25 citations · 25 across the 8 of their papers we have counts for
12 papers · 1 filter
Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations
H. ÃaÄrı Bilgi, Lydia Y. Chen, Kubilay Atasu
Graph Neural Networks (GNNs) have seen significant advances in recent years, yet their application to multigraphs, where parallel edges exist between the same pair of nodes, remain…
MPQ-Diff: Mixed Precision Quantization for Diffusion Models
Rocco Manz Maruzzelli, Basile Lewandowski, Lydia Y. Chen
Diffusion models (DMs) generate remarkable high quality images via the stochastic denoising process, which unfortunately incurs high sampling time. Post-quantizing the trained diff…
Duwak: Dual Watermarks in Large Language Models
Chaoyi Zhu, Jeroen Galjaard, Pin-Yu Chen +1
As large language models (LLM) are increasingly used for text generation tasks, it is critical to audit their usages, govern their applications, and mitigate their potential harms.…
TabVFL: Improving Latent Representation in Vertical Federated Learning
Mohamed Rashad, Zilong Zhao, Jeremie Decouchant +1
Autoencoders are popular neural networks that are able to compress high dimensional data to extract relevant latent information. TabNet is a state-of-the-art neural network model d…
Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients
Yuncong Zuo, Bart Cox, Lydia Y. Chen +1
Federated learning (FL) systems enable multiple clients to train a machine learning model iteratively through synchronously exchanging the intermediate model weights with a single…
Asynchronous Byzantine Federated Learning
Bart Cox, Abele MÄlan, Lydia Y. Chen +1
Federated learning (FL) enables a set of geographically distributed clients to collectively train a model through a server. Classically, the training process is synchronous, but ca…