Zobrazeno 1 - 10
of 130
pro vyhledávání: '"A. Agravante"'
Foundation models (FMs) such as large language models have revolutionized the field of AI by showing remarkable performance in various tasks. However, they exhibit numerous limitations that prevent their broader adoption in many real-world systems, w
Externí odkaz:
http://arxiv.org/abs/2402.01602
Autor:
Toleubay, Yeldar, Agravante, Don Joven, Kimura, Daiki, Lin, Baihan, Bouneffouf, Djallel, Tatsubori, Michiaki
In response to the global challenge of mental health problems, we proposes a Logical Neural Network (LNN) based Neuro-Symbolic AI method for the diagnosis of mental disorders. Due to the lack of effective therapy coverage for mental disorders, there
Externí odkaz:
http://arxiv.org/abs/2306.03902
Autor:
Lee, Junkyu, Katz, Michael, Agravante, Don Joven, Liu, Miao, Tasse, Geraud Nangue, Klinger, Tim, Sohrabi, Shirin
Two common approaches to sequential decision-making are AI planning (AIP) and reinforcement learning (RL). Each has strengths and weaknesses. AIP is interpretable, easy to integrate with symbolic knowledge, and often efficient, but requires an up-fro
Externí odkaz:
http://arxiv.org/abs/2203.00669
Autor:
Kimura, Daiki, Chaudhury, Subhajit, Ono, Masaki, Tatsubori, Michiaki, Agravante, Don Joven, Munawar, Asim, Wachi, Akifumi, Kohita, Ryosuke, Gray, Alexander
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
Externí odkaz:
http://arxiv.org/abs/2110.10973
Autor:
Kimura, Daiki, Ono, Masaki, Chaudhury, Subhajit, Kohita, Ryosuke, Wachi, Akifumi, Agravante, Don Joven, Tatsubori, Michiaki, Munawar, Asim, Gray, Alexander
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 convergence and interpretability for the policy in RL, we propose a novel
Externí odkaz:
http://arxiv.org/abs/2110.10963
Autor:
Moing, Guillaume Le, Agravante, Don Joven, Inoue, Tadanobu, Vongkulbhisal, Jayakorn, Munawar, Asim, Tachibana, Ryuki, Vinayavekhin, Phongtharin
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 neural networks have led to promising results for this task, yet they requi
Externí odkaz:
http://arxiv.org/abs/2012.05908
Autor:
Moing, Guillaume Le, Vinayavekhin, Phongtharin, Agravante, Don Joven, Inoue, Tadanobu, Vongkulbhisal, Jayakorn, Munawar, Asim, Tachibana, Ryuki
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 acoustic conditions and microphone array layouts. One can leverage acoustic
Externí odkaz:
http://arxiv.org/abs/2012.05533
Autor:
Moing, Guillaume Le, Vinayavekhin, Phongtharin, Inoue, Tadanobu, Vongkulbhisal, Jayakorn, Munawar, Asim, Tachibana, Ryuki, Agravante, Don Joven
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 sources in an enclosed environment by using multiple microphone arrays.
Externí odkaz:
http://arxiv.org/abs/2012.05515
Autor:
Sylvester Cortes, Alma Agero, Elena Maria Agravante, Janelyn Arado, Cynthia Anne Arbilon, Eddalin Lampawog, Arlene Fe Letrondo, Anne Lorca, Asuncion Monsanto, Hedeliza Pineda, Cristina Ramas, Raamah Rosales, Cecile Sadili, Juanita Sayson, Ryan Tubog
Publikováno v:
Cogent Education, Vol 10, Iss 2 (2023)
With the declining number of students interested in pursuing STEM courses such as Bachelor’s Degree in Biology as evidenced by low enrollment, HEIs currently offering and those which intend to offer the academic degree program are competing and loo
Externí odkaz:
https://doaj.org/article/f4a49cb8411d4b17af6307d62c7b9f2c
Autor:
Aba, Richard Paolo M., Bayaga, Cecile Leah T., Peralta, Justin Godfred B., Agravante, Stephen Jan M., Cauilan, Judith J., Gabriel, Alonzo A.
Publikováno v:
In Food and Humanity December 2023 1:536-542