papers

Publications (5)

eess.IV2019

Towards improved lossy image compression: Human image reconstruction with public-domain images

Ashutosh Bhown, Soham Mukherjee, Sean Yang +5

Lossy image compression has been studied extensively in the context of typical loss functions such as RMSE, MS-SSIM, etc. However, compression at low bitrates generally produces un…

cs.DC2024

Supercharging Federated Learning with Flower and NVIDIA FLARE

Holger R. Roth, Daniel J. Beutel, Yan Cheng +13

Several open-source systems, such as Flower and NVIDIA FLARE, have been developed in recent years while focusing on different aspects of federated learning (FL). Flower is dedicate…

cs.LG2024

Empowering Federated Learning for Massive Models with NVIDIA FLARE

Holger R. Roth, Ziyue Xu, Yuan-Ting Hsieh +12

In the ever-evolving landscape of artificial intelligence (AI) and large language models (LLMs), handling and leveraging data effectively has become a critical challenge. Most stat…

cs.LG2023

NVIDIA FLARE: Federated Learning from Simulation to Real-World

Holger R. Roth, Yan Cheng, Yuhong Wen +20

Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVI…

cs.DL2019

Delineating Knowledge Domains in the Scientific Literature Using Visual Information

Sean Yang, Po-shen Lee, Jevin D. West +1

Figures are an important channel for scientific communication, used to express complex ideas, models and data in ways that words cannot. However, this visual information is mostly…