47 citations · 95 across the 7 of their papers we have counts for
7 papers
GS-NFS: Bandwidth-adaptive Streaming of Dynamic Gaussian Splats and Point Clouds
Rajrup Ghosh, Haodong Wang, Haoran Hong +6
Dynamic 3D Gaussian Splatting (3DGS) holds great promise as a 3D video streaming technology since it can represent complex 3D scenes with high fidelity. In this approach, every fra…
DeGenTWeb: A First Look at MGT-dominant Websites
Sichang Steven He, Calvin Ardi, Ramesh Govindan +1
Many news reports have claimed that machine-generated text (MGT) from large language models (LLMs) is taking over the web. However, these claims are typically not based on a repres…
Preprint: Poster: Did I Just Browse A Website Written by LLMs?
Sichang Steven He, Ramesh Govindan, Harsha V. Madhyastha
Increasingly, web content is automatically generated by large language models (LLMs) with little human input. We call this "LLM-dominant" content. Since LLMs plagiarize and halluci…
A Collaborative Process Parameter Recommender System for Fleets of Networked Manufacturing Machines -- with Application to 3D Printing
Weishi Wang, Sicong Guo, Chenhuan Jiang +5
Fleets of networked manufacturing machines of the same type, that are collocated or geographically distributed, are growing in popularity. An excellent example is the rise of 3D pr…
Auxo: Efficient Federated Learning via Scalable Client Clustering
Jiachen Liu, Fan Lai, Yinwei Dai +3
Federated learning (FL) is an emerging machine learning (ML) paradigm that enables heterogeneous edge devices to collaboratively train ML models without revealing their raw data to…
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
Fan Lai, Yinwei Dai, Sanjay S. Singapuram +4
We present FedScale, a federated learning (FL) benchmarking suite with realistic datasets and a scalable runtime to enable reproducible FL research. FedScale datasets encompass a w…
Oort: Efficient Federated Learning via Guided Participant Selection
Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha +1
Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that enables in-situ model training and testing on edge data. Despite having the same end goal…