5 papers
Unlocking Realism and Interpretability in Wireless Channel Synthesis: A Physics-Guided Generative Approach
Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad +3
In recent years, machine learning (ML) methods have become increasingly popular for wireless communication systems. These require large amounts of data reflecting the behavior of r…
From Fewer Samples to Fewer Bits: Reframing Dataset Distillation as Joint Optimization of Precision and Compactness
My H. Dinh, Aditya Sant, Akshay Malhotra +2
Dataset Distillation (DD) compresses large datasets into compact synthetic ones that maintain training performance. However, current methods mainly target sample reduction, with li…
Sculpting Latent Spaces With MMD: Disentanglement With Programmable Priors
Quentin Fruytier, Akshay Malhotra, Shahab Hamidi-Rad +3
Learning disentangled representations, where distinct factors of variation are captured by independent latent variables, is a central goal in machine learning. The dominant approac…
Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad +3
In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML s…
Physics-Informed Generative Approaches for Wireless Channel Modeling
Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad +3
In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML s…