3 papers
cs.CL2026
PIG: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models
Quentin Fruytier, Anton Xue, Sanjay Shakkottai +1
Diffusion language models accelerate generation by decoding multiple masked positions per forward pass. However, accuracy is known to drop when parallel decoding trajectories devia…
cs.LG2025
Sculpting Latent Spaces With MMD: Disentanglement With Programmable Priors
Quentin Fruytier, Akshay Malhotra, Shahab Hamidi-Rad +3
Learning disentangled representations, where distinct factors of variation are captured by independent latent variables, is a central goal in machine learning. The dominant approac…
cs.LG2024
Learning Mixtures of Experts with EM: A Mirror Descent Perspective
Quentin Fruytier, Aryan Mokhtari, Sujay Sanghavi
Classical Mixtures of Experts (MoE) are Machine Learning models that involve partitioning the input space, with a separate "expert" model trained on each partition. Recently, MoE-b…