29 citations · 54 across the 13 of their papers we have counts for
20 papers
LOA: Logical Optimal Actions for Text-based Interaction Games
Daiki Kimura, Subhajit Chaudhury, Masaki Ono +6
We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural ne…
Neuro-Symbolic Reinforcement Learning with First-Order Logic
Daiki Kimura, Masaki Ono, Subhajit Chaudhury +6
Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided. In order to achieve fast conv…
Reinforcement Learning with External Knowledge by using Logical Neural Networks
Daiki Kimura, Subhajit Chaudhury, Akifumi Wachi +4
Conventional deep reinforcement learning methods are sample-inefficient and usually require a large number of training trials before convergence. Since such methods operate on an u…
Learning Multiple Sound Source 2D Localization
Guillaume Le Moing, Phongtharin Vinayavekhin, Tadanobu Inoue +4
In this paper, we propose novel deep learning based algorithms for multiple sound source localization. Specifically, we aim to find the 2D Cartesian coordinates of multiple sound s…
Ensemble of Discriminators for Domain Adaptation in Multiple Sound Source 2D Localization
Guillaume Le Moing, Don Joven Agravante, Tadanobu Inoue +4
This paper introduces an ensemble of discriminators that improves the accuracy of a domain adaptation technique for the localization of multiple sound sources. Recently, deep neura…
Data-Efficient Framework for Real-world Multiple Sound Source 2D Localization
Guillaume Le Moing, Phongtharin Vinayavekhin, Don Joven Agravante +4
Deep neural networks have recently led to promising results for the task of multiple sound source localization. Yet, they require a lot of training data to cover a variety of acous…