research
∙
08/20/2023
Rethinking Client Drift in Federated Learning: A Logit Perspective
Federated Learning (FL) enables multiple clients to collaboratively lear...
share
research
∙
08/20/2023
Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction
While multi-modal learning has been widely used for MRI reconstruction, ...
research
∙
05/09/2023
DeepFire2: A Convolutional Spiking Neural Network Accelerator on FPGAs
Brain-inspired spiking neural networks (SNNs) replace the multiply-accum...
research
∙
03/23/2023
Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging
Deep learning models have shown promising performance in the field of di...
research
∙
01/30/2023
TrFedDis: Trusted Federated Disentangling Network for Non-IID Domain Feature
Federated learning (FL), as an effective decentralized distributed learn...
research
∙
01/01/2023
EvidenceCap: Towards trustworthy medical image segmentation via evidential identity cap
Medical image segmentation (MIS) is essential for supporting disease dia...
research
∙
12/01/2022
Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images
Focusing on the complicated pathological features, such as blurred bound...
research
∙
11/10/2022
Desire Backpropagation: A Lightweight Training Algorithm for Multi-Layer Spiking Neural Networks based on Spike-Timing-Dependent Plasticity
Spiking neural networks (SNN) are a viable alternative to conventional a...
research
∙
06/06/2022
A Resource-efficient Spiking Neural Network Accelerator Supporting Emerging Neural Encoding
Spiking neural networks (SNNs) recently gained momentum due to their low...
research
∙
12/01/2021
Optimizing for In-memory Deep Learning with Emerging Memory Technology
In-memory deep learning computes neural network models where they are st...
research
∙
11/19/2021
E3NE: An End-to-End Framework for Accelerating Spiking Neural Networks with Emerging Neural Encoding on FPGAs
Compiler frameworks are crucial for the widespread use of FPGA-based dee...
research
∙
10/07/2021
Efficient Sharpness-aware Minimization for Improved Training of Neural Networks
Overparametrized Deep Neural Networks (DNNs) often achieve astounding pe...
research
∙
07/10/2021
Few-Shot Domain Adaptation with Polymorphic Transformers
Deep neural networks (DNNs) trained on one set of medical images often e...
research
∙
06/08/2021
Adversarial Semantic Hallucination for Domain Generalized Semantic Segmentation
Convolutional neural networks may perform poorly when the test and train...
research
∙
05/20/2021
Medical Image Segmentation using Squeeze-and-Expansion Transformers
Medical image segmentation is important for computer-aided diagnosis. Go...
research
∙
06/08/2020
EDCompress: Energy-Aware Model Compression with Dataflow
Edge devices demand low energy consumption, cost and small form factor. ...
research
∙
05/25/2020
Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learning
Countries across the globe have been pushing strict regulations on the p...
research
∙
04/24/2020
RAIN: Robust and Accurate Classification Networks with Randomization and Enhancement
Along with the extensive applications of CNN models for classification, ...
research
∙
04/12/2020
Feature Lenses: Plug-and-play Neural Modules for Transformation-Invariant Visual Representations
Convolutional Neural Networks (CNNs) are known to be brittle under vario...
research
∙
03/17/2020
Privacy-preserving Weighted Federated Learning within Oracle-Aided MPC Framework
This paper studies privacy-preserving weighted federated learning within...
research
∙
07/04/2019
Multi-Instance Multi-Scale CNN for Medical Image Classification
Deep learning for medical image classification faces three major challen...
research
∙
06/15/2016
Multi-Modal Hybrid Deep Neural Network for Speech Enhancement
Deep Neural Networks (DNN) have been successful in en- hancing noisy spe...
research
∙
05/13/2016
