Search Results for author: Tao Sun

Found 41 papers, 8 papers with code

Safe Self-Refinement for Transformer-based Domain Adaptation

1 code implementation16 Apr 2022 Tao Sun, Cheng Lu, Tianshuo Zhang, Haibin Ling

Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source domain to solve tasks on a related unlabeled target domain.

Transfer Learning Unsupervised Domain Adaptation

Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic Optimization

no code implementations18 Oct 2021 Tao Sun, Huaming Ling, Zuoqiang Shi, Dongsheng Li, Bao Wang

In this paper, to eliminate the effort for tuning the momentum-related hyperparameter, we propose a new adaptive momentum inspired by the optimal choice of the heavy ball momentum for quadratic optimization.

Image Classification Language Modelling +2

AIR-Net: Adaptive and Implicit Regularization Neural Network for Matrix Completion

1 code implementation12 Oct 2021 Zhemin Li, Tao Sun, Hongxia Wang, Bao Wang

The explicit low-rank regularization, e. g., nuclear norm regularization, has been widely used in imaging sciences.

Matrix Completion Missing Elements

Deep Learning Approach Protecting Privacy in Camera-Based Critical Applications

no code implementations4 Oct 2021 Gautham Ramajayam, Tao Sun, Chiu C. Tan, Lannan Luo, Haibin Ling

Many critical applications rely on cameras to capture video footage for analytical purposes.

On the Practicality of Deterministic Epistemic Uncertainty

1 code implementation1 Jul 2021 Janis Postels, Mattia Segu, Tao Sun, Luc van Gool, Fisher Yu, Federico Tombari

A set of novel approaches for estimating epistemic uncertainty in deep neural networks with a single forward pass has recently emerged as a valid alternative to Bayesian Neural Networks.

OOD Detection Semantic Segmentation

Decentralized Federated Averaging

no code implementations23 Apr 2021 Tao Sun, Dongsheng Li, Bao Wang

In FedAvg, clients keep their data locally for privacy protection; a central parameter server is used to communicate between clients.

Stability and Generalization of the Decentralized Stochastic Gradient Descent

no code implementations2 Feb 2021 Tao Sun, Dongsheng Li, Bao Wang

The stability and generalization of stochastic gradient-based methods provide valuable insights into understanding the algorithmic performance of machine learning models.

Inertial Proximal Deep Learning Alternating Minimization for Efficient Neutral Network Training

no code implementations30 Jan 2021 Linbo Qiao, Tao Sun, Hengyue Pan, Dongsheng Li

In recent years, the Deep Learning Alternating Minimization (DLAM), which is actually the alternating minimization applied to the penalty form of the deep neutral networks training, has been developed as an alternative algorithm to overcome several drawbacks of Stochastic Gradient Descent (SGD) algorithms.

Three-quarter Sibling Regression for Denoising Observational Data

no code implementations31 Dec 2020 Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich

However, it will remove intrinsic variability if the variables are dependent, and therefore does not apply to many situations, including modeling of species counts that are controlled by common causes.


Robust Multi-Agent Reinforcement Learning with Model Uncertainty

no code implementations NeurIPS 2020 Kaiqing Zhang, Tao Sun, Yunzhe Tao, Sahika Genc, Sunil Mallya, Tamer Basar

In contrast, we model the problem as a robust Markov game, where the goal of all agents is to find policies such that no agent has the incentive to deviate, i. e., reach some equilibrium point, which is also robust to the possible uncertainty of the MARL model.

Multi-agent Reinforcement Learning Q-Learning +1

REPAINT: Knowledge Transfer in Deep Reinforcement Learning

no code implementations24 Nov 2020 Yunzhe Tao, Sahika Genc, Jonathan Chung, Tao Sun, Sunil Mallya

Accelerating learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low.

reinforcement-learning Transfer Learning

REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement Learning

no code implementations28 Sep 2020 Yunzhe Tao, Sahika Genc, Tao Sun, Sunil Mallya

Accelerating the learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low or unknown.

reinforcement-learning Transfer Learning

End-to-end Full Projector Compensation

1 code implementation30 Jul 2020 Bingyao Huang, Tao Sun, Haibin Ling

Full projector compensation aims to modify a projector input image to compensate for both geometric and photometric disturbance of the projection surface.

FedGAN: Federated Generative Adversarial Networks for Distributed Data

no code implementations12 Jun 2020 Mohammad Rasouli, Tao Sun, Ram Rajagopal

We propose Federated Generative Adversarial Network (FedGAN) for training a GAN across distributed sources of non-independent-and-identically-distributed data sources subject to communication and privacy constraints.

Time Series

Adaptive Temporal Difference Learning with Linear Function Approximation

no code implementations20 Feb 2020 Tao Sun, Han Shen, Tianyi Chen, Dongsheng Li

Typically, the performance of TD(0) and TD($\lambda$) is very sensitive to the choice of stepsizes.

OpenAI Gym reinforcement-learning

Zero-Shot Reinforcement Learning with Deep Attention Convolutional Neural Networks

no code implementations2 Jan 2020 Sahika Genc, Sunil Mallya, Sravan Bodapati, Tao Sun, Yunzhe Tao

Simulation-to-simulation and simulation-to-real world transfer of neural network models have been a difficult problem.

Autonomous Driving Deep Attention +3

General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme

no code implementations NeurIPS 2019 Tao Sun, Yuejiao Sun, Dongsheng Li, Qing Liao

In this paper, we propose a general proximal incremental aggregated gradient algorithm, which contains various existing algorithms including the basic incremental aggregated gradient method.

Decentralized Markov Chain Gradient Descent

no code implementations23 Sep 2019 Tao Sun, Dongsheng Li

Decentralized stochastic gradient method emerges as a promising solution for solving large-scale machine learning problems.

Coexistence under hierarchical resource exploitation: the role of R*-preemption tradeoff

no code implementations22 Aug 2019 Man Qi, Niv DeMalach, Tao Sun, Hailin Zhang

Thus, we developed an extension of resource competition theory to investigate partial and total preemption (in the latter, the preemptor is unaffected by species with lower preemption rank).

Inertial nonconvex alternating minimizations for the image deblurring

no code implementations27 Jul 2019 Tao Sun, Roberto Barrio, Marcos Rodriguez, Hao Jiang

In image processing, Total Variation (TV) regularization models are commonly used to recover blurred images.

Deblurring Image Deblurring +1

Heavy-ball Algorithms Always Escape Saddle Points

no code implementations23 Jul 2019 Tao Sun, Dongsheng Li, Zhe Quan, Hao Jiang, Shengguo Li, Yong Dou

In this paper, we answer a question: can the nonconvex heavy-ball algorithms with random initialization avoid saddle points?

Cloud Storage for Multi-Service Battery Operation (Extended Version)

no code implementations17 May 2019 Mohammad Rasouli, Tao Sun, Camille Pache, Patrick Panciatici, Jean Maeght, Ramesh Johari, Ram Rajagopal

The methodology consists in modelling the problem as a two-stage stochastic optimization between high priority stochastic grid services and low priority cloud storage for stochastic end users.

Stochastic Optimization

phq: a Fortran code to compute phonon quasiparticle properties and dispersions

1 code implementation18 Feb 2019 Zhen Zhang, Dong-Bo Zhang, Tao Sun, Renata Wentzcovitch

We here introduce a Fortran code that computes anharmonic free energy of solids from first-principles based on our phonon quasiparticle approach.

Materials Science

Iteratively reweighted penalty alternating minimization methods with continuation for image deblurring

no code implementations9 Feb 2019 Tao Sun, Dongsheng Li, Hao Jiang, Zhe Quan

In this paper, we consider a class of nonconvex problems with linear constraints appearing frequently in the area of image processing.

Deblurring Image Deblurring

TraceCaps: A Capsule-based Neural Network for Semantic Segmentation

no code implementations ICLR 2019 Tao Sun, Zhewei Wang, C. D. Smith, Jundong Liu

We model this procedure as a traceback pipeline and take it as a central piece to build an end-to-end segmentation network.

Semantic Segmentation

Quality-Aware Multimodal Saliency Detection via Deep Reinforcement Learning

no code implementations27 Nov 2018 Xiao Wang, Tao Sun, Rui Yang, Chenglong Li, Bin Luo, Jin Tang

In this paper, we propose an efficient quality-aware deep neural network to model the weight of data from each domain using deep reinforcement learning (DRL).

Decision Making reinforcement-learning +3

Markov Chain Block Coordinate Descent

no code implementations22 Nov 2018 Tao Sun, Yuejiao Sun, Yangyang Xu, Wotao Yin

random and cyclic selections are either infeasible or very expensive.

Distributed Optimization

Non-ergodic Convergence Analysis of Heavy-Ball Algorithms

no code implementations5 Nov 2018 Tao Sun, Penghang Yin, Dongsheng Li, Chun Huang, Lei Guan, Hao Jiang

For objective functions satisfying a relaxed strongly convex condition, the linear convergence is established under weaker assumptions on the step size and inertial parameter than made in the existing literature.

On Markov Chain Gradient Descent

no code implementations NeurIPS 2018 Tao Sun, Yuejiao Sun, Wotao Yin

This paper studies Markov chain gradient descent, a variant of stochastic gradient descent where the random samples are taken on the trajectory of a Markov chain.

An Efficient ADMM-Based Algorithm to Nonconvex Penalized Support Vector Machines

no code implementations11 Sep 2018 Lei Guan, Linbo Qiao, Dongsheng Li, Tao Sun, Keshi Ge, Xicheng Lu

Support vector machines (SVMs) with sparsity-inducing nonconvex penalties have received considerable attentions for the characteristics of automatic classification and variable selection.

General Classification Variable Selection

LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning

1 code implementation NeurIPS 2018 Tianyi Chen, Georgios B. Giannakis, Tao Sun, Wotao Yin

This paper presents a new class of gradient methods for distributed machine learning that adaptively skip the gradient calculations to learn with reduced communication and computation.

Non-ergodic Complexity of Convex Proximal Inertial Gradient Descents

no code implementations23 Jan 2018 Tao Sun, Linbo Qiao, Dongsheng Li

The non-ergodic O(1/k) rate is proved for proximal inertial gradient descent with constant stepzise when the objective function is coercive.

A convergence framework for inexact nonconvex and nonsmooth algorithms and its applications to several iterations

no code implementations12 Sep 2017 Tao Sun, Hao Jiang, Li-Zhi Cheng, Wei Zhu

In fact, a lot of classical inexact nonconvex and nonsmooth algorithms allow these three conditions.

Iteratively Linearized Reweighted Alternating Direction Method of Multipliers for a Class of Nonconvex Problems

no code implementations1 Sep 2017 Tao Sun, Hao Jiang, Lizhi Cheng, Wei Zhu

The traditional alternating direction method of multipliers encounters troubles in both mathematics and computations in solving the nonconvex and nonsmooth subproblem.

Differentially Private Learning of Graphical Models using CGMs

no code implementations ICML 2017 Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau

A naive learning algorithm that uses the noisy sufficient statistics “as is” outperforms general-purpose differentially private learning algorithms.

Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models

no code implementations14 Jun 2017 Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau

We investigate the problem of learning discrete, undirected graphical models in a differentially private way.

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