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Parallel Language Models via Variational Decoding
Felix Draxler, Robin C. Geyer, Daniel Geyfman +13
Autoregressive language models emit one token per forward pass. This causes high inference latency, in particular as models are scaled up for quality. In this paper, we introduce P…
Wavelet Flow Matching for Time Series
Lucas Poinsignon, Jorge da Silva Goncalves, Jorge da Silva Gonçalves +3
Synthetic time series are increasingly used for data augmentation, privacy-preserving data sharing, and downstream model development, yet faithfully reproducing both multi-scale te…
Cyclostationary Phase Conditioning for Medical Time Series Diffusion
Samuel Ruiperez-Campillo, Michele Copetti, Jorge da Silva Goncalves +3
Many physiological time series, such as cardiac and brain recordings, exhibit cyclostationarity: their statistics vary periodically with an underlying cycle phase. Corruption from…
You Only Train Once: Differentiable Subset Selection for Omics Data
Daphné Chopard, Jorge da Silva Gonçalves, Irene Cannistraci +2
Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling.…
Structured Generations: Using Hierarchical Clusters to guide Diffusion Models
Jorge da Silva Goncalves, Laura Manduchi, Moritz Vandenhirtz +1
This paper introduces Diffuse-TreeVAE, a deep generative model that integrates hierarchical clustering into the framework of Denoising Diffusion Probabilistic Models (DDPMs). The p…