33 citations · 44 across the 13 of their papers we have counts for
13 papers
Harpoon: Generalised Manifold Guidance for Conditional Tabular Diffusion
Aditya Shankar, Yuandou Wang, Rihan Hai +1
Generating tabular data under conditions is critical to applications requiring precise control over the generative process. Existing methods rely on training-time strategies that d…
An Attention-based Feature Memory Design for Energy-Efficient Continual Learning
Yuandou Wang, Filip Gunnarsson, Rihan Hai
Tabular data streams are increasingly prevalent in real-time decision-making across healthcare, finance, and the Internet of Things, often generated and processed on resource-const…
Managing Federated Learning on Decentralized Infrastructures as a Reputation-based Collaborative Workflow
Yuandou Wang, Zhiming Zhao
Federated Learning (FL) has recently emerged as a collaborative learning paradigm that can train a global model among distributed participants without raw data exchange to satisfy…
CrowdAL: Towards a Blockchain-empowered Active Learning System in Crowd Data Labeling
Shaojie Hou, Yuandou Wang, Zhiming Zhao
Active Learning (AL) is a machine learning technique where the model selectively queries the most informative data points for labeling by human experts. Integrating AL with crowdso…
Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows
Rafael Ferreira da Silva, Deborah Bard, Kyle Chard +108
The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensit…
D-VRE: From a Jupyter-enabled Private Research Environment to Decentralized Collaborative Research Ecosystem
Yuandou Wang, Sheejan Tripathi, Siamak Farshidi +1
Today, scientific research is increasingly data-centric and compute-intensive, relying on data and models across distributed sources. However, it still faces challenges in the trad…
PriCE: Privacy-Preserving and Cost-Effective Scheduling for Parallelizing the Large Medical Image Processing Workflow over Hybrid Clouds
Yuandou Wang, Neel Kanwal, Kjersti Engan +3
Running deep neural networks for large medical images is a resource-hungry and time-consuming task with centralized computing. Outsourcing such medical image processing tasks to hy…
Towards Privacy-, Budget-, and Deadline-Aware Service Optimization for Large Medical Image Processing across Hybrid Clouds
Yuandou Wang, Neel Kanwal, Kjersti Engan +3
Efficiently processing medical images, such as whole slide images in digital pathology, is essential for timely diagnosing high-risk diseases. However, this demands advanced comput…
Towards Seamless Serverless Computing Across an Edge-Cloud Continuum
Emilian Simion, Yuandou Wang, Hsiang-ling Tai +2
Serverless computing has emerged as an attractive paradigm due to the efficiency of development and the ease of deployment without managing any underlying infrastructure. Neverthel…
Towards a privacy-preserving distributed cloud service for preprocessing very large medical images
Yuandou Wang, Neel Kanwal, Kjersti Engan +2
Digitized histopathology glass slides, known as Whole Slide Images (WSIs), are often several gigapixels large and contain sensitive metadata information, which makes distributed pr…
WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval
Zahra Tabatabaei, Yuandou Wang, Adrián Colomer +3
The paper proposes a Federated Content-Based Medical Image Retrieval (FedCBMIR) platform that utilizes Federated Learning (FL) to address the challenges of acquiring a diverse medi…
A Survey on Dataset Distillation: Approaches, Applications and Future Directions
Jiahui Geng, Zongxiong Chen, Yuandou Wang +5
Dataset distillation is attracting more attention in machine learning as training sets continue to grow and the cost of training state-of-the-art models becomes increasingly high.…
Notebook-as-a-VRE (NaaVRE): from private notebooks to a collaborative cloud virtual research environment
Zhiming Zhao, Spiros Koulouzis, Riccardo Bianchi +8
Virtual Research Environments (VREs) provide user-centric support in the lifecycle of research activities, e.g., discovering and accessing research assets, or composing and executi…