Search Results for author:
Found 17 papers, 13 papers with code
Task-unaware Lifelong Robot Learning with Retrieval-based Weighted Local Adaptation
Real-world environments require robots to continuously acquire new skills while retaining previously learned abilities, all without the need for clearly defined task boundaries.
Domain-Inspired Sharpness-Aware Minimization Under Domain Shifts
This paper presents a Domain-Inspired Sharpness-Aware Minimization (DISAM) algorithm for optimization under domain shifts.
Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping
Statistical heterogeneity severely limits the performance of federated learning (FL), motivating several explorations e. g., FedProx, MOON and FedDyn, to alleviate this problem.
Federated Learning with Bilateral Curation for Partially Class-Disjoint Data
Partially class-disjoint data (PCDD), a common yet under-explored data formation where each client contributes a part of classes (instead of all classes) of samples, severely challenges the performance of federated algorithms.
Fair Evaluation of Federated Learning Algorithms for Automated Breast Density Classification: The Results of the 2022 ACR-NCI-NVIDIA Federated Learning Challenge
The correct interpretation of breast density is important in the assessment of breast cancer risk.
Optimize Individualized Energy Delivery for Septic Patients Using Predictive Deep Learning Models: A Real World Study
Excessive energy intake increased mortality rapidly in the early period of the acute phase.
UniChest: Conquer-and-Divide Pre-training for Multi-Source Chest X-Ray Classification
Vision-Language Pre-training (VLP) that utilizes the multi-modal information to promote the training efficiency and effectiveness, has achieved great success in vision recognition of natural domains and shown promise in medical imaging diagnosis for the Chest X-Rays (CXRs).
Federated Domain Generalization With Generalization Adjustment
Federated Domain Generalization (FedDG) attempts to learn a global model in a privacy-preserving manner that generalizes well to new clients possibly with domain shift.
Secure Decentralized IoT Service Platform
The experimental results demonstrate that our solution can maintain high performance with a throughput of approximately 800 reads per second (RPS), 50–80 transactions per second (TPS), and a latency of 50 ms–2 s under light to moderate workloads.
Secure Decentralized IoT Service Platform Using Consortium
The experimental results demonstrate that our solution can maintain high performance with a throughput of approximately 800 reads per second (RPS), 50–80 transactions per second (TPS), and a latency of 50 ms–2 s under light to moderate workloads.
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The experimental results demonstrate that our solution can maintain high performance with a throughput of approximately 800 reads per second (RPS), 50–80 transactions per second (TPS), and a latency of 50 ms–2 s under light to moderate workloads.
Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding
Learning-based methods have shown promising performance for accelerating motion planning, but mostly in the setting of static environments.
Working memory inspired hierarchical video decomposition with transformative representations
Video decomposition is very important to extract moving foreground objects from complex backgrounds in computer vision, machine learning, and medical imaging, e. g., extracting moving contrast-filled vessels from the complex and noisy backgrounds of X-ray coronary angiography (XCA).
ReLF: Scalable Remote Live Forensics for Android
The world has witnessed the proliferation of mobile technologies as well as smartphone-related cybercrimes in recent years.
A Fourier-based Framework for Domain Generalization
Modern deep neural networks suffer from performance degradation when evaluated on testing data under different distributions from training data.
Powering Hands-on Cybersecurity Practices with Cloud Computing
In this work, we present a cloud-based platform named EZSetup for hands-on cybersecurity practice at scale and our experience of using it in class.
Isoflat: Flat Provider Network Multiplexing and Firewalling in OpenStack Cloud
Our evaluation results show that a provider network with Isoflat can achieve similar network performance as a flat or VLAN provider network.
