Yiran Chen

is this you? claim profile

∙ 0 followers
Duke University ∙ Facebook ∙ University of Pittsburgh

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

Lawrence Carin
169 publications
Xin Li
147 publications
Yu Wang
144 publications
Xin Liu
113 publications
Xuanjing Huang
83 publications
Ang Li
81 publications
Xipeng Qiu
81 publications
Xin Chen
76 publications
Di Wang
70 publications
Xiang Chen
64 publications
Pan Zhou
63 publications

Lithography Hotspot Detection via Heterogeneous Federated Learning with Local Adaptation

As technology scaling is approaching the physical limit, lithography hot...
07/09/2021 ∙ by Xuezhong Lin, et al. ∙ 0 ∙ share

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

Learning with graphs has attracted significant attention recently. Exist...
07/03/2021 ∙ by Binghui Wang, et al. ∙ 0 ∙ share

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

Enabling out-of-distribution (OOD) detection for DNNs is critical for th...
06/07/2021 ∙ by Jingyang Zhang, et al. ∙ 0 ∙ share

read it
Enhancing Scientific Papers Summarization with Citation Graph

Previous work for text summarization in scientific domain mainly focused...
04/07/2021 ∙ by Chenxin An, et al. ∙ 0 ∙ share

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

Client-wise heterogeneity is one of the major issues that hinder effecti...
03/24/2021 ∙ by Minxue Tang, et al. ∙ 0 ∙ share

read it
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...
03/17/2021 ∙ by Nathan Inkawhich, et al. ∙ 0 ∙ share

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

Over recent years, a myriad of novel convolutional network architectures...
03/17/2021 ∙ by Matthew Inkawhich, et al. ∙ 13 ∙ share

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

Mixed-precision quantization can potentially achieve the optimal tradeof...
02/20/2021 ∙ by Huanrui Yang, et al. ∙ 0 ∙ share

read it
On Provable Backdoor Defense in Collaborative Learning

As collaborative learning allows joint training of a model using multipl...
01/19/2021 ∙ by Ximing Qiao, et al. ∙ 0 ∙ share

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

With the incoming 5G network, the ubiquitous Internet of Things (IoT) de...
01/08/2021 ∙ by Zhihui Gao, et al. ∙ 0 ∙ share

read it
Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective

Federated learning (FL) is a popular distributed learning framework that...
12/08/2020 ∙ by Jingwei Sun, et al. ∙ 0 ∙ share

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

Graph-based semi-supervised node classification (GraphSSC) has wide appl...
12/08/2020 ∙ by Binghui Wang, et al. ∙ 0 ∙ share

read it
Automatic Routability Predictor Development Using Neural Architecture Search

The rise of machine learning technology inspires a boom of its applicati...
12/03/2020 ∙ by Jingyu Pan, et al. ∙ 0 ∙ share

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

Scale variance among different sizes of body parts and objects is a chal...
11/30/2020 ∙ by Hsin-Pai Cheng, et al. ∙ 0 ∙ share

read it
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 ...
11/27/2020 ∙ by Zhiyao Xie, et al. ∙ 0 ∙ share

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

IR drop is a fundamental constraint required by almost all chip designs....
11/26/2020 ∙ by Zhiyao Xie, et al. ∙ 0 ∙ share

read it
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...
11/26/2020 ∙ by Zhiyao Xie, et al. ∙ 0 ∙ share

read it
Fast IR Drop Estimation with Machine Learning

IR drop constraint is a fundamental requirement enforced in almost all c...
11/26/2020 ∙ by Zhiyao Xie, et al. ∙ 0 ∙ share

read it
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...
11/08/2020 ∙ by Fuxun Yu, et al. ∙ 0 ∙ share

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

We propose ReFloat, a principled approach for low-cost floating-point pr...
11/06/2020 ∙ by Linghao Song, et al. ∙ 0 ∙ share

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

Neural network-based models augmented with unsupervised pre-trained know...
10/11/2020 ∙ by Yiran Chen, et al. ∙ 0 ∙ share

read it
Evasion Attacks to Graph Neural Networks via Influence Function

Graph neural networks (GNNs) have achieved state-of-the-art performance ...
09/01/2020 ∙ by Binghui Wang, et al. ∙ 0 ∙ share

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

Link prediction in dynamic graphs (LPDG) is an important research proble...
09/01/2020 ∙ by Houxiang Fan, et al. ∙ 4 ∙ share

read it
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...
08/07/2020 ∙ by Ang Li, et al. ∙ 12 ∙ share

read it
SparseTrain: Exploiting Dataflow Sparsity for Efficient Convolutional Neural Networks Training

Training Convolutional Neural Networks (CNNs) usually requires a large n...
07/21/2020 ∙ by Pengcheng Dai, et al. ∙ 0 ∙ share

read it
NASGEM: Neural Architecture Search via Graph Embedding Method

Neural Architecture Search (NAS) automates and prospers the design of ne...
07/08/2020 ∙ by Hsin-Pai Cheng, et al. ∙ 0 ∙ share

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

Deepfake represents a category of face-swapping attacks that leverage ma...
06/12/2020 ∙ by Chaofei Yang, et al. ∙ 0 ∙ share

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

Recent research has made great progress in realizing neural style transf...
05/24/2020 ∙ by Ang Li, et al. ∙ 3 ∙ share

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

The success of deep learning partially benefits from the availability of...
05/23/2020 ∙ by Ang Li, et al. ∙ 3 ∙ share

read it
PENNI: Pruned Kernel Sharing for Efficient CNN Inference

Although state-of-the-art (SOTA) CNNs achieve outstanding performance on...
05/14/2020 ∙ by Shiyu Li, et al. ∙ 0 ∙ share

read it
TRP: Trained Rank Pruning for Efficient Deep Neural Networks

To enable DNNs on edge devices like mobile phones, low-rank approximatio...
04/30/2020 ∙ by Yuhui Xu, et al. ∙ 8 ∙ share

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

We consider the blackbox transfer-based targeted adversarial attack thre...
04/29/2020 ∙ by Nathan Inkawhich, et al. ∙ 0 ∙ share

read it
Transferable Perturbations of Deep Feature Distributions

Almost all current adversarial attacks of CNN classifiers rely on inform...
04/27/2020 ∙ by Nathan Inkawhich, et al. ∙ 0 ∙ share

read it
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...
04/20/2020 ∙ by Huanrui Yang, et al. ∙ 10 ∙ share

read it
Extractive Summarization as Text Matching

This paper creates a paradigm shift with regard to the way we build neur...
04/19/2020 ∙ by Ming Zhong, et al. ∙ 0 ∙ share

read it
Neural Predictor for Neural Architecture Search

Neural Architecture Search methods are effective but often use complex a...
12/02/2019 ∙ by Wei Wen, et al. ∙ 0 ∙ share

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

Resource is an important constraint when deploying Deep Neural Networks ...
11/21/2019 ∙ by Tunhou Zhang, et al. ∙ 0 ∙ share

read it
Structural sparsification for Far-field Speaker Recognition with GNA

Recently, deep neural networks (DNN) have been widely used in speaker re...
10/25/2019 ∙ by Jingchi Zhang, et al. ∙ 0 ∙ share

read it
Traned Rank Pruning for Efficient Deep Neural Networks

To accelerate DNNs inference, low-rank approximation has been widely ado...
10/09/2019 ∙ by Yuhui Xu, et al. ∙ 0 ∙ share

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

Generative adversarial network (GAN) has greatly improved the quality of...
09/25/2019 ∙ by Chunpeng Wu, et al. ∙ 0 ∙ share

read it
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, ...
09/17/2019 ∙ by Juncheng Shen, et al. ∙ 0 ∙ share

read it
DeepObfuscator: Adversarial Training Framework for Privacy-Preserving Image Classification

Deep learning has been widely utilized in many computer vision applicati...
09/09/2019 ∙ by Ang Li, et al. ∙ 21 ∙ share

read it
Accelerating CNN Training by Sparsifying Activation Gradients

Gradients to activations get involved in most of the calculations during...
08/01/2019 ∙ by Xucheng Ye, et al. ∙ 1 ∙ share

read it
RED: A ReRAM-based Deconvolution Accelerator

Deconvolution has been widespread in neural networks. For example, it is...
07/05/2019 ∙ by Zichen Fan, et al. ∙ 0 ∙ share

read it
SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures

Designing neural architectures for edge devices is subject to constraint...
06/19/2019 ∙ by Hsin-Pai, et al. ∙ 0 ∙ share

read it
Thread Batching for High-performance Energy-efficient GPU Memory Design

Massive multi-threading in GPU imposes tremendous pressure on memory sub...
06/13/2019 ∙ by Bing Li, et al. ∙ 0 ∙ share

read it
eSLAM: An Energy-Efficient Accelerator for Real-Time ORB-SLAM on FPGA Platform

Simultaneous Localization and Mapping (SLAM) is a critical task for auto...
06/03/2019 ∙ by Runze Liu, et al. ∙ 0 ∙ share

read it
Snooping Attacks on Deep Reinforcement Learning

Adversarial attacks have exposed a significant security vulnerability in...
05/28/2019 ∙ by Matthew Inkawhich, et al. ∙ 0 ∙ share

read it
Low-Power Computer Vision: Status, Challenges, Opportunities

Computer vision has achieved impressive progress in recent years. Meanwh...
04/15/2019 ∙ by Sergei Alyamkin, et al. ∙ 16 ∙ share

read it
Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution

The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual comp...
03/12/2019 ∙ by Chen Feng, et al. ∙ 6 ∙ share

read it