b'Yiran Chen'

research

∙ 08/16/2023

Stable and Causal Inference for Discriminative Self-supervised Deep Visual Representations

In recent years, discriminative self-supervised methods have made signif...

0 Yuewei Yang, et al. ∙

research

∙ 07/06/2023

LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search

Search spaces hallmark the advancement of Neural Architecture Search (NA...

0 Bhavna Gopal, et al. ∙

research

∙ 06/15/2023

OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection

Out-of-Distribution (OOD) detection is critical for the reliable operati...

0 Jingyang Zhang, et al. ∙

research

∙ 05/09/2023

Towards Building the Federated GPT: Federated Instruction Tuning

While “instruction-tuned" generative large language models (LLMs) have d...

0 Jianyi Zhang, et al. ∙

research

∙ 03/28/2023

Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples

Federated learning is a popular collaborative learning approach that ena...

0 Jingwei Sun, et al. ∙

research

∙ 03/25/2023

SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection

Building up reliable Out-of-Distribution (OOD) detectors is challenging,...

0 Jingyang Zhang, et al. ∙

research

∙ 02/10/2023

Is It Enough to Recommend Tasks to Newcomers? Understanding Mentoring on Good First Issues

Newcomers are critical for the success and continuity of open source sof...

0 Xin Tan, et al. ∙

research

∙ 01/13/2023

Analog, In-memory Compute Architectures for Artificial Intelligence

This paper presents an analysis of the fundamental limits on energy effi...

0 Patrick Bowen, et al. ∙

research

∙ 12/29/2022

Biologically Plausible Learning on Neuromorphic Hardware Architectures

With an ever-growing number of parameters defining increasingly complex ...

0 Christopher Wolters, et al. ∙

research

∙ 11/28/2022

PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud

The interaction and dimension of points are two important axes in design...

0 Tunhou Zhang, et al. ∙

research

∙ 11/11/2022

More Generalized and Personalized Unsupervised Representation Learning In A Distributed System

Discriminative unsupervised learning methods such as contrastive learnin...

0 Yuewei Yang, et al. ∙

research

∙ 10/06/2022

Join-Chain Network: A Logical Reasoning View of the Multi-head Attention in Transformer

Developing neural architectures that are capable of logical reasoning ha...

0 Jianyi Zhang, et al. ∙

research

∙ 09/30/2022

Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction

Due to limited communication capacities of edge devices, most existing f...

0 Jianyi Zhang, et al. ∙

research

∙ 09/09/2022

Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification

Machine learning methods must be trusted to make appropriate decisions i...

0 Randolph Linderman, et al. ∙

research

∙ 09/08/2022

FADE: Enabling Large-Scale Federated Adversarial Training on Resource-Constrained Edge Devices

Adversarial Training (AT) has been proven to be an effective method of i...

0 Minxue Tang, et al. ∙

research

∙ 08/23/2022

Self-Trained Proposal Networks for the Open World

Deep learning-based object proposal methods have enabled significant adv...

0 Matthew Inkawhich, et al. ∙

research

∙ 07/14/2022

NASRec: Weight Sharing Neural Architecture Search for Recommender Systems

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

0 Tunhou Zhang, et al. ∙

research

∙ 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 ...

0 Jingyu Pan, et al. ∙

research

∙ 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...

0 Zhiyao Xie, et al. ∙

research

∙ 02/21/2022

Privacy Leakage of Adversarial Training Models in Federated Learning Systems

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

0 Jingyang Zhang, et al. ∙

research

∙ 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 ...

0 Huanrui Yang, et al. ∙

research

∙ 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...

0 Jingwei Sun, et al. ∙

research

∙ 08/13/2021

MTG: A Benchmarking Suite for Multilingual Text Generation

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

0 Yiran Chen, et al. ∙

research

∙ 07/09/2021

Lithography Hotspot Detection via Heterogeneous Federated Learning with Local Adaptation

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

0 Xuezhong Lin, et al. ∙

research

∙ 07/03/2021

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

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

0 Binghui Wang, et al. ∙

research

∙ 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...

0 Jingyang Zhang, et al. ∙

research

∙ 04/07/2021

Enhancing Scientific Papers Summarization with Citation Graph

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

0 Chenxin An, et al. ∙

research

∙ 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...

0 Minxue Tang, et al. ∙

research

∙ 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...

0 Nathan Inkawhich, et al. ∙

research

∙ 03/17/2021

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

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

13 Matthew Inkawhich, et al. ∙

research

∙ 02/20/2021

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

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

0 Huanrui Yang, et al. ∙

research

∙ 01/19/2021

On Provable Backdoor Defense in Collaborative Learning

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

0 Ximing Qiao, et al. ∙

research

∙ 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...

0 Zhihui Gao, et al. ∙

research

∙ 12/08/2020

Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective

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

0 Jingwei Sun, et al. ∙

research

∙ 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...

0 Binghui Wang, et al. ∙

research

∙ 12/03/2020

Automatic Routability Predictor Development Using Neural Architecture Search

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

0 Jingyu Pan, et al. ∙

research

∙ 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...

0 Hsin-Pai Cheng, et al. ∙

research

∙ 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 ...

0 Zhiyao Xie, et al. ∙

research

∙ 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....

0 Zhiyao Xie, et al. ∙

research

∙ 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...

0 Zhiyao Xie, et al. ∙

research

∙ 11/26/2020

Fast IR Drop Estimation with Machine Learning

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

0 Zhiyao Xie, et al. ∙

research

∙ 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...

0 Fuxun Yu, et al. ∙

research

∙ 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...

0 Linghao Song, et al. ∙

research

∙ 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...

0 Yiran Chen, et al. ∙

research

∙ 09/01/2020

Evasion Attacks to Graph Neural Networks via Influence Function

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

0 Binghui Wang, et al. ∙

research

∙ 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...

4 Houxiang Fan, et al. ∙

research

∙ 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...

12 Ang Li, et al. ∙

research

∙ 07/21/2020

SparseTrain: Exploiting Dataflow Sparsity for Efficient Convolutional Neural Networks Training

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

1 Pengcheng Dai, et al. ∙

research

∙ 07/08/2020

NASGEM: Neural Architecture Search via Graph Embedding Method

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

0 Hsin-Pai Cheng, et al. ∙

research

∙ 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...

0 Chaofei Yang, et al. ∙

Yiran Chen

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