b'Issei Sato'

research

∙ 06/02/2022

Excess risk analysis for epistemic uncertainty with application to variational inference

We analyze the epistemic uncertainty (EU) of supervised learning in Baye...

Futoshi Futami, et al. ∙

research

∙ 05/15/2022

Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective

The lottery ticket hypothesis (LTH) has attracted attention because it c...

Keitaro Sakamoto, et al. ∙

research

∙ 04/29/2022

Goldilocks-curriculum Domain Randomization and Fractal Perlin Noise with Application to Sim2Real Pneumonia Lesion Detection

A computer-aided detection (CAD) system based on machine learning is exp...

Takahiro Suzuki, et al. ∙

research

∙ 04/18/2022

Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey

Representation learning enables us to automatically extract generic feat...

Kento Nozawa, et al. ∙

research

∙ 04/11/2022

Neural Lagrangian Schrödinger bridge

Population dynamics is the study of temporal and spatial variation in th...

Takeshi Koshizuka, et al. ∙

research

∙ 10/11/2021

A Closer Look at Prototype Classifier for Few-shot Image Classification

The prototypical network is a prototype classifier based on meta-learnin...

Mingcheng Hou, et al. ∙

research

∙ 08/31/2021

Disentanglement Analysis with Partial Information Decomposition

Given data generated from multiple factors of variation that cooperative...

Seiya Tokui, et al. ∙

research

∙ 06/09/2021

Loss function based second-order Jensen inequality and its application to particle variational inference

Bayesian model averaging, obtained as the expectation of a likelihood fu...

Futoshi Futami, et al. ∙

research

∙ 03/17/2021

Toward Neural-Network-Guided Program Synthesis and Verification

We propose a novel framework of program and invariant synthesis called n...

Naoki Kobayashi, et al. ∙

research

∙ 02/24/2021

Abelian Neural Networks

We study the problem of modeling a binary operation that satisfies some ...

Kenshin Abe, et al. ∙

research

∙ 02/13/2021

Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning

Instance discriminative self-supervised representation learning has been...

Kento Nozawa, et al. ∙

research

∙ 02/01/2021

Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification

To cope with high annotation costs, training a classifier only from weak...

Shida Lei, et al. ∙

research

∙ 11/23/2020

Stable Weight Decay Regularization

Weight decay is a popular regularization technique for training of deep ...

Zeke Xie, et al. ∙

research

∙ 11/12/2020

Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting

Deep learning is often criticized by two serious issues which rarely exi...

Zeke Xie, et al. ∙

research

∙ 08/03/2020

Classification from Ambiguity Comparisons

Labeling data is an unavoidable pre-processing procedure for most machin...

Zhenghang Cui, et al. ∙

research

∙ 07/03/2020

Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain

Label disagreement between human experts is a common issue in the medica...

Takahiro Mimori, et al. ∙

research

∙ 06/29/2020

Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia

Adaptive Momentum Estimation (Adam), which combines Adaptive Learning Ra...

Zeke Xie, et al. ∙

research

∙ 06/15/2020

LFD-ProtoNet: Prototypical Network Based on Local Fisher Discriminant Analysis for Few-shot Learning

The prototypical network (ProtoNet) is a few-shot learning framework tha...

Kei Mukaiyama, et al. ∙

research

∙ 06/13/2020

γ-ABC: Outlier-Robust Approximate Bayesian Computation based on Robust Divergence Estimator

Making a reliable inference in complex models is an essential issue in s...

Masahiro Fujisawa, et al. ∙

research

∙ 06/11/2020

Similarity-based Classification: Connecting Similarity Learning to Binary Classification

In real-world classification problems, pairwise supervision (i.e., a pai...

Han Bao, et al. ∙

research

∙ 05/08/2020

Sequential Gallery for Interactive Visual Design Optimization

Visual design tasks often involve tuning many design parameters. For exa...

Yuki Koyama, et al. ∙

research

∙ 03/10/2020

Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time

The Gaussian process bandit is a problem in which we want to find a maxi...

Hideaki Imamura, et al. ∙

research

∙ 02/10/2020

Few-shot Domain Adaptation by Causal Mechanism Transfer

We study few-shot supervised domain adaptation (DA) for regression probl...

Takeshi Teshima, et al. ∙

research

∙ 02/10/2020

A Diffusion Theory for Deep Learning Dynamics: Stochastic Gradient Descent Escapes From Sharp Minima Exponentially Fast

Stochastic optimization algorithms, such as Stochastic Gradient Descent ...

Zeke Xie, et al. ∙

research

∙ 11/20/2019

Bayesian interpretation of SGD as Ito process

The current interpretation of stochastic gradient descent (SGD) as a sto...

Soma Yokoi, et al. ∙

research

∙ 07/24/2019

Classification from Triplet Comparison Data

Learning from triplet comparison data has been extensively studied in th...

Zhenghang Cui, et al. ∙

research

∙ 06/24/2019

Interactive Subspace Exploration on Generative Image Modelling

Generative image modeling techniques such as GAN demonstrate highly conv...

Toby Chong Long Hin, et al. ∙

research

∙ 05/28/2019

Solving NP-Hard Problems on Graphs by Reinforcement Learning without Domain Knowledge

We propose an algorithm based on reinforcement learning for solving NP-h...

Kenshin Abe, et al. ∙

research

∙ 05/02/2019

Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping

Deep neural networks (DNNs) have a high accuracy on image classification...

Xi Yang, et al. ∙

research

∙ 04/26/2019

Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization

Pairwise similarities and dissimilarities between data points might be e...

Takuya Shimada, et al. ∙

research

∙ 03/22/2019

Use of Ghost Cytometry to Differentiate Cells with Similar Gross Morphologic Characteristics

Imaging flow cytometry shows significant potential for increasing our un...

Hiroaki Adachi, et al. ∙

research

∙ 03/14/2019

On Learning from Ghost Imaging without Imaging

Computational ghost imaging is an imaging technique with which an object...

Issei Sato, et al. ∙

research

∙ 03/07/2019

On Transformations in Stochastic Gradient MCMC

Stochastic gradient Langevin dynamics (SGLD) is a widely used sampler fo...

Soma Yokoi, et al. ∙

research

∙ 02/12/2019

PAC-Bayes Analysis of Sentence Representation

Learning sentence vectors from an unlabeled corpus has attracted attenti...

Kento Nozawa, et al. ∙

research

∙ 02/04/2019

Online Multiclass Classification Based on Prediction Margin for Partial Feedback

We consider the problem of online multiclass classification with partial...

Takuo Kaneko, et al. ∙

research

∙ 02/01/2019

Multi-level Monte Carlo Variational Inference

In many statistics and machine learning frameworks, stochastic optimizat...

Masahiro Fujisawa, et al. ∙

research

∙ 01/31/2019

Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization

We consider the semi-supervised ordinal regression problem, where unlabe...

Taira Tsuchiya, et al. ∙

research

∙ 01/15/2019

Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks using PAC-Bayesian Analysis

The notion of flat minima has played a key role in the generalization pr...

Yusuke Tsuzuku, et al. ∙

research

∙ 09/13/2018

Clipped Matrix Completion: a Remedy for Ceiling Effects

We consider the recovery of a low-rank matrix from its clipped observati...

Takeshi Teshima, et al. ∙

research

∙ 09/11/2018

On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions

Data-agnostic quasi-imperceptible perturbations on inputs can severely d...

Yusuke Tsuzuku, et al. ∙

research

∙ 09/11/2018

Unsupervised Domain Adaptation Based on Source-guided Discrepancy

Unsupervised domain adaptation is the problem setting where data generat...

Seiichi Kuroki, et al. ∙

research

∙ 05/21/2018

Frank-Wolfe Stein Sampling

In Bayesian inference, the posterior distributions are difficult to obta...

Futoshi Futami, et al. ∙

research

∙ 03/12/2018

Variational Inference for Gaussian Process with Panel Count Data

We present the first framework for Gaussian-process-modulated Poisson pr...

Hongyi Ding, et al. ∙

research

∙ 02/13/2018

Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model

While crowdsourcing has become an important means to label data, crowdwo...

Hideaki Imamura, et al. ∙

research

∙ 02/12/2018

Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks

High sensitivity of neural networks against malicious perturbations on i...

Yusuke Tsuzuku, et al. ∙

research

∙ 02/12/2018

Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer

Learning using privileged information is an attractive problem setting t...

Ryosuke Kamesawa, et al. ∙

research

∙ 10/18/2017

Variational Inference based on Robust Divergences

Robustness to outliers is a central issue in real-world machine learning...

Futoshi Futami, et al. ∙

research

∙ 09/26/2017

On the Model Shrinkage Effect of Gamma Process Edge Partition Models

The edge partition model (EPM) is a fundamental Bayesian nonparametric m...

Iku Ohama, et al. ∙

research

∙ 05/25/2017

Expectation Propagation for t-Exponential Family Using Q-Algebra

Exponential family distributions are highly useful in machine learning s...

Futoshi Futami, et al. ∙

research

∙ 05/19/2017

Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling

Analyzing the underlying structure of multiple time-sequences provides i...

Hongyi Ding, et al. ∙

Issei Sato

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