7 papers · 1 filter
Size Transferability of Graph Transformers with Convolutional Positional Encodings
Javier Porras-Valenzuela, Zhiyang Wang, Xiaotao Shang +2
Transformers have achieved remarkable success across domains, motivating the rise of Graph Transformers (GTs) as attention-based architectures for graph-structured data. A key desi…
RKHS Representation of Algebraic Convolutional Filters with Integral Operators
Alejandro Parada-Mayorga, Alejandro Ribeiro, Juan Bazerque
Integral operators play a central role in signal processing, underpinning classical convolution, and filtering on continuous network models such as graphons. While these operators…
Learning Policy Representations for Steerable Behavior Synthesis
Beiming Li, Sergio Rozada, Alejandro Ribeiro
Given a Markov decision process (MDP), we seek to learn representations for a range of policies to facilitate behavior steering at test time. As policies of an MDP are uniquely det…
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…
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…
Robust Stochastically-Descending Unrolled Networks
Samar Hadou, Navid NaderiAlizadeh, Alejandro Ribeiro
Deep unrolling, or unfolding, is an emerging learning-to-optimize method that unrolls a truncated iterative algorithm in the layers of a trainable neural network. However, the conv…