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Flash PD-SSM: Memory-Optimized Structured Sparse State-Space Models
Aleksandar TerziÄ, Francesco Carzaniga, Nicolas Menet +4
State-space models (SSMs) face a fundamental trade-off between efficiency and expressivity that is mainly dictated by the structure of the model's transition matrix. Unstructured t…
POETS: Uncertainty-Aware LLM Optimization via Compute-Efficient Policy Ensembles
Nicolas Menet, Andreas Krause, Abbas Rahimi
Balancing exploration and exploitation is a core challenge in sequential decision-making and black-box optimization. We introduce POETS (licy nsembles for…
Soft-Masked Diffusion Language Models
Michael Hersche, Samuel Moor-Smith, Thomas Hofmann +1
Diffusion models have demonstrated strong potential in language modeling, offering various advantages over traditional autoregressive approaches. Their ability to generate and revi…
Thompson Sampling via Fine-Tuning of LLMs
Nicolas Menet, Aleksandar TerziÄ, Michael Hersche +2
Bayesian optimization in large unstructured discrete spaces is often hindered by the computational cost of maximizing acquisition functions due to the absence of gradients. We prop…
A Composable Channel-Adaptive Architecture for Seizure Classification
Francesco Carzaniga, Michael Hersche, Kaspar Schindler +1
Objective: We develop a channel-adaptive (CA) architecture that seamlessly processes multi-variate time-series with an arbitrary number of channels, and in particular intracranial…
Scalable Evaluation and Neural Models for Compositional Generalization
Giacomo Camposampiero, Pietro Barbiero, Michael Hersche +2
Compositional generalization-a key open challenge in modern machine learning-requires models to predict unknown combinations of known concepts. However, assessing compositional gen…