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Recently, the connectionist temporal classification (CTC) model coupled with recurrent (RNN) or convolutional neural networks (CNN), …
In Reinforcement Learning (RL), an agent is guided by the rewards it receives from the reward function. Unfortunately, it may take many …
Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, …
This paper examines the problem of how to teach multiple tasks to a Reinforcement Learning (RL) agent. To this end, we use Linear …
In this paper we propose Reward Machines – a type of finite state machine that supports the spec- ification of reward functions while …
The aim of the study is to provide interesting insights on how efficient machine learning algorithms could be adapted to solve com- …
The objective of unsupervised domain adaptation is to leverage features from a labeled source domain and learn a classifier for an …
We examine the problem of question answering over knowledge graphs, focusing on simple questions that can be answered by the lookup of …
Convergence to an optimal policy using model-based rein- forcement learning can require significant exploration of the environment. In …
At present, the vast majority of building blocks, techniques, and architectures for deep learning are based on real-valued operations …