Yiran Chen

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Duke University

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University of Pittsburgh

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ByteDance Inc.

Associate Professor, Co-director of Duke Center for Evolutionary Intelligence at Duke University since 2017, Associate Professor, Bicentennial Alumni Faculty Fellow at University of Pittsburgh since 2014, Assistant Professor at University of Pittsburgh from 2010-2014, Staff Engineer at Seagate LLC from 2007-2010, Sr. R&D Engineer I at Synopsys from 2005-2007, Research Assistant at Purdue University from 2001-2005, Intern Engineer at Micron Technology 2003, Intern Engineer at Micron Technology 2002, Ph.D. Electrical and Computer Engineering at Purdue University 2001-2005.

Featured Co-authors

Xin Li
206 publications
Yu Wang
196 publications
Lei Li
189 publications
Lawrence Carin
174 publications
Xin Liu
159 publications
Ang Li
138 publications
Hao Zhou
114 publications
Xiang Chen
108 publications
Xin Chen
103 publications
Xipeng Qiu
102 publications
Xuanjing Huang
98 publications

∙ 07/14/2022

NASRec: Weight Sharing Neural Architecture Search for Recommender Systems

The rise of deep neural networks provides an important driver in optimiz...

Tunhou Zhang, et al. ∙

∙ 03/30/2022

Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data

Applying machine learning (ML) in design flow is a popular trend in EDA ...

Jingyu Pan, et al. ∙

∙ 03/20/2022

The Dark Side: Security Concerns in Machine Learning for EDA

The growing IC complexity has led to a compelling need for design effici...

Zhiyao Xie, et al. ∙

∙ 02/21/2022

Privacy Leakage of Adversarial Training Models in Federated Learning Systems

Adversarial Training (AT) is crucial for obtaining deep neural networks ...

Jingyang Zhang, et al. ∙

∙ 11/23/2021

HERO: Hessian-Enhanced Robust Optimization for Unifying and Improving Generalization and Quantization Performance

With the recent demand of deploying neural network models on mobile and ...

Huanrui Yang, et al. ∙

∙ 10/26/2021

FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective

Federated learning (FL) is a popular distributed learning framework that...

Jingwei Sun, et al. ∙

∙ 08/13/2021

MTG: A Benchmarking Suite for Multilingual Text Generation

We introduce MTG, a new benchmark suite for training and evaluating mult...

Yiran Chen, et al. ∙

∙ 07/09/2021

Lithography Hotspot Detection via Heterogeneous Federated Learning with Local Adaptation

As technology scaling is approaching the physical limit, lithography hot...

Xuezhong Lin, et al. ∙

∙ 07/03/2021

Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective

Learning with graphs has attracted significant attention recently. Exist...

Binghui Wang, et al. ∙

∙ 06/07/2021

Fine-grained Out-of-Distribution Detection with Mixup Outlier Exposure

Enabling out-of-distribution (OOD) detection for DNNs is critical for th...

Jingyang Zhang, et al. ∙

∙ 04/07/2021

Enhancing Scientific Papers Summarization with Citation Graph

Previous work for text summarization in scientific domain mainly focused...

Chenxin An, et al. ∙

∙ 03/24/2021

FedGP: Correlation-Based Active Client Selection for Heterogeneous Federated Learning

Client-wise heterogeneity is one of the major issues that hinder effecti...

Minxue Tang, et al. ∙

∙ 03/17/2021

Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?

We design blackbox transfer-based targeted adversarial attacks for an en...

Nathan Inkawhich, et al. ∙

∙ 03/17/2021

The Untapped Potential of Off-the-Shelf Convolutional Neural Networks

Over recent years, a myriad of novel convolutional network architectures...

Matthew Inkawhich, et al. ∙

∙ 02/20/2021

BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization

Mixed-precision quantization can potentially achieve the optimal tradeof...

Huanrui Yang, et al. ∙

∙ 01/19/2021

On Provable Backdoor Defense in Collaborative Learning

As collaborative learning allows joint training of a model using multipl...

Ximing Qiao, et al. ∙

∙ 01/08/2021

Hermes: Decentralized Dynamic Spectrum Access System for Massive Devices Deployment in 5G

With the incoming 5G network, the ubiquitous Internet of Things (IoT) de...

Zhihui Gao, et al. ∙

∙ 12/08/2020

Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective

Federated learning (FL) is a popular distributed learning framework that...

Jingwei Sun, et al. ∙

∙ 12/08/2020

GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs

Graph-based semi-supervised node classification (GraphSSC) has wide appl...

Binghui Wang, et al. ∙

∙ 12/03/2020

Automatic Routability Predictor Development Using Neural Architecture Search

The rise of machine learning technology inspires a boom of its applicati...

Jingyu Pan, et al. ∙

∙ 11/30/2020

ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition

Scale variance among different sizes of body parts and objects is a chal...

Hsin-Pai Cheng, et al. ∙

∙ 11/27/2020

Net2: A Graph Attention Network Method Customized for Pre-Placement Net Length Estimation

Net length is a key proxy metric for optimizing timing and power across ...

Zhiyao Xie, et al. ∙

∙ 11/26/2020

PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network

IR drop is a fundamental constraint required by almost all chip designs....

Zhiyao Xie, et al. ∙

∙ 11/26/2020

FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning

Design flow parameters are of utmost importance to chip design quality a...

Zhiyao Xie, et al. ∙

∙ 11/26/2020

Fast IR Drop Estimation with Machine Learning

IR drop constraint is a fundamental requirement enforced in almost all c...

Zhiyao Xie, et al. ∙

∙ 11/08/2020

Towards Latency-aware DNN Optimization with GPU Runtime Analysis and Tail Effect Elimination

Despite the superb performance of State-Of-The-Art (SOTA) DNNs, the incr...

Fuxun Yu, et al. ∙

∙ 11/06/2020

Low-Cost Floating-Point Processing in ReRAM for Scientific Computing

We propose ReFloat, a principled approach for low-cost floating-point pr...

Linghao Song, et al. ∙

∙ 10/11/2020

CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems

Neural network-based models augmented with unsupervised pre-trained know...

Yiran Chen, et al. ∙

∙ 09/01/2020

Evasion Attacks to Graph Neural Networks via Influence Function

Graph neural networks (GNNs) have achieved state-of-the-art performance ...

Binghui Wang, et al. ∙

∙ 09/01/2020

Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs

Link prediction in dynamic graphs (LPDG) is an important research proble...

Houxiang Fan, et al. ∙

∙ 08/07/2020

LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets

Federated learning is a popular distributed machine learning paradigm wi...

Ang Li, et al. ∙

∙ 07/21/2020

SparseTrain: Exploiting Dataflow Sparsity for Efficient Convolutional Neural Networks Training

Training Convolutional Neural Networks (CNNs) usually requires a large n...

Pengcheng Dai, et al. ∙

∙ 07/08/2020

NASGEM: Neural Architecture Search via Graph Embedding Method

Neural Architecture Search (NAS) automates and prospers the design of ne...

Hsin-Pai Cheng, et al. ∙

∙ 06/12/2020

Defending against GAN-based Deepfake Attacks via Transformation-aware Adversarial Faces

Deepfake represents a category of face-swapping attacks that leverage ma...

Chaofei Yang, et al. ∙

∙ 05/24/2020

MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones

Recent research has made great progress in realizing neural style transf...

Ang Li, et al. ∙

∙ 05/23/2020

TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework with Anonymized Intermediate Representations

The success of deep learning partially benefits from the availability of...

Ang Li, et al. ∙

∙ 05/14/2020

PENNI: Pruned Kernel Sharing for Efficient CNN Inference

Although state-of-the-art (SOTA) CNNs achieve outstanding performance on...

Shiyu Li, et al. ∙

∙ 04/30/2020

TRP: Trained Rank Pruning for Efficient Deep Neural Networks

To enable DNNs on edge devices like mobile phones, low-rank approximatio...

Yuhui Xu, et al. ∙

∙ 04/29/2020

Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability

We consider the blackbox transfer-based targeted adversarial attack thre...

Nathan Inkawhich, et al. ∙

∙ 04/27/2020

Transferable Perturbations of Deep Feature Distributions

Almost all current adversarial attacks of CNN classifiers rely on inform...

Nathan Inkawhich, et al. ∙

∙ 04/20/2020

Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification

Modern deep neural networks (DNNs) often require high memory consumption...

Huanrui Yang, et al. ∙

∙ 04/19/2020

Extractive Summarization as Text Matching

This paper creates a paradigm shift with regard to the way we build neur...

Ming Zhong, et al. ∙

∙ 12/02/2019

Neural Predictor for Neural Architecture Search

Neural Architecture Search methods are effective but often use complex a...

Wei Wen, et al. ∙

∙ 11/21/2019

AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture

Resource is an important constraint when deploying Deep Neural Networks ...

Tunhou Zhang, et al. ∙

∙ 10/25/2019

Structural sparsification for Far-field Speaker Recognition with GNA

Recently, deep neural networks (DNN) have been widely used in speaker re...

Jingchi Zhang, et al. ∙

∙ 10/09/2019

Traned Rank Pruning for Efficient Deep Neural Networks

To accelerate DNNs inference, low-rank approximation has been widely ado...

Yuhui Xu, et al. ∙

∙ 09/25/2019

Conditional Transferring Features: Scaling GANs to Thousands of Classes with 30

Generative adversarial network (GAN) has greatly improved the quality of...

Chunpeng Wu, et al. ∙

∙ 09/17/2019

Towards Efficient and Secure Delivery of Data for Deep Learning with Privacy-Preserving

Privacy recently emerges as a severe concern in deep learning, that is, ...

Juncheng Shen, et al. ∙

∙ 09/09/2019

DeepObfuscator: Adversarial Training Framework for Privacy-Preserving Image Classification

Deep learning has been widely utilized in many computer vision applicati...

Ang Li, et al. ∙

∙ 08/01/2019

Accelerating CNN Training by Sparsifying Activation Gradients

Gradients to activations get involved in most of the calculations during...

Xucheng Ye, et al. ∙