50 citations · 66 across the 6 of their papers we have counts for
6 papers
Biases in In Silico Evaluation of Molecular Optimization Methods and Bias-Reduced Evaluation Methodology
Hiroshi Kajino, Kohei Miyaguchi, Takayuki Osogami
We are interested in in silico evaluation methodology for molecular optimization methods. Given a sample of molecules and their properties of our interest, we wish not only to trai…
Classical Planning in Deep Latent Space
Masataro Asai, Hiroshi Kajino, Alex Fukunaga +1
Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, alth…
A Differentiable Point Process with Its Application to Spiking Neural Networks
Hiroshi Kajino
This paper is concerned about a learning algorithm for a probabilistic model of spiking neural networks (SNNs). Jimenez Rezende & Gerstner (2014) proposed a stochastic variational…
Towards Stable Symbol Grounding with Zero-Suppressed State AutoEncoder
Masataro Asai, Hiroshi Kajino
While classical planning has been an active branch of AI, its applicability is limited to the tasks precisely modeled by humans. Fully automated high-level agents should be instead…
Safe Exploration in Markov Decision Processes with Time-Variant Safety using Spatio-Temporal Gaussian Process
Akifumi Wachi, Hiroshi Kajino, Asim Munawar
In many real-world applications (e.g., planetary exploration, robot navigation), an autonomous agent must be able to explore a space with guaranteed safety. Most safe exploration a…
Molecular Hypergraph Grammar with its Application to Molecular Optimization
Hiroshi Kajino
Molecular optimization aims to discover novel molecules with desirable properties. Two fundamental challenges are: (i) it is not trivial to generate valid molecules in a controllab…