Search Results for author: Tao Sun

Found 69 papers, 17 papers with code

RJUA-MedDQA: A Multimodal Benchmark for Medical Document Question Answering and Clinical Reasoning

no code implementations19 Feb 2024 Congyun Jin, Ming Zhang, Xiaowei Ma, Li Yujiao, Yingbo Wang, Yabo Jia, Yuliang Du, Tao Sun, Haowen Wang, Cong Fan, Jinjie Gu, Chenfei Chi, Xiangguo Lv, Fangzhou Li, Wei Xue, Yiran Huang

Recent advancements in Large Language Models (LLMs) and Large Multi-modal Models (LMMs) have shown potential in various medical applications, such as Intelligent Medical Diagnosis.

document understanding Medical Diagnosis +1

REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models

no code implementations10 Feb 2024 Yinghao Zhu, Changyu Ren, Shiyun Xie, Shukai Liu, Hangyuan Ji, Zixiang Wang, Tao Sun, Long He, Zhoujun Li, Xi Zhu, Chengwei Pan

Leveraging clinical notes and multivariate time-series EHR, existing models often lack the medical context relevent to clinical tasks, prompting the incorporation of external knowledge, particularly from the knowledge graph (KG).

Language Modelling Large Language Model +1

OrchMoE: Efficient Multi-Adapter Learning with Task-Skill Synergy

no code implementations19 Jan 2024 Haowen Wang, Tao Sun, Kaixiang Ji, Jian Wang, Cong Fan, Jinjie Gu

We advance the field of Parameter-Efficient Fine-Tuning (PEFT) with our novel multi-adapter method, OrchMoE, which capitalizes on modular skill architecture for enhanced forward transfer in neural networks.

Multi-Task Learning

xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning

no code implementations13 Jan 2024 Linzheng Chai, Jian Yang, Tao Sun, Hongcheng Guo, Jiaheng Liu, Bing Wang, Xiannian Liang, Jiaqi Bai, Tongliang Li, Qiyao Peng, Zhoujun Li

To bridge the gap among different languages, we propose a cross-lingual instruction fine-tuning framework (xCOT) to transfer knowledge from high-resource languages to low-resource languages.

Few-Shot Learning Language Modelling +1

Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning

no code implementations6 Dec 2023 Haowen Wang, Tao Sun, Cong Fan, Jinjie Gu

Modular and composable transfer learning is an emerging direction in the field of Parameter Efficient Fine-Tuning, as it enables neural networks to better organize various aspects of knowledge, leading to improved cross-task generalization.

Multi-Task Learning

Nothing Stands Still: A Spatiotemporal Benchmark on 3D Point Cloud Registration Under Large Geometric and Temporal Change

no code implementations15 Nov 2023 Tao Sun, Yan Hao, Shengyu Huang, Silvio Savarese, Konrad Schindler, Marc Pollefeys, Iro Armeni

To this end, we introduce the Nothing Stands Still (NSS) benchmark, which focuses on the spatiotemporal registration of 3D scenes undergoing large spatial and temporal change, ultimately creating one coherent spatiotemporal map.

Point Cloud Registration

Rethinking SIGN Training: Provable Nonconvex Acceleration without First- and Second-Order Gradient Lipschitz

no code implementations23 Oct 2023 Tao Sun, Congliang Chen, Peng Qiao, Li Shen, Xinwang Liu, Dongsheng Li

Sign-based stochastic methods have gained attention due to their ability to achieve robust performance despite using only the sign information for parameter updates.

Stability and Generalization for Minibatch SGD and Local SGD

no code implementations2 Oct 2023 Yunwen Lei, Tao Sun, Mingrui Liu

We show both minibatch and local SGD achieve a linear speedup to attain the optimal risk bounds.

Towards Understanding the Generalizability of Delayed Stochastic Gradient Descent

no code implementations18 Aug 2023 Xiaoge Deng, Li Shen, Shengwei Li, Tao Sun, Dongsheng Li, DaCheng Tao

Stochastic gradient descent (SGD) performed in an asynchronous manner plays a crucial role in training large-scale machine learning models.

MV-ROPE: Multi-view Constraints for Robust Category-level Object Pose and Size Estimation

1 code implementation17 Aug 2023 Jiaqi Yang, Yucong Chen, Xiangting Meng, Chenxin Yan, Min Li, Ran Cheng, Lige Liu, Tao Sun, Laurent Kneip

Additionally, by incorporating constraints on the camera relative pose, we can apply trimming strategies and robust pose averaging on the multi-view object poses, resulting in more accurate and robust estimations of category-level object poses even in the absence of direct depth readings.

Depth Estimation Object

DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction

no code implementations7 Aug 2023 Chengqing Yu, Fei Wang, Zezhi Shao, Tao Sun, Lin Wu, Yongjun Xu

Multivariate time series long-term prediction, which aims to predict the change of data in a long time, can provide references for decision-making.

Decision Making Time Series

Temporal Contrastive Learning for Spiking Neural Networks

no code implementations23 May 2023 Haonan Qiu, Zeyin Song, Yanqi Chen, Munan Ning, Wei Fang, Tao Sun, Zhengyu Ma, Li Yuan, Yonghong Tian

However, in this work, we find the method above is not ideal for the SNNs training as it omits the temporal dynamics of SNNs and degrades the performance quickly with the decrease of inference time steps.

Contrastive Learning

Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropout

no code implementations20 Apr 2023 Tao Sun, Bojian Yin, Sander Bohte

Spiking neural networks (SNNs) have gained attention as models of sparse and event-driven communication of biological neurons, and as such have shown increasing promise for energy-efficient applications in neuromorphic hardware.

Autonomous Vehicles Decision Making +1

NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

1 code implementation10 Apr 2023 Jason Yik, Korneel Van den Berghe, Douwe den Blanken, Younes Bouhadjar, Maxime Fabre, Paul Hueber, Denis Kleyko, Noah Pacik-Nelson, Pao-Sheng Vincent Sun, Guangzhi Tang, Shenqi Wang, Biyan Zhou, Soikat Hasan Ahmed, George Vathakkattil Joseph, Benedetto Leto, Aurora Micheli, Anurag Kumar Mishra, Gregor Lenz, Tao Sun, Zergham Ahmed, Mahmoud Akl, Brian Anderson, Andreas G. Andreou, Chiara Bartolozzi, Arindam Basu, Petrut Bogdan, Sander Bohte, Sonia Buckley, Gert Cauwenberghs, Elisabetta Chicca, Federico Corradi, Guido de Croon, Andreea Danielescu, Anurag Daram, Mike Davies, Yigit Demirag, Jason Eshraghian, Tobias Fischer, Jeremy Forest, Vittorio Fra, Steve Furber, P. Michael Furlong, William Gilpin, Aditya Gilra, Hector A. Gonzalez, Giacomo Indiveri, Siddharth Joshi, Vedant Karia, Lyes Khacef, James C. Knight, Laura Kriener, Rajkumar Kubendran, Dhireesha Kudithipudi, Yao-Hong Liu, Shih-Chii Liu, Haoyuan Ma, Rajit Manohar, Josep Maria Margarit-Taulé, Christian Mayr, Konstantinos Michmizos, Dylan Muir, Emre Neftci, Thomas Nowotny, Fabrizio Ottati, Ayca Ozcelikkale, Priyadarshini Panda, Jongkil Park, Melika Payvand, Christian Pehle, Mihai A. Petrovici, Alessandro Pierro, Christoph Posch, Alpha Renner, Yulia Sandamirskaya, Clemens JS Schaefer, André van Schaik, Johannes Schemmel, Samuel Schmidgall, Catherine Schuman, Jae-sun Seo, Sadique Sheik, Sumit Bam Shrestha, Manolis Sifalakis, Amos Sironi, Matthew Stewart, Kenneth Stewart, Terrence C. Stewart, Philipp Stratmann, Jonathan Timcheck, Nergis Tömen, Gianvito Urgese, Marian Verhelst, Craig M. Vineyard, Bernhard Vogginger, Amirreza Yousefzadeh, Fatima Tuz Zohora, Charlotte Frenkel, Vijay Janapa Reddi

The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.


Mask and Restore: Blind Backdoor Defense at Test Time with Masked Autoencoder

1 code implementation27 Mar 2023 Tao Sun, Lu Pang, Chao Chen, Haibin Ling

It detects possible triggers in the token space using image structural similarity and label consistency between the test image and MAE restorations.

backdoor defense Image Generation +1

Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentation

no code implementations14 Feb 2023 Ye Yue, Marc Baltes, Nidal Abujahar, Tao Sun, Charles D. Smith, Trevor Bihl, Jundong Liu

Over the past decade, artificial neural networks (ANNs) have made tremendous advances, in part due to the increased availability of annotated data.


Backdoor Cleansing with Unlabeled Data

1 code implementation CVPR 2023 Lu Pang, Tao Sun, Haibin Ling, Chao Chen

In experiments, we show that our method, trained without labels, is on-par with state-of-the-art defense methods trained using labels.

Knowledge Distillation

Local Context-Aware Active Domain Adaptation

1 code implementation ICCV 2023 Tao Sun, Cheng Lu, Haibin Ling

In this paper, we propose a Local context-aware ADA framework, named LADA, to address this issue.

Domain Adaptation

Domain Adaptation with Adversarial Training on Penultimate Activations

1 code implementation26 Aug 2022 Tao Sun, Cheng Lu, Haibin Ling

We show that this strategy is more efficient and better correlated with the objective of boosting prediction confidence than adversarial training on input images or intermediate features, as used in previous works.

Unsupervised Domain Adaptation

Adaptive and Implicit Regularization for Matrix Completion

1 code implementation11 Aug 2022 Zhemin Li, Tao Sun, Hongxia Wang, Bao Wang

Theoretically, we show that the adaptive regularization of \ReTwo{AIR} enhances the implicit regularization and vanishes at the end of training.

Matrix Completion

Prior Knowledge Guided Unsupervised Domain Adaptation

1 code implementation18 Jul 2022 Tao Sun, Cheng Lu, Haibin Ling

We propose a general rectification module that uses such prior knowledge to refine model generated pseudo labels.

Unsupervised Domain Adaptation

Safe Self-Refinement for Transformer-based Domain Adaptation

1 code implementation CVPR 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.

BIG-bench Machine Learning Image Classification +3

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

2 code implementations12 Oct 2021 Zhemin Li, Tao Sun, Hongxia Wang, Bao Wang

Theoretically, we show that the adaptive regularization of AIR enhances the implicit regularization and vanishes at the end of training.

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, Luca Sieber, Luc van Gool, Fisher Yu, Federico Tombari

We find that, while DUMs scale to realistic vision tasks and perform well on OOD detection, the practicality of current methods is undermined by poor calibration under distributional shifts.

Out of Distribution (OOD) Detection Semantic Segmentation +1

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.

BIG-bench Machine Learning

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.

Denoising regression

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 +2

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 Reinforcement Learning (RL) +1

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 Reinforcement Learning (RL) +1

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.

Generative Adversarial Network Time Series +1

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 +1

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 +4

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.

Blocking RTE +1

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.

Segmentation 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 object-detection +5

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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