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Masaru Kitsuregawa

3 accepted papers

2026

Atom-level Adaptive Receptive Fields: A Pruning-Based Encoder for 2D Molecular Graphs (Student Abstract)

AAAI 2026technical

The two-dimensional (2D) graph structure of a molecule encodes abundant latent property information. A well-designed molecular graph encoder can capture informative low-dimensional dense representations of molecules, which can subsequently be applied to a widerange of downstream tasks. To achieve fi

Cited by 0SourcePDFScholar
2024

MapLE: Matching Molecular Analogues Promptly with Low Computational Resources by Multi-Metrics Evaluation (Student Abstract)

AAAI 2024technical

Matching molecular analogues is a computational chemistry and bioinformatics research issue which is used to identify molecules that are structurally or functionally similar to a target molecule. Recent studies on matching analogous molecules have predominantly concentrated on enhancing effectivenes…

Cited by 0SourcePDFScholar
2021

Speculative Sampling in Variational Autoencoders for Dialogue Response Generation

EMNLP 2021finding

Variational autoencoders have been studied as a promising approach to model one-to-many mappings from context to response in chat response generation. However, they often fail to learn proper mappings. One of the reasons for this failure is the discrepancy between a response and a latent variable sa…