4 papers · 1 filter
When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatch
Maria V. Salvador, Ana F. Sequeira, Jaime S. Cardoso
Missing data is a pervasive challenge in machine learning, often degrading predictive performance and complicating model deployment. Most existing approaches treat imputation as a…
AdaFlash: Adaptive Speculative Decoding via On-Policy Distilled Diffusion Drafters
Yu-Yang Qian, Hao-Cong Wu, Chen Chen +4
Speculative decoding, in which a lightweight draft model first generates a draft sequence that is then verified by the target model, has become a prevalent paradigm for acceleratin…
When Drafts Evolve: Speculative Decoding Meets Online Learning
Yu-Yang Qian, Hao-Cong Wu, Yichao Fu +2
Speculative decoding has emerged as a widely adopted paradigm for accelerating large language model inference, where a lightweight draft model rapidly generates candidate tokens th…
Choose Your Model Size: Any Compression of Large Language Models Without Re-Computation
Martin Genzel, Patrick Putzky, Pengfei Zhao +5
The adoption of Foundation Models in resource-constrained environments remains challenging due to their large size and inference costs. A promising way to overcome these limitation…