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 the Latent Space: Disentanglement with WAE-MMD and Programmable Priors
Quentin Fruytier, Akshay Malhotra, Shahab Hamidi-Rad +3
Learning disentangled representations, where semantic features are independently captured in a latent space, is dominated by Variational Auto Encoders (VAE) and Adversarial Trainin…
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…