36 citations · 52 across the 6 of their papers we have counts for
6 papers
RAMP: Recognition parametrisation by Amortised Message Passing
Lior Fox, Kai Biegun, James Heald +3
A central aim of unsupervised learning is to uncover latent factors that explain dependencies among observations. Probabilistic models typically achieve this by introducing multipl…
Maximum Likelihood Learning of Latent Dynamics Without Reconstruction
Samo Hromadka, Kai Biegun, Lior Fox +2
We introduce a novel unsupervised learning method for time series data with latent dynamical structure: the recognition-parametrized Gaussian state space model (RP-GSSM). The RP-GS…
Is there Value in Reinforcement Learning?
Lior Fox, Yonatan Loewenstein
Action-values play a central role in popular Reinforcement Learing (RL) models of behavior. Yet, the idea that action-values are explicitly represented has been extensively debated…
Reinforcement Learning with Large Action Spaces for Neural Machine Translation
Asaf Yehudai, Leshem Choshen, Lior Fox +1
Applying Reinforcement learning (RL) following maximum likelihood estimation (MLE) pre-training is a versatile method for enhancing neural machine translation (NMT) performance. Ho…
On the Weaknesses of Reinforcement Learning for Neural Machine Translation
Leshem Choshen, Lior Fox, Zohar Aizenbud +1
Reinforcement learning (RL) is frequently used to increase performance in text generation tasks, including machine translation (MT), notably through the use of Minimum Risk Trainin…
DORA The Explorer: Directed Outreaching Reinforcement Action-Selection
Leshem Choshen, Lior Fox, Yonatan Loewenstein
Exploration is a fundamental aspect of Reinforcement Learning, typically implemented using stochastic action-selection. Exploration, however, can be more efficient if directed towa…