177 citations · 387 across the 12 of their papers we have counts for
5 papers · 1 filter
Fast Context Adaptation via Meta-Learning
Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin +2
We propose CAVIA for meta-learning, a simple extension to MAML that is less prone to meta-overfitting, easier to parallelise, and more interpretable. CAVIA partitions the model par…
Depth and nonlinearity induce implicit exploration for RL
Justas Dauparas, Ryota Tomioka, Katja Hofmann
The question of how to explore, i.e., take actions with uncertain outcomes to learn about possible future rewards, is a key question in reinforcement learning (RL). Here, we show a…
Variational Inference for Data-Efficient Model Learning in POMDPs
Sebastian Tschiatschek, Kai Arulkumaran, Jan Stühmer +1
Partially observable Markov decision processes (POMDPs) are a powerful abstraction for tasks that require decision making under uncertainty, and capture a wide range of real world…
Cross Domain Regularization for Neural Ranking Models Using Adversarial Learning
Daniel Cohen, Bhaskar Mitra, Katja Hofmann +1
Unlike traditional learning to rank models that depend on hand-crafted features, neural representation learning models learn higher level features for the ranking task by training…
Meta Reinforcement Learning with Latent Variable Gaussian Processes
Steindór Sæmundsson, Katja Hofmann, Marc Peter Deisenroth
Learning from small data sets is critical in many practical applications where data collection is time consuming or expensive, e.g., robotics, animal experiments or drug design. Me…