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Temporal Self-Distillation: Faster Inference in Discrete Diffusion Language Models
Shijian Xu, Andrea Miele, Metod Jazbec +3
Diffusion language models (dLLMs) promise fast inference by generating multiple tokens in parallel, but suffer severe performance degradation when parallel decoding is pushed too a…
Marginal Flow: a flexible and efficient framework for density estimation
Marcello Massimo Negri, Jonathan Aellen, Manuel Jahn +2
Current density modeling approaches suffer from at least one of the following shortcomings: expensive training, slow inference, approximate likelihood, mode collapse or architectur…
NIMO: a Nonlinear Interpretable MOdel
Shijian Xu, Marcello Massimo Negri, Volker Roth
Deep learning has achieved remarkable success across many domains, but it has also created a growing demand for interpretability in model predictions. Although many explainable mac…
Injective flows for star-like manifolds
Marcello Massimo Negri, Jonathan Aellen, Volker Roth
Normalizing Flows (NFs) are powerful and efficient models for density estimation. When modeling densities on manifolds, NFs can be generalized to injective flows but the Jacobian d…