Hugging Face Trending Papers
Physics-informed VAE-EVT for Tail Aware Radio Map Prediction
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arXiv AI
Aug 18
Physics-informed VAE-EVT for Tail Aware Radio Map Prediction
arXiv:2608. 15314v1 Announce Type: new Abstract: Ultra-reliable low-latency communication (URLLC) requires precise identification of spatial regions where the signal-to-noise ratio (SNR) falls below an outage threshold.
arXiv AI
Jun 4
Markov Chain Decoders Overcome the Heavy-Tail Limitations of Lipschitz Generative Models
arXiv:2605. 18931v2 Announce Type: replace-cross Abstract: Heavy-tailed distributions are prevalent in performance evaluation, network traffic, and risk modeling.
arXiv Machine Learning
Jul 10
RadioDiff-v2: Generative Angular Radio Maps for Multi-Beam Selection and Localization
arXiv:2607. 08045v1 Announce Type: cross Abstract: Angular radio maps describe the received-power distribution over the angle of arrival and underpin beam selection and receiver localization in sixth-generation (6G) networks.
arXiv Machine Learning
Jul 14
Physics-Aware Conditional SetGAN for Spatially Consistent Multi-User TR 38.901 Channel Generation
arXiv:2607. 11429v1 Announce Type: new Abstract: TR 38.
arXiv Machine Learning
Aug 13
Fine-Tuning Generative Models for Extreme Events via CVaR-Penalized Wasserstein Gradient Flows
arXiv:2608. 11544v1 Announce Type: cross Abstract: We propose CVaR-penalized Generative Particle Algorithm (CVaR-GPA), a robust, tail-agnostic algorithm for fine-tuning generative models to learn heavy-tailed distributions and capture extreme events, requiring no prior knowledge or estimation of the target's tail characteristics.
arXiv Computer Vision
Sep 22
Bridging Reconstruction and Generation: A Latent Distribution Perspective on Evaluation and Improvement
The paper investigates why reconstruction quality in latent generative models does not always predict generative performance, attributing the issue to a mismatch between encoder-induced and generation-time latent distributions. It introduces Generation‑Aware Reconstruction (GAR), a method that perturbs encoder latents with noise and denoises them through the generative model before decoding, creating a continuous trajectory that reveals how the decoder behaves across latent spaces. The resulting GAR‑FID metric correlates strongly with generation FID, and using intermediate GAR latents for decoder adaptation consistently improves generative quality across different model scales.