17 citations · 51 across the 7 of their papers we have counts for
7 papers
TorchRL: A data-driven decision-making library for PyTorch
Albert Bou, Matteo Bettini, Sebastian Dittert +5
PyTorch has ascended as a premier machine learning framework, yet it lacks a native and comprehensive library for decision and control tasks suitable for large development teams de…
CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning
Zhao Mandi, Homanga Bharadhwaj, Vincent Moens +3
Large-scale training have propelled significant progress in various sub-fields of AI such as computer vision and natural language processing. However, building robot learning syste…
Viscos Flows: Variational Schur Conditional Sampling With Normalizing Flows
Vincent Moens, Aivar Sootla, Haitham Bou Ammar +1
We present a method for conditional sampling for pre-trained normalizing flows when only part of an observation is available. We derive a lower bound to the conditioning variable l…
Efficient Semi-Implicit Variational Inference
Vincent Moens, Hang Ren, Alexandre Maraval +3
In this paper, we propose CI-VI an efficient and scalable solver for semi-implicit variational inference (SIVI). Our method, first, maps SIVI's evidence lower bound (ELBO) to a for…
SAMBA: Safe Model-Based & Active Reinforcement Learning
Alexander I. Cowen-Rivers, Daniel Palenicek, Vincent Moens +4
In this paper, we propose SAMBA, a novel framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. Our metho…
ILCRO: Making Importance Landscapes Flat Again
Vincent Moens, Simiao Yu, Gholamreza Salimi-Khorshidi
Convolutional neural networks have had a great success in numerous tasks, including image classification, object detection, sequence modelling, and many more. It is generally assum…