8 citations · 36 across the 25 of their papers we have counts for
9 papers · 1 filter
Generative Diffusion Models of Stochastic Graph Signals
Yiğit Berkay Uslu, Samar Hadou, Sergio Rozada +2
Sampling stochastic signals supported on a graph underlies many graph machine learning tasks, including recommender systems, forecasting in financial markets, and wireless network…
Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks
Xingran Chen, Navid NaderiAlizadeh, Alejandro Ribeiro +1
We address real-time sampling and estimation of autoregressive Markovian sources in dynamic yet structurally similar multi-hop wireless networks. Each node caches samples from othe…
Graph Signal Generative Diffusion Models
Yigit Berkay Uslu, Samar Hadou, Sergio Rozada +2
We introduce U-shaped encoder-decoder graph neural networks (U-GNNs) for stochastic graph signal generation using denoising diffusion processes. The architecture learns node featur…
Generative Diffusion Models for Resource Allocation in Wireless Networks
Yigit Berkay Uslu, Samar Hadou, Shirin Saeedi Bidokhti +1
This paper proposes a supervised training algorithm for learning stochastic resource allocation policies with generative diffusion models (GDMs). We formulate the allocation proble…
Text + Sketch: Image Compression at Ultra Low Rates
Eric Lei, Yiğit Berkay Uslu, Hamed Hassani +1
Recent advances in text-to-image generative models provide the ability to generate high-quality images from short text descriptions. These foundation models, when pre-trained on bi…
Federated Neural Compression Under Heterogeneous Data
Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti
We discuss a federated learned compression problem, where the goal is to learn a compressor from real-world data which is scattered across clients and may be statistically heteroge…
Containing a spread through sequential learning: to exploit or to explore?
Xingran Chen, Hesam Nikpey, Jungyeol Kim +2
The spread of an undesirable contact process, such as an infectious disease (e.g. COVID-19), is contained through testing and isolation of infected nodes. The temporal and spatial…
Robust Graph Neural Networks via Probabilistic Lipschitz Constraints
Raghu Arghal, Eric Lei, Shirin Saeedi Bidokhti
Graph neural networks (GNNs) have recently been demonstrated to perform well on a variety of network-based tasks such as decentralized control and resource allocation, and provide…
Out-of-Distribution Robustness in Deep Learning Compression
Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti
In recent years, deep neural network (DNN) compression systems have proved to be highly effective for designing source codes for many natural sources. However, like many other mach…