1 citations · 4 across the 11 of their papers we have counts for
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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…
Monitoring Risks in Test-Time Adaptation
Mona Schirmer, Metod Jazbec, Christian A. Naesseth +1
Encountering shifted data at test time is a ubiquitous challenge when deploying predictive models. Test-time adaptation (TTA) methods address this issue by continuously adapting a…
Generative Uncertainty in Diffusion Models
Metod Jazbec, Eliot Wong-Toi, Guoxuan Xia +3
Diffusion models have recently driven significant breakthroughs in generative modeling. While state-of-the-art models produce high-quality samples on average, individual samples ca…
Temporal Test-Time Adaptation with State-Space Models
Mona Schirmer, Dan Zhang, Eric Nalisnick
Distribution shifts between training and test data are inevitable over the lifecycle of a deployed model, leading to performance decay. Adapting a model on test samples can help mi…
Fast yet Safe: Early-Exiting with Risk Control
Metod Jazbec, Alexander Timans, Tin Hadži Veljković +4
Scaling machine learning models significantly improves their performance. However, such gains come at the cost of inference being slow and resource-intensive. Early-exit neural net…