no code implementations • 2 Feb 2024 • Debarun Bhattacharjya, JunKyu Lee, Don Joven Agravante, Balaji Ganesan, Radu Marinescu
Foundation models (FMs) such as large language models have revolutionized the field of AI by showing remarkable performance in various tasks.
no code implementations • 6 Jun 2023 • Yeldar Toleubay, Don Joven Agravante, Daiki Kimura, Baihan Lin, Djallel Bouneffouf, Michiaki Tatsubori
The proposed system addresses the lack of explainability of current Neural Network models and provides a more trustworthy solution for mental disorder diagnosis.
1 code implementation • 1 Mar 2022 • JunKyu Lee, Michael Katz, Don Joven Agravante, Miao Liu, Geraud Nangue Tasse, Tim Klinger, Shirin Sohrabi
Our approach defines options in hierarchical reinforcement learning (HRL) from AIP operators by establishing a correspondence between the state transition model of AI planning problem and the abstract state transition system of a Markov Decision Process (MDP).
1 code implementation • ACL 2021 • Daiki Kimura, Subhajit Chaudhury, Masaki Ono, Michiaki Tatsubori, Don Joven Agravante, Asim Munawar, Akifumi Wachi, Ryosuke Kohita, Alexander Gray
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 network and symbolic knowledge acquisition approach for natural language interaction games.
no code implementations • EMNLP 2021 • Daiki Kimura, Masaki Ono, Subhajit Chaudhury, Ryosuke Kohita, Akifumi Wachi, Don Joven Agravante, Michiaki Tatsubori, Asim Munawar, Alexander Gray
Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided.
no code implementations • 10 Dec 2020 • Guillaume Le Moing, Don Joven Agravante, Tadanobu Inoue, Jayakorn Vongkulbhisal, Asim Munawar, Ryuki Tachibana, Phongtharin Vinayavekhin
This paper introduces an ensemble of discriminators that improves the accuracy of a domain adaptation technique for the localization of multiple sound sources.
no code implementations • 10 Dec 2020 • Guillaume Le Moing, Phongtharin Vinayavekhin, Don Joven Agravante, Tadanobu Inoue, Jayakorn Vongkulbhisal, Asim Munawar, Ryuki Tachibana
Moreover, learning for different microphone array layouts makes the task more complicated due to the infinite number of possible layouts.
no code implementations • 10 Dec 2020 • Guillaume Le Moing, Phongtharin Vinayavekhin, Tadanobu Inoue, Jayakorn Vongkulbhisal, Asim Munawar, Ryuki Tachibana, Don Joven Agravante
In this paper, we propose novel deep learning based algorithms for multiple sound source localization.
1 code implementation • 21 Sep 2018 • Tu-Hoa Pham, Giovanni De Magistris, Don Joven Agravante, Subhajit Chaudhury, Asim Munawar, Ryuki Tachibana
We consider the problem of reinforcement learning under safety requirements, in which an agent is trained to complete a given task, typically formalized as the maximization of a reward signal over time, while concurrently avoiding undesirable actions or states, associated to lower rewards, or penalties.
no code implementations • 22 Jun 2018 • Phongtharin Vinayavekhin, Subhajit Chaudhury, Asim Munawar, Don Joven Agravante, Giovanni De Magistris, Daiki Kimura, Ryuki Tachibana
This paper is a contribution towards interpretability of the deep learning models in different applications of time-series.