87 citations · 809 across the 93 of their papers we have counts for
93 papers
Muffin: A Framework Toward Multi-Dimension AI Fairness by Uniting Off-the-Shelf Models
Yi Sheng, Junhuan Yang, Lei Yang +3
Model fairness (a.k.a., bias) has become one of the most critical problems in a wide range of AI applications. An unfair model in autonomous driving may cause a traffic accident if…
Proof-of-Federated-Learning-Subchain: Free Partner Selection Subchain Based on Federated Learning
Boyang Li, Bingyu Shen, Qing Lu +2
The continuous thriving of the Blockchain society motivates research in novel designs of schemes supporting cryptocurrencies. Previously multiple Proof-of-Deep-Learning(PoDL) conse…
Unleashing the Potential of LLMs for Quantum Computing: A Study in Quantum Architecture Design
Zhiding Liang, Jinglei Cheng, Rui Yang +6
Large Language Models (LLMs) contribute significantly to the development of conversational AI and has great potentials to assist the scientific research in various areas. This pape…
On the Viability of using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Accelerators
Zheyu Yan, Yifan Qin, Xiaobo Sharon Hu +1
Deep Neural Networks (DNNs) have demonstrated impressive performance across a wide range of tasks. However, deploying DNNs on edge devices poses significant challenges due to strin…
Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation
Xinrong Hu, Yu-Jen Chen, Tsung-Yi Ho +1
Recent advances in denoising diffusion probabilistic models have shown great success in image synthesis tasks. While there are already works exploring the potential of this powerfu…
Additional Positive Enables Better Representation Learning for Medical Images
Dewen Zeng, Yawen Wu, Xinrong Hu +3
This paper presents a new way to identify additional positive pairs for BYOL, a state-of-the-art (SOTA) self-supervised learning framework, to improve its representation learning a…
Fidelity estimator, randomized benchmarking and ZNE for quantum pulses
Jinglei Cheng, Zhiding Liang, Rui Yang +4
Most previous research focused on designing pulse programs without considering the performance of individual elements or the final fidelity. To evaluate the performance of quantum…
NeFT: Negative Feedback Training to Improve Robustness of Compute-In-Memory DNN Accelerators
Yifan Qin, Zheyu Yan, Dailin Gan +5
Compute-in-memory accelerators built upon non-volatile memory devices excel in energy efficiency and latency when performing deep neural network (DNN) inference, thanks to their in…
TinyML Design Contest for Life-Threatening Ventricular Arrhythmia Detection
Zhenge Jia, Dawei Li, Cong Liu +4
The first ACM/IEEE TinyML Design Contest (TDC) held at the 41st International Conference on Computer-Aided Design (ICCAD) in 2022 is a challenging, multi-month, research and develo…
Towards Advantages of Parameterized Quantum Pulses
Zhiding Liang, Jinglei Cheng, Zhixin Song +7
The advantages of quantum pulses over quantum gates have attracted increasing attention from researchers. Quantum pulses offer benefits such as flexibility, high fidelity, scalabil…
Fair Multi-Exit Framework for Facial Attribute Classification
Ching-Hao Chiu, Hao-Wei Chung, Yu-Jen Chen +2
Fairness has become increasingly pivotal in facial recognition. Without bias mitigation, deploying unfair AI would harm the interest of the underprivileged population. In this pape…
Development of A Real-time POCUS Image Quality Assessment and Acquisition Guidance System
Zhenge Jia, Yiyu Shi, Jingtong Hu +2
Point-of-care ultrasound (POCUS) is one of the most commonly applied tools for cardiac function imaging in the clinical routine of the emergency department and pediatric intensive…
Self-supervised On-device Federated Learning from Unlabeled Streams
Jiahe Shi, Yawen Wu, Dewen Zeng +3
The ubiquity of edge devices has led to a growing amount of unlabeled data produced at the edge. Deep learning models deployed on edge devices are required to learn from these unla…
Hybrid Gate-Pulse Model for Variational Quantum Algorithms
Zhiding Liang, Zhixin Song, Jinglei Cheng +8
Current quantum programs are mostly synthesized and compiled on the gate-level, where quantum circuits are composed of quantum gates. The gate-level workflow, however, introduces s…
Unsupervised Feature Clustering Improves Contrastive Representation Learning for Medical Image Segmentation
Yejia Zhang, Xinrong Hu, Nishchal Sapkota +2
Self-supervised instance discrimination is an effective contrastive pretext task to learn feature representations and address limited medical image annotations. The idea is to make…
ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images
An Zeng, Chunbiao Wu, Meiping Huang +10
Cardiovascular disease (CVD) accounts for about half of non-communicable diseases. Vessel stenosis in the coronary artery is considered to be the major risk of CVD. Computed tomogr…
TopGen: Topology-Aware Bottom-Up Generator for Variational Quantum Circuits
Jinglei Cheng, Hanrui Wang, Zhiding Liang +3
Variational Quantum Algorithms (VQA) are promising to demonstrate quantum advantages on near-term devices. Designing ansatz, a variational circuit with parameterized gates, is of p…
QuEst: Graph Transformer for Quantum Circuit Reliability Estimation
Hanrui Wang, Pengyu Liu, Jinglei Cheng +10
Among different quantum algorithms, PQC for QML show promises on near-term devices. To facilitate the QML and PQC research, a recent python library called TorchQuantum has been rel…
Improving Quantum Classifier Performance in NISQ Computers by Voting Strategy from Ensemble Learning
Ruiyang Qin, Zhiding Liang, Jinglei Cheng +2
Due to the immense potential of quantum computers and the significant computing overhead required in machine learning applications, the variational quantum classifier (VQC) has rec…
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis
Yawen Wu, Dewen Zeng, Zhepeng Wang +5
In dermatological disease diagnosis, the private data collected by mobile dermatology assistants exist on distributed mobile devices of patients. Federated learning (FL) can use de…
Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization
Gelei Xu, Yawen Wu, Jingtong Hu +1
Dermatological diseases pose a major threat to the global health, affecting almost one-third of the world's population. Various studies have demonstrated that early diagnosis and i…
Distributed Contrastive Learning for Medical Image Segmentation
Yawen Wu, Dewen Zeng, Zhepeng Wang +2
Supervised deep learning needs a large amount of labeled data to achieve high performance. However, in medical imaging analysis, each site may only have a limited amount of data an…
NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms
Zhiding Liang, Jinglei Cheng, Hang Ren +8
Variational quantum algorithms (VQAs) have demonstrated great potentials in the Noisy Intermediate Scale Quantum (NISQ) era. In the workflow of VQA, the parameters of ansatz are it…
Computing-In-Memory Neural Network Accelerators for Safety-Critical Systems: Can Small Device Variations Be Disastrous?
Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
Computing-in-Memory (CiM) architectures based on emerging non-volatile memory (NVM) devices have demonstrated great potential for deep neural network (DNN) acceleration thanks to t…