Search Results for author: Jian Huang

Found 53 papers, 11 papers with code

Convergence of Continuous Normalizing Flows for Learning Probability Distributions

no code implementations31 Mar 2024 Yuan Gao, Jian Huang, Yuling Jiao, Shurong Zheng

We establish non-asymptotic error bounds for the distribution estimator based on CNFs, in terms of the Wasserstein-2 distance.

Image Generation Protein Structure Prediction

Penalized Generative Variable Selection

no code implementations26 Feb 2024 Tong Wang, Jian Huang, Shuangge Ma

Deep networks are increasingly applied to a wide variety of data, including data with high-dimensional predictors.

Variable Selection

DisGNet: A Distance Graph Neural Network for Forward Kinematics Learning of Gough-Stewart Platform

1 code implementation14 Feb 2024 Huizhi Zhu, Wenxia Xu, Jian Huang, Jiaxin Li

As executed on a GPU, our two-stage method can ensure the requirement for real-time computation.

Conditional Stochastic Interpolation for Generative Learning

no code implementations9 Dec 2023 Ding Huang, Jian Huang, Ting Li, Guohao Shen

We propose a conditional stochastic interpolation (CSI) approach to learning conditional distributions.

Image Generation

Gaussian Interpolation Flows

no code implementations20 Nov 2023 Yuan Gao, Jian Huang, Yuling Jiao

Gaussian denoising has emerged as a powerful principle for constructing simulation-free continuous normalizing flows for generative modeling.

Denoising

G10: Enabling An Efficient Unified GPU Memory and Storage Architecture with Smart Tensor Migrations

1 code implementation13 Oct 2023 Haoyang Zhang, Yirui Eric Zhou, Yuqi Xue, Yiqi Liu, Jian Huang

Based on this unified GPU memory and storage architecture, G10 utilizes compiler techniques to characterize the tensor behaviors in deep learning workloads.

Non-Asymptotic Bounds for Adversarial Excess Risk under Misspecified Models

no code implementations2 Sep 2023 Changyu Liu, Yuling Jiao, Junhui Wang, Jian Huang

For the quadratic loss in nonparametric regression, we show that the adversarial excess risk bound can be improved over those for a general loss.

Adversarial Attack regression

Wasserstein Generative Regression

no code implementations27 Jun 2023 Shanshan Song, Tong Wang, Guohao Shen, Yuanyuan Lin, Jian Huang

Our approach simultaneously estimates a regression function and a conditional generator using a generative learning framework, where a conditional generator is a function that can generate samples from a conditional distribution.

Prediction Intervals regression

Differentiable Neural Networks with RePU Activation: with Applications to Score Estimation and Isotonic Regression

no code implementations1 May 2023 Guohao Shen, Yuling Jiao, Yuanyuan Lin, Jian Huang

We establish error bounds for simultaneously approximating $C^s$ smooth functions and their derivatives using RePU-activated deep neural networks.

regression

Nonparametric Quantile Regression: Non-Crossing Constraints and Conformal Prediction

no code implementations18 Oct 2022 Wenlu Tang, Guohao Shen, Yuanyuan Lin, Jian Huang

We also derive non-asymptotic upper bounds for the difference of the lengths between the proposed non-crossing conformal prediction interval and the theoretically oracle prediction interval.

Conformal Prediction Prediction Intervals +1

Deep Sufficient Representation Learning via Mutual Information

no code implementations21 Jul 2022 Siming Zheng, Yuanyuan Lin, Jian Huang

We propose a mutual information-based sufficient representation learning (MSRL) approach, which uses the variational formulation of the mutual information and leverages the approximation power of deep neural networks.

Dimensionality Reduction Representation Learning

Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks

no code implementations21 Jul 2022 Guohao Shen, Yuling Jiao, Yuanyuan Lin, Joel L. Horowitz, Jian Huang

We propose a penalized nonparametric approach to estimating the quantile regression process (QRP) in a nonseparable model using rectifier quadratic unit (ReQU) activated deep neural networks and introduce a novel penalty function to enforce non-crossing of quantile regression curves.

regression

Learning Video Representations of Human Motion From Synthetic Data

no code implementations CVPR 2022 Xi Guo, Wei Wu, Dongliang Wang, Jing Su, Haisheng Su, Weihao Gan, Jian Huang, Qin Yang

In this paper, we take an early step towards video representation learning of human actions with the help of largescale synthetic videos, particularly for human motion representation enhancement.

Action Recognition Contrastive Learning +2

Wasserstein Generative Learning of Conditional Distribution

1 code implementation19 Dec 2021 Shiao Liu, Xingyu Zhou, Yuling Jiao, Jian Huang

The proposed approach uses a conditional generator to transform a known distribution to the target conditional distribution.

Density Estimation Image Generation +2

Non-Asymptotic Error Bounds for Bidirectional GANs

no code implementations NeurIPS 2021 Shiao Liu, Yunfei Yang, Jian Huang, Yuling Jiao, Yang Wang

Our results are also applicable to the Wasserstein bidirectional GAN if the target distribution is assumed to have a bounded support.

A Learning-based Approach Towards Automated Tuning of SSD Configurations

no code implementations17 Oct 2021 Daixuan Li, Jian Huang

Thanks to the mature manufacturing techniques, solid-state drives (SSDs) are highly customizable for applications today, which brings opportunities to further improve their storage performance and resource utilization.

Relative Entropy Gradient Sampler for Unnormalized Distributions

no code implementations6 Oct 2021 Xingdong Feng, Yuan Gao, Jian Huang, Yuling Jiao, Xu Liu

We propose a relative entropy gradient sampler (REGS) for sampling from unnormalized distributions.

An error analysis of generative adversarial networks for learning distributions

no code implementations27 May 2021 Jian Huang, Yuling Jiao, Zhen Li, Shiao Liu, Yang Wang, Yunfei Yang

This paper studies how well generative adversarial networks (GANs) learn probability distributions from finite samples.

Non-asymptotic Excess Risk Bounds for Classification with Deep Convolutional Neural Networks

no code implementations1 May 2021 Guohao Shen, Yuling Jiao, Yuanyuan Lin, Jian Huang

To establish these results, we derive an upper bound for the covering number for the class of general convolutional neural networks with a bias term in each convolutional layer, and derive new results on the approximation power of CNNs for any uniformly-continuous target functions.

Binary Classification

Dynamic sensitivity of quantum Rabi model with quantum criticality

no code implementations5 Jan 2021 Ying Hu, Jian Huang, Jin-Feng Huang, Qiong-Tao Xie, Jie-Qiao Liao

We study the dynamic sensitivity of the quantum Rabi model, which exhibits quantum criticality in the finite-component-system case.

Quantum Physics

Toward Understanding Supervised Representation Learning with RKHS and GAN

no code implementations1 Jan 2021 Xu Liao, Jin Liu, Tianwen Wen, Yuling Jiao, Jian Huang

At the population level, we formulate the ideal representation learning task as that of finding a nonlinear map that minimizes the sum of losses characterizing conditional independence (with RKHS) and disentanglement (with GAN).

Disentanglement Image Classification

Sufficient and Disentangled Representation Learning

no code implementations1 Jan 2021 Jian Huang, Yuling Jiao, Xu Liao, Jin Liu, Zhou Yu

We provide strong statistical guarantees for the learned representation by establishing an upper bound on the excess error of the objective function and show that it reaches the nonparametric minimax rate under mild conditions.

Disentanglement

Quantum simulation of a three-mode optomechanical system based on the Fredkin-type interaction

no code implementations17 Dec 2020 Jin Liu, Yue-Hui Zhou, Jian Huang, Jin-Feng Huang, Jie-Qiao Liao

The realization of multimode optomechanical interactions in the single-photon strong-coupling regime is a desired task in cavity optomechanics, but it remains a challenge in realistic physical systems.

Quantum Physics

Generative Learning With Euler Particle Transport

no code implementations11 Dec 2020 Yuan Gao, Jian Huang, Yuling Jiao, Jin Liu, Xiliang Lu, Zhijian Yang

The key task in training is the estimation of the density ratios or differences that determine the residual maps.

Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline

1 code implementation3 Jul 2020 Dongrui Wu, Xue Jiang, Ruimin Peng, Wanzeng Kong, Jian Huang, Zhigang Zeng

Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promising performance.

Classification EEG +4

Deep Dimension Reduction for Supervised Representation Learning

1 code implementation10 Jun 2020 Jian Huang, Yuling Jiao, Xu Liao, Jin Liu, Zhou Yu

We propose a deep dimension reduction approach to learning representations with these characteristics.

Dimensionality Reduction Disentanglement

Efficient Use of heuristics for accelerating XCS-based Policy Learning in Markov Games

no code implementations26 May 2020 Hao Chen, Chang Wang, Jian Huang, Jianxing Gong

Besides, taking advantages of the condition representation and matching mechanism of XCS, the heuristic policies and the opponent model can provide guidance for situations with similar feature representation.

Reinforcement Learning (RL)

BoostTree and BoostForest for Ensemble Learning

1 code implementation21 Mar 2020 Changming Zhao, Dongrui Wu, Jian Huang, Ye Yuan, Hai-Tao Zhang, Ruimin Peng, Zhenhua Shi

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance.

Ensemble Learning General Classification +1

Learning Implicit Generative Models with Theoretical Guarantees

no code implementations7 Feb 2020 Yuan Gao, Jian Huang, Yuling Jiao, Jin Liu

We then solve the McKean-Vlasov equation numerically using the forward Euler iteration, where the forward Euler map depends on the density ratio (density difference) between the distribution at current iteration and the underlying target distribution.

On Newton Screening

no code implementations27 Jan 2020 Jian Huang, Yuling Jiao, Lican Kang, Jin Liu, Yanyan Liu, Xiliang Lu, Yuanyuan Yang

Based on this KKT system, a built-in working set with a relatively small size is first determined using the sum of primal and dual variables generated from the previous iteration, then the primal variable is updated by solving a least-squares problem on the working set and the dual variable updated based on a closed-form expression.

Sparse Learning

A Support Detection and Root Finding Approach for Learning High-dimensional Generalized Linear Models

no code implementations16 Jan 2020 Jian Huang, Yuling Jiao, Lican Kang, Jin Liu, Yanyan Liu, Xiliang Lu

Feature selection is important for modeling high-dimensional data, where the number of variables can be much larger than the sample size.

feature selection

Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality Reduction

1 code implementation9 Jan 2020 Zhenhua Shi, Dongrui Wu, Jian Huang, Yu-Kai Wang, Chin-Teng Lin

Approaches that preserve only the local data structure, such as locality preserving projections, are usually unsupervised (and hence cannot use label information) and uses a fixed similarity graph.

Clustering General Classification +1

Domain adversarial learning for emotion recognition

no code implementations24 Oct 2019 Zheng Lian, Jian-Hua Tao, Bin Liu, Jian Huang

The secondary task is to learn a common representation where speaker identities can not be distinguished.

Emotion Recognition

Conversational Emotion Analysis via Attention Mechanisms

no code implementations24 Oct 2019 Zheng Lian, Jian-Hua Tao, Bin Liu, Jian Huang

Different from the emotion recognition in individual utterances, we propose a multimodal learning framework using relation and dependencies among the utterances for conversational emotion analysis.

Emotion Recognition

Speech Emotion Recognition via Contrastive Loss under Siamese Networks

no code implementations23 Oct 2019 Zheng Lian, Ya Li, Jian-Hua Tao, Jian Huang

It outperforms the baseline system that is optimized without the contrastive loss function with 1. 14% and 2. 55% in the weighted accuracy and the unweighted accuracy, respectively.

feature selection Speech Emotion Recognition

Expression Analysis Based on Face Regions in Read-world Conditions

no code implementations23 Oct 2019 Zheng Lian, Ya Li, Jian-Hua Tao, Jian Huang, Ming-Yue Niu

To sum up, the contributions of this paper lie in two areas: 1) We visualize concerned areas of human faces in emotion recognition; 2) We analyze the contribution of different face areas to different emotions in real-world conditions through experimental analysis.

Facial Emotion Recognition Facial Expression Recognition +1

Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization

1 code implementation1 Aug 2019 Yuqi Cui, Jian Huang, Dongrui Wu

Takagi-Sugeno-Kang (TSK) fuzzy systems are flexible and interpretable machine learning models; however, they may not be easily optimized when the data size is large, and/or the data dimensionality is high.

General Classification Interpretable Machine Learning

SNAP: A semismooth Newton algorithm for pathwise optimization with optimal local convergence rate and oracle properties

no code implementations9 Oct 2018 Jian Huang, Yuling Jiao, Xiliang Lu, Yueyong Shi, Qinglong Yang

We propose a semismooth Newton algorithm for pathwise optimization (SNAP) for the LASSO and Enet in sparse, high-dimensional linear regression.

regression

Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression

no code implementations8 Aug 2018 Dongrui Wu, Jian Huang

Acquisition of labeled training samples for affective computing is usually costly and time-consuming, as affects are intrinsically subjective, subtle and uncertain, and hence multiple human assessors are needed to evaluate each affective sample.

Active Learning regression

Active Learning for Regression Using Greedy Sampling

1 code implementation8 Aug 2018 Dongrui Wu, Chin-Teng Lin, Jian Huang

Active learning for regression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection.

Active Learning EEG +1

Photo-Guided Exploration of Volume Data Features

no code implementations18 Oct 2017 Mohammad Raji, Alok Hota, Robert Sisneros, Peter Messmer, Jian Huang

In this work, we pose the question of whether, by considering qualitative information such as a sample target image as input, one can produce a rendered image of scientific data that is similar to the target.

Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized Huber Loss Regression and Quantile Regression

no code implementations9 Sep 2015 Congrui Yi, Jian Huang

We propose an algorithm, semismooth Newton coordinate descent (SNCD), for the elastic-net penalized Huber loss regression and quantile regression in high dimensional settings.

regression

A Unified Primal Dual Active Set Algorithm for Nonconvex Sparse Recovery

no code implementations4 Oct 2013 Jian Huang, Yuling Jiao, Bangti Jin, Jin Liu, Xiliang Lu, Can Yang

In this paper, we consider the problem of recovering a sparse signal based on penalized least squares formulations.

SCAD-penalized regression in high-dimensional partially linear models

no code implementations31 Mar 2009 Huiliang Xie, Jian Huang

We consider the problem of simultaneous variable selection and estimation in partially linear models with a divergent number of covariates in the linear part, under the assumption that the vector of regression coefficients is sparse.

Statistics Theory Statistics Theory 62J05, 62G08 (Primary) 62E20 (Secondary)

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