2 citations · 4 across the 18 of their papers we have counts for
8 papers · 1 filter
Enhancing Diffusion Language Models with Autoregressive Post-Training Weights
Yiming Qin, Ke Wang, Amel Abdelraheem +2
Diffusion language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) language models, offering flexible token-update orders and parallel decoding. Recen…
Multiclass Speech Classification Under Noise Disparity
Mahdi Amiri, Sayantan Biswas, Mingchi Hou +2
We investigate noise disparity in multi-class speech classification tasks and develop a strategy to prevent classifiers from exploiting class-dependent noise characteristics. Build…
Augmenting Large Audio Language Models with Low-Level Acoustic Features for Dysarthric Speech Detection
Mahdi Amiri, Hatef Otroshi Shahreza, Pascal Frossard +1
Automatic dysarthric speech detection approaches can support traditional clinical diagnosis, which relies on costly and time-consuming evaluation by a speech and language pathologi…
Recursive Scaling in Masked Diffusion Models
Alba Carballo-Castro, Julianna Piskorz, Paulius Rauba +2
Masked diffusion models (MDMs) generate sequences by iteratively refining a partially masked state and committing tokens in parallel. We introduce recursion in MDMs and propose new…
PriFT: Guiding Supervised Fine-Tuning with Pretrained Priors
Ke Wang, Shuangqi Li, Mathieu Salzmann +1
Supervised fine-tuning (SFT) is an efficient approach for downstream task adaptation and often serves as the initialization stage for reinforcement learning (RL), but it can show w…
Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability
Mathieu Cyrille Simon, Pascal Frossard, Christophe De Vleeschouwer
This paper explores unsupervised disentangled representation learning from a functional perspective. We define latent concepts as factors that influence observations through locall…
Fixed-Point Masked Generative Modeling
Andrea Miele, Yiming Qin, Alba Carballo-Castro +2
Masked Generative Models (MGMs) enable parallel decoding and achieve strong performance across modalities, but require full-sequence bidirectional transformers at every step, makin…
Model soups need only one ingredient
Alireza Abdollahpoorrostam, Nikolaos Dimitriadis, Adam Hazimeh +1
Fine-tuning large pre-trained models on a target distribution often improves in-distribution (ID) accuracy, but at the cost of out-of-distribution (OOD) robustness as representatio…