Models?
G Lindsay, T Moskovitz, GR Yang, K Miller
Conference on Cognitive Computational Neuroscience (CCN) 2019, 2019
A State Representation for Diminishing Rewards
T Moskovitz, S Hromadka, A Touati, D Borsa, M Sahani
arXiv preprint arXiv:2309.03710, 2023
Undo Maps: A Tool for Adapting Policies to Perceptual Distortions
A Gupta, T Moskovitz, D Alvarez-Melis, A Pacchiano
ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, 2023
Minimum Description Length Control
T Moskovitz, TC Kao, M Sahani, MM Botvinick
International Conference on Learning Representations (ICLR) 2023, 2022
Deep Transfer Learning for Language Generation from Limited Corpora
T Moskovitz
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