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08/16/2023
Stable and Causal Inference for Discriminative Self-supervised Deep Visual Representations
In recent years, discriminative self-supervised methods have made signif...
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07/06/2023
LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search
Search spaces hallmark the advancement of Neural Architecture Search (NA...
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06/15/2023
OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection
Out-of-Distribution (OOD) detection is critical for the reliable operati...
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05/09/2023
Towards Building the Federated GPT: Federated Instruction Tuning
While “instruction-tuned" generative large language models (LLMs) have d...
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03/28/2023
Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples
Federated learning is a popular collaborative learning approach that ena...
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03/25/2023
SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection
Building up reliable Out-of-Distribution (OOD) detectors is challenging,...
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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...
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01/13/2023
Analog, In-memory Compute Architectures for Artificial Intelligence
This paper presents an analysis of the fundamental limits on energy effi...
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12/29/2022
Biologically Plausible Learning on Neuromorphic Hardware Architectures
With an ever-growing number of parameters defining increasingly complex ...
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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...
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11/11/2022
More Generalized and Personalized Unsupervised Representation Learning In A Distributed System
Discriminative unsupervised learning methods such as contrastive learnin...
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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...
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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...
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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...
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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...
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08/23/2022
Self-Trained Proposal Networks for the Open World
Deep learning-based object proposal methods have enabled significant adv...
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07/14/2022
NASRec: Weight Sharing Neural Architecture Search for Recommender Systems
The rise of deep neural networks provides an important driver in optimiz...
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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 ...
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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...
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02/21/2022
Privacy Leakage of Adversarial Training Models in Federated Learning Systems
Adversarial Training (AT) is crucial for obtaining deep neural networks ...
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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 ...
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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...
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08/13/2021
MTG: A Benchmarking Suite for Multilingual Text Generation
We introduce MTG, a new benchmark suite for training and evaluating mult...
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07/09/2021
Lithography Hotspot Detection via Heterogeneous Federated Learning with Local Adaptation
As technology scaling is approaching the physical limit, lithography hot...
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07/03/2021
Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective
Learning with graphs has attracted significant attention recently. Exist...
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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...
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04/07/2021
Enhancing Scientific Papers Summarization with Citation Graph
Previous work for text summarization in scientific domain mainly focused...
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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...
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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...
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03/17/2021
The Untapped Potential of Off-the-Shelf Convolutional Neural Networks
Over recent years, a myriad of novel convolutional network architectures...
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02/20/2021
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization
Mixed-precision quantization can potentially achieve the optimal tradeof...
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01/19/2021
On Provable Backdoor Defense in Collaborative Learning
As collaborative learning allows joint training of a model using multipl...
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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...
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12/08/2020
Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective
Federated learning (FL) is a popular distributed learning framework that...
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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...
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12/03/2020
Automatic Routability Predictor Development Using Neural Architecture Search
The rise of machine learning technology inspires a boom of its applicati...
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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...
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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 ...
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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....
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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...
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11/26/2020
Fast IR Drop Estimation with Machine Learning
IR drop constraint is a fundamental requirement enforced in almost all c...
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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...
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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...
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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...
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09/01/2020
Evasion Attacks to Graph Neural Networks via Influence Function
Graph neural networks (GNNs) have achieved state-of-the-art performance ...
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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...
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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...
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07/21/2020
SparseTrain: Exploiting Dataflow Sparsity for Efficient Convolutional Neural Networks Training
Training Convolutional Neural Networks (CNNs) usually requires a large n...
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07/08/2020
NASGEM: Neural Architecture Search via Graph Embedding Method
Neural Architecture Search (NAS) automates and prospers the design of ne...
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06/12/2020
