Kyoto University

research

∙ 01/22/2020

Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift

This paper proposes a novel approach for unsupervised domain adaptation ...

Ryuhei Takahashi, et al. ∙

research

∙ 03/09/2020

When Person Re-identification Meets Changing Clothes

Person re-identification (Reid) is now an active research topic for AI-b...

Fangbin Wan, et al. ∙

research

∙ 08/30/2019

Transformer Dissection: An Unified Understanding for Transformer's Attention via the Lens of Kernel

Transformer is a powerful architecture that achieves superior performanc...

Yao-Hung Hubert Tsai, et al. ∙

research

∙ 11/21/2019

Automatically Neutralizing Subjective Bias in Text

Texts like news, encyclopedias, and some social media strive for objecti...

Reid Pryzant, et al. ∙

research

∙ 01/17/2020

GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks

Graph structured data has wide applicability in various domains such as ...

Qiang Huang, et al. ∙

research

∙ 08/14/2021

MatSat: a matrix-based differentiable SAT solver

We propose a new approach to SAT solving which solves SAT problems in ve...

Taisuke Sato, et al. ∙

research

∙ 12/17/2021

Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networks

The distributed inference (DI) framework has gained traction as a techni...

Sohei Itahara, et al. ∙

research

∙ 10/02/2018

An Entropic Optimal Transport Loss for Learning Deep Neural Networks under Label Noise in Remote Sensing Images

Deep neural networks have established as a powerful tool for large scale...

Bharath Bhushan Damodaran, et al. ∙

research

∙ 06/08/2021

Learning subtree pattern importance for Weisfeiler-Lehmanbased graph kernels

Graph is an usual representation of relational data, which are ubiquitou...

Dai Hai Nguyen, et al. ∙

research

∙ 02/26/2019

Topological Bayesian Optimization with Persistence Diagrams

Finding an optimal parameter of a black-box function is important for se...

Tatsuya Shiraishi, et al. ∙

research

∙ 03/22/2019

Unsupervised Speech Enhancement Based on Multichannel NMF-Informed Beamforming for Noise-Robust Automatic Speech Recognition

This paper describes multichannel speech enhancement for improving autom...

Kazuki Shimada, et al. ∙

research

∙ 02/26/2019

Learning to Find Hard Instances of Graph Problems

Finding hard instances, which need a long time to solve, of graph proble...

Ryoma Sato, et al. ∙

research

∙ 02/28/2020

Universal consistency of the k-NN rule in metric spaces and Nagata dimension

The k nearest neighbour learning rule (under the uniform distance tie br...

Benoît Collins, et al. ∙

research

∙ 02/15/2022

OLIVE: Oblivious and Differentially Private Federated Learning on Trusted Execution Environment

Differentially private federated learning (DP-FL) has received increasin...

Fumiyuki Kato, et al. ∙

research

∙ 06/25/2019

Shape from Water Reflection

This paper introduces single-image 3D scene reconstruction from water re...

Ryo Kawahara, et al. ∙

research

∙ 05/02/2020

Stochastic Neighbor Embedding of Multimodal Relational Data for Image-Text Simultaneous Visualization

Multimodal relational data analysis has become of increasing importance ...

Morihiro Mizutani, et al. ∙

research

∙ 07/09/2021

Structured Hammerstein-Wiener Model Learning for Model Predictive Control

This paper aims to improve the reliability of optimal control using mode...

Ryuta Moriyasu, et al. ∙

research

∙ 09/01/2021

Fairness based Multi-Preference Resource Allocation in Decentralised Open Markets

In this work, we focus on resource allocation in a decentralised open ma...

Pankaj Mishra, et al. ∙

research

∙ 04/08/2022

Network Shuffling: Privacy Amplification via Random Walks

Recently, it is shown that shuffling can amplify the central differentia...

Seng Pei Liew, et al. ∙

research

∙ 05/18/2022

Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision

Contrastive pre-training on distant supervision has shown remarkable eff...

Zhen Wan, et al. ∙

research

∙ 04/26/2022

When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?

Word alignment has proven to benefit many-to-many neural machine transla...

Zhuoyuan Mao, et al. ∙

research

∙ 06/09/2022

Globally Optimal Algorithms for Fixed-Budget Best Arm Identification

We consider the fixed-budget best arm identification problem where the g...

Junpei Komiyama, et al. ∙

research

∙ 11/28/2017

Semi-supervised learning of hierarchical representations of molecules using neural message passing

With the rapid increase of compound databases available in medicinal and...

Hai Nguyen, et al. ∙

research

∙ 11/28/2017

Deformation estimation of an elastic object by partial observation using a neural network

Deformation estimation of elastic object assuming an internal organ is i...

Utako Yamamoto, et al. ∙

research

∙ 09/28/2017

Deep Learning Assisted Heuristic Tree Search for the Container Pre-marshalling Problem

One of the key challenges for operations researchers solving real-world ...

André Hottung, et al. ∙

research

∙ 11/07/2017

Large-Scale Optimal Transport and Mapping Estimation

This paper presents a novel two-step approach for the fundamental proble...

Vivien Seguy, et al. ∙

research

∙ 11/16/2017

Efficient Diverse Ensemble for Discriminative Co-Tracking

Ensemble discriminative tracking utilizes a committee of classifiers, to...

Kourosh Meshgi, et al. ∙

research

∙ 09/03/2017

Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario

Item cold-start is a classical issue in recommender systems that affects...

Jill-Jênn Vie, et al. ∙

research

∙ 09/22/2017

Happy Travelers Take Big Pictures: A Psychological Study with Machine Learning and Big Data

In psychology, theory-driven researches are usually conducted with exten...

Xuefeng Liang, et al. ∙

research

∙ 01/29/2017

Rhythm Transcription of Polyphonic Piano Music Based on Merged-Output HMM for Multiple Voices

In a recent conference paper, we have reported a rhythm transcription me...

Eita Nakamura, et al. ∙

research

∙ 12/22/2014

A Bayesian encourages dropout

Dropout is one of the key techniques to prevent the learning from overfi...

Shin-ichi Maeda, et al. ∙

research

∙ 05/15/2017

Convex Coupled Matrix and Tensor Completion

We propose a set of convex low rank inducing norms for a coupled matrice...

Kishan Wimalawarne, et al. ∙

research

∙ 04/21/2012

Paraiso : An Automated Tuning Framework for Explicit Solvers of Partial Differential Equations

We propose Paraiso, a domain specific language embedded in functional pr...

Takayuki Muranushi, et al. ∙

research

∙ 04/02/2017

Efficient Version-Space Reduction for Visual Tracking

Discrminative trackers, employ a classification approach to separate the...

Kourosh Meshgi, et al. ∙

research

∙ 03/31/2017

Efficient Asymmetric Co-Tracking using Uncertainty Sampling

Adaptive tracking-by-detection approaches are popular for tracking arbit...

Kourosh Meshgi, et al. ∙

research

∙ 10/27/2016

GPflow: A Gaussian process library using TensorFlow

GPflow is a Gaussian process library that uses TensorFlow for its core c...

Alexander G. de G. Matthews, et al. ∙

research

∙ 03/08/2017

A Linear Extrinsic Calibration of Kaleidoscopic Imaging System from Single 3D Point

This paper proposes a new extrinsic calibration of kaleidoscopic imaging...

Kosuke Takahashi, et al. ∙

research

∙ 12/09/2016

Panoptic Studio: A Massively Multiview System for Social Interaction Capture

We present an approach to capture the 3D motion of a group of people eng...

Hanbyul Joo, et al. ∙

research

∙ 03/22/2016

Localized Lasso for High-Dimensional Regression

We introduce the localized Lasso, which is suited for learning models th...

Makoto Yamada, et al. ∙

research

∙ 03/06/2014

Collaborative Representation for Classification, Sparse or Non-sparse?

Sparse representation based classification (SRC) has been proved to be a...

Yang Wu, et al. ∙

research

∙ 01/01/2014

Design of a GIS-based Assistant Software Agent for the Incident Commander to Coordinate Emergency Response Operations

Problem: This paper addresses the design of an intelligent software syst...

Reza Nourjou, et al. ∙

research

∙ 11/19/2015

Stochastic gradient method with accelerated stochastic dynamics

In this paper, we propose a novel technique to implement stochastic grad...

Masayuki Ohzeki, et al. ∙

research

∙ 09/08/2015

On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests

Nonparametric two sample or homogeneity testing is a decision theoretic ...

Aaditya Ramdas, et al. ∙

research

∙ 09/03/2015

Bayesian Masking: Sparse Bayesian Estimation with Weaker Shrinkage Bias

A common strategy for sparse linear regression is to introduce regulariz...

Yohei Kondo, et al. ∙

research

∙ 07/07/2015

Wasserstein Training of Boltzmann Machines

The Boltzmann machine provides a useful framework to learn highly comple...

Grégoire Montavon, et al. ∙

research

∙ 07/02/2015

Distributional Smoothing with Virtual Adversarial Training

We propose local distributional smoothness (LDS), a new notion of smooth...

Takeru Miyato, et al. ∙

research

∙ 06/26/2015

Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric

Given a family of probability measures in P(X), the space of probability...

Vivien Seguy, et al. ∙

research

∙ 03/09/2015

Mathematical understanding of detailed balance condition violation and its application to Langevin dynamics

We develop an efficient sampling method by simulating Langevin dynamics ...

M. Ohzeki, et al. ∙

research

∙ 01/31/2015

Deep learning of fMRI big data: a novel approach to subject-transfer decoding

As a technology to read brain states from measurable brain activities, b...

Sotetsu Koyamada, et al. ∙

research

∙ 12/21/2014

Principal Sensitivity Analysis

We present a novel algorithm (Principal Sensitivity Analysis; PSA) to an...

Sotetsu Koyamada, et al. ∙

Kyoto University

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