Search Results for author: Cheng Chen

Found 84 papers, 24 papers with code

MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation

1 code implementation16 Sep 2023 Cheng Chen, Juzheng Miao, Dufan Wu, Zhiling Yan, Sekeun Kim, Jiang Hu, Aoxiao Zhong, Zhengliang Liu, Lichao Sun, Xiang Li, Tianming Liu, Pheng-Ann Heng, Quanzheng Li

The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation tasks.

Image Segmentation Medical Image Segmentation +3

Selection of contributing factors for predicting landslide susceptibility using machine learning and deep learning models

no code implementations12 Sep 2023 Cheng Chen, Lei Fan

In this study, the impact of the selection of contributing factors on the accuracy of landslide susceptibility predictions using ML and DL models was investigated.

Treatment Outcome Prediction for Intracerebral Hemorrhage via Generative Prognostic Model with Imaging and Tabular Data

1 code implementation24 Jul 2023 Wenao Ma, Cheng Chen, Jill Abrigo, Calvin Hoi-Kwan Mak, Yuqi Gong, Nga Yan Chan, Chu Han, Zaiyi Liu, Qi Dou

Specifically, we propose to employ a variational autoencoder model to generate a low-dimensional prognostic score, which can effectively address the selection bias resulting from the non-randomized controlled trials.

Selection bias

Semantic Contrastive Bootstrapping for Single-positive Multi-label Recognition

1 code implementation15 Jul 2023 Cheng Chen, Yifan Zhao, Jia Li

Learning multi-label image recognition with incomplete annotation is gaining popularity due to its superior performance and significant labor savings when compared to training with fully labeled datasets.

Contrastive Learning Multi-Label Classification

Monte Carlo Policy Gradient Method for Binary Optimization

1 code implementation3 Jul 2023 Cheng Chen, Ruitao Chen, Tianyou Li, Ruichen Ao, Zaiwen Wen

Binary optimization has a wide range of applications in combinatorial optimization problems such as MaxCut, MIMO detection, and MaxSAT.

Combinatorial Optimization Stochastic Optimization

REAL: A Representative Error-Driven Approach for Active Learning

1 code implementation3 Jul 2023 Cheng Chen, Yong Wang, Lizi Liao, Yueguo Chen, Xiaoyong Du

Given a limited labeling budget, active learning (AL) aims to sample the most informative instances from an unlabeled pool to acquire labels for subsequent model training.

Active Learning Informativeness +2

FedMR: Federated Learning via Model Recombination

no code implementations18 May 2023 Ming Hu, Zhihao Yue, Zhiwei Ling, Yihao Huang, Cheng Chen, Xian Wei, Yang Liu, Mingsong Chen

Although Federated Learning (FL) enables global model training across clients without compromising their raw data, existing Federated Averaging (FedAvg)-based methods suffer from the problem of low inference performance, especially for unevenly distributed data among clients.

Federated Learning

Adversary-Aware Partial label learning with Label distillation

no code implementations2 Apr 2023 Cheng Chen, Yueming Lyu, Ivor W. Tsang

However, conventional partial-label learning (PLL) methods are still vulnerable to the high ratio of noisy partial labels, especially in a large labelling space.

Partial Label Learning

HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining

1 code implementation CVPR 2023 Shixiang Tang, Cheng Chen, Qingsong Xie, Meilin Chen, Yizhou Wang, Yuanzheng Ci, Lei Bai, Feng Zhu, Haiyang Yang, Li Yi, Rui Zhao, Wanli Ouyang

Specifically, we propose a \textbf{HumanBench} based on existing datasets to comprehensively evaluate on the common ground the generalization abilities of different pretraining methods on 19 datasets from 6 diverse downstream tasks, including person ReID, pose estimation, human parsing, pedestrian attribute recognition, pedestrian detection, and crowd counting.

Autonomous Driving Crowd Counting +4

Personalized speech enhancement combining band-split RNN and speaker attentive module

no code implementations20 Feb 2023 Xiaohuai Le, Li Chen, Chao He, Yiqing Guo, Cheng Chen, Xianjun Xia, Jing Lu

Target speaker information can be utilized in speech enhancement (SE) models to more effectively extract the desired speech.

Speech Enhancement

Diffusion Model based Semi-supervised Learning on Brain Hemorrhage Images for Efficient Midline Shift Quantification

no code implementations1 Jan 2023 Shizhan Gong, Cheng Chen, Yuqi Gong, Nga Yan Chan, Wenao Ma, Calvin Hoi-Kwan Mak, Jill Abrigo, Qi Dou

Brain midline shift (MLS) is one of the most critical factors to be considered for clinical diagnosis and treatment decision-making for intracranial hemorrhage.

Decision Making Representation Learning

Multi-rate adaptive transform coding for video compression

no code implementations25 Oct 2022 Lyndon R. Duong, Bohan Li, Cheng Chen, Jingning Han

Contemporary lossy image and video coding standards rely on transform coding, the process through which pixels are mapped to an alternative representation to facilitate efficient data compression.

Data Compression Quantization +1

An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts

no code implementations COLING (WNUT) 2022 Xue-Yong Fu, Cheng Chen, Md Tahmid Rahman Laskar, Shashi Bhushan TN, Simon Corston-Oliver

We present a simple yet effective method to train a named entity recognition (NER) model that operates on business telephone conversation transcripts that contain noise due to the nature of spoken conversation and artifacts of automatic speech recognition.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Spatial and Wavelength Division Joint Multiplexing System Design for Visible Light Communications

no code implementations20 Sep 2022 Cheng Chen, Shenjie Huang, Iman Tavakkolnia, Majid Safari, Harald Haas

Instead of providing a new precoder/post-detector design, we investigate the considered joint multiplexing system from a system configuration perspective by tuning system parameters in both spatial and wavelength domains, such as LED positions and optical filter passband.

Online Active Regression

no code implementations13 Jul 2022 Cheng Chen, Yi Li, Yiming Sun

Active regression considers a linear regression problem where the learner receives a large number of data points but can only observe a small number of labels.


Simultaneously Learning Stochastic and Adversarial Bandits under the Position-Based Model

no code implementations12 Jul 2022 Cheng Chen, Canzhe Zhao, Shuai Li

This work studies the OLTR problem in both stochastic and adversarial environments under the position-based model (PBM).


Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation Learning and Retrieval

no code implementations2 Jul 2022 Keyu Wen, Zhenshan Tan, Qingrong Cheng, Cheng Chen, Xiaodong Gu

Concretely, the first module is a weight-sharing transformer that builds on the head of the visual and textual encoders, aiming to semantically align text and image.

Contrastive Learning Cross-Modal Retrieval +5

Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift

1 code implementation2 Jul 2022 Wenao Ma, Cheng Chen, Shuang Zheng, Jing Qin, Huimao Zhang, Qi Dou

In this paper, we propose the first method to tackle label shift for medical image classification, which effectively adapt the model learned from a single training label distribution to arbitrary unknown test label distribution.

Image Classification Medical Diagnosis +2

3D-model ShapeNet Core Classification using Meta-Semantic Learning

1 code implementation28 May 2022 Farid Ghareh Mohammadi, Cheng Chen, Farzan Shenavarmasouleh, M. Hadi Amini, Beshoy Morkos, Hamid R. Arabnia

Understanding 3D point cloud models for learning purposes has become an imperative challenge for real-world identification such as autonomous driving systems.

Autonomous Driving Classification +4

DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images

1 code implementation27 May 2022 Hongzheng Yang, Cheng Chen, Meirui Jiang, Quande Liu, Jianfeng Cao, Pheng Ann Heng, Qi Dou

Based on this estimated discrepancy, a dynamic learning rate adjustment strategy is then developed to achieve a suitable degree of adaptation for each test sample.

Histopathological Image Classification Image Classification +1

Developing a Production System for Purpose of Call Detection in Business Phone Conversations

no code implementations NAACL (ACL) 2022 Elena Khasanova, Pooja Hiranandani, Shayna Gardiner, Cheng Chen, Xue-Yong Fu, Simon Corston-Oliver

In this paper we describe our implementation of a commercial system to detect Purpose of Call statements in English business call transcripts in real time.

UTC: A Unified Transformer with Inter-Task Contrastive Learning for Visual Dialog

no code implementations CVPR 2022 Cheng Chen, Yudong Zhu, Zhenshan Tan, Qingrong Cheng, Xin Jiang, Qun Liu, Xiaodong Gu

In this paper, we propose a contrastive learning-based framework UTC to unify and facilitate both discriminative and generative tasks in visual dialog with a single model.

Contrastive Learning Representation Learning +1

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

1 code implementation22 Apr 2022 Sarthak Pati, Ujjwal Baid, Brandon Edwards, Micah Sheller, Shih-han Wang, G Anthony Reina, Patrick Foley, Alexey Gruzdev, Deepthi Karkada, Christos Davatzikos, Chiharu Sako, Satyam Ghodasara, Michel Bilello, Suyash Mohan, Philipp Vollmuth, Gianluca Brugnara, Chandrakanth J Preetha, Felix Sahm, Klaus Maier-Hein, Maximilian Zenk, Martin Bendszus, Wolfgang Wick, Evan Calabrese, Jeffrey Rudie, Javier Villanueva-Meyer, Soonmee Cha, Madhura Ingalhalikar, Manali Jadhav, Umang Pandey, Jitender Saini, John Garrett, Matthew Larson, Robert Jeraj, Stuart Currie, Russell Frood, Kavi Fatania, Raymond Y Huang, Ken Chang, Carmen Balana, Jaume Capellades, Josep Puig, Johannes Trenkler, Josef Pichler, Georg Necker, Andreas Haunschmidt, Stephan Meckel, Gaurav Shukla, Spencer Liem, Gregory S Alexander, Joseph Lombardo, Joshua D Palmer, Adam E Flanders, Adam P Dicker, Haris I Sair, Craig K Jones, Archana Venkataraman, Meirui Jiang, Tiffany Y So, Cheng Chen, Pheng Ann Heng, Qi Dou, Michal Kozubek, Filip Lux, Jan Michálek, Petr Matula, Miloš Keřkovský, Tereza Kopřivová, Marek Dostál, Václav Vybíhal, Michael A Vogelbaum, J Ross Mitchell, Joaquim Farinhas, Joseph A Maldjian, Chandan Ganesh Bangalore Yogananda, Marco C Pinho, Divya Reddy, James Holcomb, Benjamin C Wagner, Benjamin M Ellingson, Timothy F Cloughesy, Catalina Raymond, Talia Oughourlian, Akifumi Hagiwara, Chencai Wang, Minh-Son To, Sargam Bhardwaj, Chee Chong, Marc Agzarian, Alexandre Xavier Falcão, Samuel B Martins, Bernardo C A Teixeira, Flávia Sprenger, David Menotti, Diego R Lucio, Pamela Lamontagne, Daniel Marcus, Benedikt Wiestler, Florian Kofler, Ivan Ezhov, Marie Metz, Rajan Jain, Matthew Lee, Yvonne W Lui, Richard McKinley, Johannes Slotboom, Piotr Radojewski, Raphael Meier, Roland Wiest, Derrick Murcia, Eric Fu, Rourke Haas, John Thompson, David Ryan Ormond, Chaitra Badve, Andrew E Sloan, Vachan Vadmal, Kristin Waite, Rivka R Colen, Linmin Pei, Murat AK, Ashok Srinivasan, J Rajiv Bapuraj, Arvind Rao, Nicholas Wang, Ota Yoshiaki, Toshio Moritani, Sevcan Turk, Joonsang Lee, Snehal Prabhudesai, Fanny Morón, Jacob Mandel, Konstantinos Kamnitsas, Ben Glocker, Luke V M Dixon, Matthew Williams, Peter Zampakis, Vasileios Panagiotopoulos, Panagiotis Tsiganos, Sotiris Alexiou, Ilias Haliassos, Evangelia I Zacharaki, Konstantinos Moustakas, Christina Kalogeropoulou, Dimitrios M Kardamakis, Yoon Seong Choi, Seung-Koo Lee, Jong Hee Chang, Sung Soo Ahn, Bing Luo, Laila Poisson, Ning Wen, Pallavi Tiwari, Ruchika Verma, Rohan Bareja, Ipsa Yadav, Jonathan Chen, Neeraj Kumar, Marion Smits, Sebastian R van der Voort, Ahmed Alafandi, Fatih Incekara, Maarten MJ Wijnenga, Georgios Kapsas, Renske Gahrmann, Joost W Schouten, Hendrikus J Dubbink, Arnaud JPE Vincent, Martin J van den Bent, Pim J French, Stefan Klein, Yading Yuan, Sonam Sharma, Tzu-Chi Tseng, Saba Adabi, Simone P Niclou, Olivier Keunen, Ann-Christin Hau, Martin Vallières, David Fortin, Martin Lepage, Bennett Landman, Karthik Ramadass, Kaiwen Xu, Silky Chotai, Lola B Chambless, Akshitkumar Mistry, Reid C Thompson, Yuriy Gusev, Krithika Bhuvaneshwar, Anousheh Sayah, Camelia Bencheqroun, Anas Belouali, Subha Madhavan, Thomas C Booth, Alysha Chelliah, Marc Modat, Haris Shuaib, Carmen Dragos, Aly Abayazeed, Kenneth Kolodziej, Michael Hill, Ahmed Abbassy, Shady Gamal, Mahmoud Mekhaimar, Mohamed Qayati, Mauricio Reyes, Ji Eun Park, Jihye Yun, Ho Sung Kim, Abhishek Mahajan, Mark Muzi, Sean Benson, Regina G H Beets-Tan, Jonas Teuwen, Alejandro Herrera-Trujillo, Maria Trujillo, William Escobar, Ana Abello, Jose Bernal, Jhon Gómez, Joseph Choi, Stephen Baek, Yusung Kim, Heba Ismael, Bryan Allen, John M Buatti, Aikaterini Kotrotsou, Hongwei Li, Tobias Weiss, Michael Weller, Andrea Bink, Bertrand Pouymayou, Hassan F Shaykh, Joel Saltz, Prateek Prasanna, Sampurna Shrestha, Kartik M Mani, David Payne, Tahsin Kurc, Enrique Pelaez, Heydy Franco-Maldonado, Francis Loayza, Sebastian Quevedo, Pamela Guevara, Esteban Torche, Cristobal Mendoza, Franco Vera, Elvis Ríos, Eduardo López, Sergio A Velastin, Godwin Ogbole, Dotun Oyekunle, Olubunmi Odafe-Oyibotha, Babatunde Osobu, Mustapha Shu'aibu, Adeleye Dorcas, Mayowa Soneye, Farouk Dako, Amber L Simpson, Mohammad Hamghalam, Jacob J Peoples, Ricky Hu, Anh Tran, Danielle Cutler, Fabio Y Moraes, Michael A Boss, James Gimpel, Deepak Kattil Veettil, Kendall Schmidt, Brian Bialecki, Sailaja Marella, Cynthia Price, Lisa Cimino, Charles Apgar, Prashant Shah, Bjoern Menze, Jill S Barnholtz-Sloan, Jason Martin, Spyridon Bakas

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.

Boundary Detection Federated Learning

Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging

1 code implementation Findings (ACL) 2022 Yutai Hou, Cheng Chen, Xianzhen Luo, Bohan Li, Wanxiang Che

Such inverse prompting only requires a one-turn prediction for each slot type and greatly speeds up the prediction.

Few-Shot Learning

Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation

1 code implementation29 Mar 2022 Yueming Jin, Yang Yu, Cheng Chen, Zixu Zhao, Pheng-Ann Heng, Danail Stoyanov

Automatic surgical scene segmentation is fundamental for facilitating cognitive intelligence in the modern operating theatre.

Contrastive Learning Scene Segmentation

Impression Allocation and Policy Search in Display Advertising

no code implementations11 Mar 2022 Di wu, Cheng Chen, Xiujun Chen, Junwei Pan, Xun Yang, Qing Tan, Jian Xu, Kuang-Chih Lee

In order to address the unstable traffic pattern challenge and achieve the optimal overall outcome, we propose a multi-agent reinforcement learning method to adjust the bids from each guaranteed contract, which is simple, converging efficiently and scalable.

Multi-agent Reinforcement Learning

A Unified Framework for Campaign Performance Forecasting in Online Display Advertising

no code implementations24 Feb 2022 Jun Chen, Cheng Chen, Huayue Zhang, Qing Tan

Advertisers usually enjoy the flexibility to choose criteria like target audience, geographic area and bid price when planning an campaign for online display advertising, while they lack forecast information on campaign performance to optimize delivery strategies in advance, resulting in a waste of labour and budget for feedback adjustments.

Multi-Task Learning

Deep Single Image Deraining using An Asymetric Cycle Generative and Adversarial Framework

no code implementations19 Feb 2022 Wei Liu, Rui Jiang, Cheng Chen, Tao Lu, Zixiang Xiong

The former consists of parallel rain removal path and rain-fog feature extraction path by the rain and derain-fog network and the attention rain-fog feature extraction network (ARFE) , while the latter only contains a synthetic rain transformation path.

Single Image Deraining

Unpaired Quad-Path Cycle Consistent Adversarial Networks for Single Image Defogging

no code implementations19 Feb 2022 Wei Liu, Cheng Chen, Rui Jiang, Tao Lu, Zixiang Xiong

To address these issues, we develop a novel generative adversarial network, called quad-path cycle consistent adversarial network (QPC-Net), for single image defogging.

MuZero with Self-competition for Rate Control in VP9 Video Compression

no code implementations14 Feb 2022 Amol Mandhane, Anton Zhernov, Maribeth Rauh, Chenjie Gu, Miaosen Wang, Flora Xue, Wendy Shang, Derek Pang, Rene Claus, Ching-Han Chiang, Cheng Chen, Jingning Han, Angie Chen, Daniel J. Mankowitz, Jackson Broshear, Julian Schrittwieser, Thomas Hubert, Oriol Vinyals, Timothy Mann

Specifically, we target the problem of learning a rate control policy to select the quantization parameters (QP) in the encoding process of libvpx, an open source VP9 video compression library widely used by popular video-on-demand (VOD) services.

Decision Making Quantization +1

Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning

no code implementations18 Oct 2021 Rodolfo S. M. Freitas, Ágatha P. F. Lima, Cheng Chen, Fernando A. Rochinha, Daniel Mira, Xi Jiang

Accurate determination of fuel properties of complex mixtures over a wide range of pressure and temperature conditions is essential to utilizing alternative fuels.

Gaussian Processes

bert2BERT: Towards Reusable Pretrained Language Models

no code implementations ACL 2022 Cheng Chen, Yichun Yin, Lifeng Shang, Xin Jiang, Yujia Qin, Fengyu Wang, Zhi Wang, Xiao Chen, Zhiyuan Liu, Qun Liu

However, large language model pre-training costs intensive computational resources and most of the models are trained from scratch without reusing the existing pre-trained models, which is wasteful.

Language Modelling Large Language Model

Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization

no code implementations10 Oct 2021 Luo Luo, YuJun Li, Cheng Chen

In this paper, we propose a novel approach for minimax optimization, called Minimax Cubic Newton (MCN), which could find an $\big(\varepsilon,\kappa^{1. 5}\sqrt{\rho\varepsilon}\,\big)$-second-order stationary point of $P({\bf x})$ with calling ${\mathcal O}\big(\kappa^{1. 5}\sqrt{\rho}\varepsilon^{-1. 5}\big)$ times of second-order oracles and $\tilde{\mathcal O}\big(\kappa^{2}\sqrt{\rho}\varepsilon^{-1. 5}\big)$ times of first-order oracles, where $\kappa$ is the condition number and $\rho$ is the Lipschitz continuous constant for the Hessian of $f({\bf x},{\bf y})$.

Improving Punctuation Restoration for Speech Transcripts via External Data

no code implementations WNUT (ACL) 2021 Xue-Yong Fu, Cheng Chen, Md Tahmid Rahman Laskar, Shashi Bhushan TN, Simon Corston-Oliver

To leverage the available written text datasets, we introduce a data sampling technique based on an n-gram language model to sample more training data that are similar to our in-domain data.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Assisted Learning for Organizations with Limited Imbalanced Data

no code implementations29 Sep 2021 Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou, Bhavya Kailkhura

We develop an assisted learning framework for assisting organization-level learners to improve their learning performance with limited and imbalanced data.

Decision Making

Assisted Learning for Organizations with Limited Data

no code implementations20 Sep 2021 Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou

We develop an assisted learning framework for assisting organization-level learners to improve their learning performance with limited and imbalanced data.

Decision Making

Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling

1 code implementation19 Sep 2021 Cheng Chen, Quande Liu, Yueming Jin, Qi Dou, Pheng-Ann Heng

We present a novel denoised pseudo-labeling method for this problem, which effectively makes use of the source model and unlabeled target data to promote model self-adaptation from pseudo labels.

Denoising Image Segmentation +2

Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack

no code implementations6 Sep 2021 Shengcai Liu, Ning Lu, Cheng Chen, Ke Tang

Over the past few years, various word-level textual attack approaches have been proposed to reveal the vulnerability of deep neural networks used in natural language processing.

Combinatorial Optimization

AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models

1 code implementation ACL 2021 Yichun Yin, Cheng Chen, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu

Specifically, we carefully design the techniques of one-shot learning and the search space to provide an adaptive and efficient development way of tiny PLMs for various latency constraints.

Neural Architecture Search One-Shot Learning

Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling

no code implementations Findings (ACL) 2021 Yutai Hou, Yongkui Lai, Cheng Chen, Wanxiang Che, Ting Liu

However, dialogue language understanding contains two closely related tasks, i. e., intent detection and slot filling, and often benefits from jointly learning the two tasks.

Few-Shot Learning Intent Detection +2

Extract then Distill: Efficient and Effective Task-Agnostic BERT Distillation

no code implementations24 Apr 2021 Cheng Chen, Yichun Yin, Lifeng Shang, Zhi Wang, Xin Jiang, Xiao Chen, Qun Liu

Task-agnostic knowledge distillation, a teacher-student framework, has been proved effective for BERT compression.

Knowledge Distillation

Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing

no code implementations30 Mar 2021 Cheng Chen, Bhavya Kailkhura, Ryan Goldhahn, Yi Zhou

Federated learning is an emerging data-private distributed learning framework, which, however, is vulnerable to adversarial attacks.

Federated Learning

Temporal Memory Relation Network for Workflow Recognition from Surgical Video

1 code implementation30 Mar 2021 Yueming Jin, Yonghao Long, Cheng Chen, Zixu Zhao, Qi Dou, Pheng-Ann Heng

In this paper, we propose a novel end-to-end temporal memory relation network (TMRNet) for relating long-range and multi-scale temporal patterns to augment the present features.

Relation Network

Computation Resource Allocation Solution in Recommender Systems

no code implementations3 Mar 2021 Xun Yang, Yunli Wang, Cheng Chen, Qing Tan, Chuan Yu, Jian Xu, Xiaoqiang Zhu

On the other hand, the response time of these systems is strictly limited to a short period, e. g. 300 milliseconds in our real system, which is also being exhausted by the increasingly complex models and algorithms.

Recommendation Systems

MITNet: GAN Enhanced Magnetic Induction Tomography Based on Complex CNN

no code implementations16 Feb 2021 Zuohui Chen, Qing Yuan, Xujie Song, Cheng Chen, Dan Zhang, Yun Xiang, Ruigang Liu, Qi Xuan

Magnetic induction tomography (MIT) is an efficient solution for long-term brain disease monitoring, which focuses on reconstructing bio-impedance distribution inside the human brain using non-intrusive electromagnetic fields.

Image Reconstruction

C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling

1 code implementation13 Dec 2020 Yutai Hou, Sanyuan Chen, Wanxiang Che, Cheng Chen, Ting Liu

Slot filling, a fundamental module of spoken language understanding, often suffers from insufficient quantity and diversity of training data.

Data Augmentation slot-filling +2

Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study

no code implementations13 Nov 2020 Cheng Chen, Junjie Yang, Yi Zhou

Specifically, we find that the optimization trajectories of successful DNN trainings consistently obey a certain regularity principle that regularizes the model update direction to be aligned with the trajectory direction.

Efficient Projection-Free Algorithms for Saddle Point Problems

no code implementations NeurIPS 2020 Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu

The Frank-Wolfe algorithm is a classic method for constrained optimization problems.

Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning

no code implementations5 Oct 2020 Cheng Chen, Olmo Cerri, Thong Q. Nguyen, Jean-Roch Vlimant, Maurizio Pierini

We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets.

Data Augmentation

FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling

no code implementations22 Sep 2020 Cheng Chen, Ziyi Chen, Yi Zhou, Bhavya Kailkhura

We develop FedCluster--a novel federated learning framework with improved optimization efficiency, and investigate its theoretical convergence properties.

Federated Learning

Learning Directional Feature Maps for Cardiac MRI Segmentation

1 code implementation22 Jul 2020 Feng Cheng, Cheng Chen, Yukang Wang, Heshui Shi, Yukun Cao, Dandan Tu, Changzheng Zhang, Yongchao Xu

Cardiac MRI segmentation plays a crucial role in clinical diagnosis for evaluating personalized cardiac performance parameters.

Cardiac Segmentation MRI segmentation

Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion

1 code implementation22 Feb 2020 Cheng Chen, Qi Dou, Yueming Jin, Hao Chen, Jing Qin, Pheng-Ann Heng

We tackle this challenge and propose a novel multimodal segmentation framework which is robust to the absence of imaging modalities.

Brain Tumor Segmentation Disentanglement +2

Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation

1 code implementation6 Feb 2020 Cheng Chen, Qi Dou, Hao Chen, Jing Qin, Pheng Ann Heng

In this work, we present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA), to effectively adapt a segmentation network to an unlabeled target domain.

Image Segmentation Medical Image Segmentation +3

An Optimization Principle Of Deep Learning?

no code implementations25 Sep 2019 Cheng Chen, Junjie Yang, Yi Zhou

In particular, we observe that the trainings that apply the training techniques achieve accelerated convergence and obey the principle with a large $\gamma$, which is consistent with the $\mathcal{O}(1/\gamma K)$ convergence rate result under the optimization principle.

A Stochastic Proximal Point Algorithm for Saddle-Point Problems

no code implementations13 Sep 2019 Luo Luo, Cheng Chen, Yu-Jun Li, Guangzeng Xie, Zhihua Zhang

We consider saddle point problems which objective functions are the average of $n$ strongly convex-concave individual components.

Multi-Instance Multi-Scale CNN for Medical Image Classification

no code implementations4 Jul 2019 Shaohua Li, Yong liu, Xiuchao Sui, Cheng Chen, Gabriel Tjio, Daniel Shu Wei Ting, Rick Siow Mong Goh

Deep learning for medical image classification faces three major challenges: 1) the number of annotated medical images for training are usually small; 2) regions of interest (ROIs) are relatively small with unclear boundaries in the whole medical images, and may appear in arbitrary positions across the x, y (and also z in 3D images) dimensions.

General Classification Image Classification +2

PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation

2 code implementations19 Dec 2018 Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Ben Glocker, Xiahai Zhuang, Pheng-Ann Heng

In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e. g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner.

Cardiac Segmentation Domain Adaptation +1

A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising

no code implementations10 Sep 2018 Di Wu, Cheng Chen, Xun Yang, Xiujun Chen, Qing Tan, Jian Xu, Kun Gai

With this formulation, we derive the optimal impression allocation strategy by solving the optimal bidding functions for contracts.

Multi-agent Reinforcement Learning reinforcement-learning +1

Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation

no code implementations2 Jun 2018 Cheng Chen, Qi Dou, Hao Chen, Pheng-Ann Heng

In spite of the compelling achievements that deep neural networks (DNNs) have made in medical image computing, these deep models often suffer from degraded performance when being applied to new test datasets with domain shift.

Transfer Learning Unsupervised Domain Adaptation

RPC Considered Harmful: Fast Distributed Deep Learning on RDMA

no code implementations22 May 2018 Jilong Xue, Youshan Miao, Cheng Chen, Ming Wu, Lintao Zhang, Lidong Zhou

Its computation is typically characterized by a simple tensor data abstraction to model multi-dimensional matrices, a data-flow graph to model computation, and iterative executions with relatively frequent synchronizations, thereby making it substantially different from Map/Reduce style distributed big data computation.

Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss

2 code implementations29 Apr 2018 Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Pheng-Ann Heng

The domain adaptation is more significant while challenging in the field of biomedical image analysis, where cross-modality data have largely different distributions.

Transfer Learning Unsupervised Domain Adaptation

Robust Frequent Directions with Application in Online Learning

no code implementations15 May 2017 Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang

We also apply RFD to online learning and propose an effective hyperparameter-free online Newton algorithm.

A Novel Method to Study Bottom-up Visual Saliency and its Neural Mechanism

no code implementations13 Apr 2016 Cheng Chen, Xilin Zhang, Yizhou Wang, Fang Fang

In this study, we propose a novel method to measure bottom-up saliency maps of natural images.

A Parallel algorithm for $\mathcal{X}$-Armed bandits

no code implementations26 Oct 2015 Cheng Chen, Shuang Liu, Zhihua Zhang, Wu-Jun Li

To deal with these large-scale data sets, we study a distributed setting of $\mathcal{X}$-armed bandits, where $m$ players collaborate to find the maximum of the unknown function.

NV-Tree: Reducing Consistency Cost for NVM-based Single Level Systems

no code implementations16 Apr 2015 Jun Yang, Qingsong Wei, Cheng Chen, Chundong Wang, and Khai Leong Yong, Data Storage Institute, A-STAR; Bingsheng He, Nanyang Technological University

Although the memory fence and CPU cacheline flush instructions can order memory writes to achieve data consistency, they introduce a significant overhead (more than 10X slower in performance).

Regret vs. Communication: Distributed Stochastic Multi-Armed Bandits and Beyond

no code implementations14 Apr 2015 Shuang Liu, Cheng Chen, Zhihua Zhang

When the time horizon is unknown, we measure the frequency of communication through a new notion called the density of the communication set, and give an exact characterization of the interplay between regret and communication.

Multi-Armed Bandits

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