Search Results for author: Han Bao

Found 21 papers, 4 papers with code

Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data

no code implementations30 Sep 2022 Yuki Takezawa, Han Bao, Kenta Niwa, Ryoma Sato, Makoto Yamada

In this study, we propose Momentum Tracking, which is a method with momentum acceleration whose convergence rate is proven to be independent of data heterogeneity.

BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal Stereo

1 code implementation21 Sep 2022 Yinhao Li, Han Bao, Zheng Ge, Jinrong Yang, Jianjian Sun, Zeming Li

To this end, we introduce an effective temporal stereo method to dynamically select the scale of matching candidates, enable to significantly reduce computation overhead.

3D Object Detection Depth Estimation +1

Approximating 1-Wasserstein Distance with Trees

no code implementations24 Jun 2022 Makoto Yamada, Yuki Takezawa, Ryoma Sato, Han Bao, Zornitsa Kozareva, Sujith Ravi

In this paper, we aim to approximate the 1-Wasserstein distance by the tree-Wasserstein distance (TWD), where TWD is a 1-Wasserstein distance with tree-based embedding and can be computed in linear time with respect to the number of nodes on a tree.

On the Surrogate Gap between Contrastive and Supervised Losses

1 code implementation6 Oct 2021 Han Bao, Yoshihiro Nagano, Kento Nozawa

Recent theoretical studies have attempted to explain the benefit of the large negative sample size by upper-bounding the downstream classification loss with the contrastive loss.

Classification Data Augmentation +1

Retrodiction beyond the Heisenberg uncertainty relation

no code implementations26 Sep 2020 Han Bao, Shenchao Jin, Junlei Duan, Suotang Jia, Klaus Mølmer, Heng Shen, Yanhong Xiao

In quantum mechanics, the Heisenberg uncertainty relation presents an ultimate limit to the precision by which one can predict the outcome of position and momentum measurements on a particle.

Quantum Physics

Pairwise Supervision Can Provably Elicit a Decision Boundary

no code implementations11 Jun 2020 Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama

A classifier built upon the representations is expected to perform well in downstream classification; however, little theory has been given in literature so far and thereby the relationship between similarity and classification has remained elusive.

Classification Contrastive Learning +3

Calibrated Surrogate Losses for Adversarially Robust Classification

no code implementations28 May 2020 Han Bao, Clayton Scott, Masashi Sugiyama

Adversarially robust classification seeks a classifier that is insensitive to adversarial perturbations of test patterns.

Classification General Classification +1

Deep Learning Interfacial Momentum Closures in Coarse-Mesh CFD Two-Phase Flow Simulation Using Validation Data

no code implementations7 May 2020 Han Bao, Jinyong Feng, Nam Dinh, Hongbin Zhang

Development of those closures traditionally rely on the experimental data and analytical derivation with simplified assumptions that usually cannot deliver a universal solution across a wide range of flow conditions.

Learning from Noisy Similar and Dissimilar Data

no code implementations3 Feb 2020 Soham Dan, Han Bao, Masashi Sugiyama

We perform a detailed investigation of this problem under two realistic noise models and propose two algorithms to learn from noisy S-D data.

Using Deep Learning to Explore Local Physical Similarity for Global-scale Bridging in Thermal-hydraulic Simulation

no code implementations6 Jan 2020 Han Bao, Nam Dinh, Linyu Lin, Robert Youngblood, Jeffrey Lane, Hongbin Zhang

Current system thermal-hydraulic codes have limited credibility in simulating real plant conditions, especially when the geometry and boundary conditions are extrapolated beyond the range of test facilities.

Investigations of the Influences of a CNN's Receptive Field on Segmentation of Subnuclei of Bilateral Amygdalae

no code implementations7 Nov 2019 Han Bao

Segmentation of objects with various sizes is relatively less explored in medical imaging, and has been very challenging in computer vision tasks in general.

Computationally Efficient CFD Prediction of Bubbly Flow using Physics-Guided Deep Learning

no code implementations17 Oct 2019 Han Bao, Jinyong Feng, Nam Dinh, Hongbin Zhang

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach.

Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification

no code implementations29 May 2019 Han Bao, Masashi Sugiyama

A clue to tackle their direct optimization is a calibrated surrogate utility, which is a tractable lower bound of the true utility function representing a given metric.

Classification General Classification +3

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

no code implementations26 Apr 2019 Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama

In this paper, we derive an unbiased risk estimator which can handle all of similarities/dissimilarities and unlabeled data.

General Classification

Unsupervised Domain Adaptation Based on Source-guided Discrepancy

no code implementations11 Sep 2018 Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama

A previously proposed discrepancy that does not use the source domain labels requires high computational cost to estimate and may lead to a loose generalization error bound in the target domain.

Unsupervised Domain Adaptation

Classification from Pairwise Similarity and Unlabeled Data

2 code implementations ICML 2018 Han Bao, Gang Niu, Masashi Sugiyama

Supervised learning needs a huge amount of labeled data, which can be a big bottleneck under the situation where there is a privacy concern or labeling cost is high.

Classification General Classification

Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags

1 code implementation22 Apr 2017 Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama

Multiple instance learning (MIL) is a variation of traditional supervised learning problems where data (referred to as bags) are composed of sub-elements (referred to as instances) and only bag labels are available.

Content-Based Image Retrieval Multiple Instance Learning +1

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