1 code implementation • 20 Oct 2023 • Eduardo Soares, Akihiro Kishimoto, Emilio Vital Brazil, Seiji Takeda, Hiroshi Kajino, Renato Cerqueira
Pre-trained Language Models have emerged as promising tools for predicting molecular properties, yet their development is in its early stages, necessitating further research to enhance their efficacy and address challenges such as generalization and sample efficiency.
no code implementations • 28 Sep 2023 • Akihiro Kishimoto, Hiroshi Kajino, Masataka Hirose, Junta Fuchiwaki, Indra Priyadarsini, Lisa Hamada, Hajime Shinohara, Daiju Nakano, Seiji Takeda
Property prediction plays an important role in material discovery.
no code implementations • 28 Jan 2022 • Hiroshi Kajino, Kohei Miyaguchi, Takayuki Osogami
We are interested in in silico evaluation methodology for molecular optimization methods.
1 code implementation • 30 Jun 2021 • Masataro Asai, Hiroshi Kajino, Alex Fukunaga, Christian Muise
Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck.
1 code implementation • 2 Jun 2021 • Hiroshi Kajino
We develop a differentiable point process, which is the technical highlight of this paper, and apply it to derive the path-wise gradient estimator for SNNs.
no code implementations • ICLR 2019 • Hirono Okamoto, Shohei Ohsawa, Itto Higuchi, Haruka Murakami, Mizuki Sango, Zhenghang Cui, Masahiro Suzuki, Hiroshi Kajino, Yutaka Matsuo
It reformulates the posterior with a natural paring $\langle, \rangle: \mathcal{Z} \times \mathcal{Z}^* \rightarrow \Real$, which can be expanded to uncountable infinite domains such as continuous domains as well as interpolation.
no code implementations • 27 Mar 2019 • Masataro Asai, Hiroshi Kajino
We analyze the problem in Latplan both formally and empirically, and propose "Zero-Suppressed SAE", an enhancement that stabilizes the propositions using the idea of closed-world assumption as a prior for NN optimization.
no code implementations • 12 Sep 2018 • Akifumi Wachi, Hiroshi Kajino, Asim Munawar
This paper presents a learning algorithm called ST-SafeMDP for exploring Markov decision processes (MDPs) that is based on the assumption that the safety features are a priori unknown and time-variant.
no code implementations • 8 Sep 2018 • Hiroshi Kajino
Two fundamental challenges are: (i) it is not trivial to generate valid molecules in a controllable way due to hard chemical constraints such as the valency conditions, and (ii) it is often costly to evaluate a property of a novel molecule, and therefore, the number of property evaluations is limited.
no code implementations • ICLR 2018 • Shohei Ohsawa, Kei Akuzawa, Tatsuya Matsushima, Gustavo Bezerra, Yusuke Iwasawa, Hiroshi Kajino, Seiya Takenaka, Yutaka Matsuo
Existing multi-agent reinforcement learning (MARL) communication methods have relied on a trusted third party (TTP) to distribute reward to agents, leaving them inapplicable in peer-to-peer environments.
no code implementations • ICML 2017 • Takayuki Osogami, Hiroshi Kajino, Taro Sekiyama
Hidden units can play essential roles in modeling time-series having long-term dependency or on-linearity but make it difficult to learn associated parameters.