11 citations · 21 across the 3 of their papers we have counts for
3 papers
cs.AI2023★ 5 cited
Predictive auxiliary objectives in deep RL mimic learning in the brain
Ching Fang, Kimberly L Stachenfeld
The ability to predict upcoming events has been hypothesized to comprise a key aspect of natural and machine cognition. This is supported by trends in deep reinforcement learning (…
q-bio.NC2019★ 11 cited
Probabilistic Successor Representations with Kalman Temporal Differences
Jesse P. Geerts, Kimberly L. Stachenfeld, Neil Burgess
The effectiveness of Reinforcement Learning (RL) depends on an animal's ability to assign credit for rewards to the appropriate preceding stimuli. One aspect of understanding the n…
cs.LG2018★ 5 cited
Spectral Inference Networks: Unifying Deep and Spectral Learning
David Pfau, Stig Petersen, Ashish Agarwal +2
We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature…