154 citations · 717 across the 63 of their papers we have counts for
7 papers · 1 filter
A Closer Look at Prototype Classifier for Few-shot Image Classification
Mingcheng Hou, Issei Sato
The prototypical network is a prototype classifier based on meta-learning and is widely used for few-shot learning because it classifies unseen examples by constructing class-speci…
Disentanglement Analysis with Partial Information Decomposition
Seiya Tokui, Issei Sato
We propose a framework to analyze how multivariate representations disentangle ground-truth generative factors. A quantitative analysis of disentanglement has been based on metrics…
Loss function based second-order Jensen inequality and its application to particle variational inference
Futoshi Futami, Tomoharu Iwata, Naonori Ueda +2
Bayesian model averaging, obtained as the expectation of a likelihood function by a posterior distribution, has been widely used for prediction, evaluation of uncertainty, and mode…
Toward Neural-Network-Guided Program Synthesis and Verification
Naoki Kobayashi, Taro Sekiyama, Issei Sato +1
We propose a novel framework of program and invariant synthesis called neural network-guided synthesis. We first show that, by suitably designing and training neural networks, we c…
Abelian Neural Networks
Kenshin Abe, Takanori Maehara, Issei Sato
We study the problem of modeling a binary operation that satisfies some algebraic requirements. We first construct a neural network architecture for Abelian group operations and de…
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning
Kento Nozawa, Issei Sato
Instance discriminative self-supervised representation learning has been attracted attention thanks to its unsupervised nature and informative feature representation for downstream…