33 citations · 76 across the 10 of their papers we have counts for
5 papers · 1 filter
Meta Learning as Bayes Risk Minimization
Shin-ichi Maeda, Toshiki Nakanishi, Masanori Koyama
Meta-Learning is a family of methods that use a set of interrelated tasks to learn a model that can quickly learn a new query task from a possibly small contextual dataset. In this…
Robustness to Adversarial Perturbations in Learning from Incomplete Data
Amir Najafi, Shin-ichi Maeda, Masanori Koyama +1
What is the role of unlabeled data in an inference problem, when the presumed underlying distribution is adversarially perturbed? To provide a concrete answer to this question, thi…
Neural Multi-scale Image Compression
Ken Nakanishi, Shin-ichi Maeda, Takeru Miyato +1
This study presents a new lossy image compression method that utilizes the multi-scale features of natural images. Our model consists of two networks: multi-scale lossy autoencoder…
Semi-supervised learning of hierarchical representations of molecules using neural message passing
Hai Nguyen, Shin-ichi Maeda, Kenta Oono
With the rapid increase of compound databases available in medicinal and material science, there is a growing need for learning representations of molecules in a semi-supervised ma…
Neural Sequence Model Training via -divergence Minimization
Sotetsu Koyamada, Yuta Kikuchi, Atsunori Kanemura +2
We propose a new neural sequence model training method in which the objective function is defined by -divergence. We demonstrate that the objective function generalizes the maxi…