8 papers · 1 filter
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…
Structured Sparse Transition Matrices to Enable State Tracking in State-Space Models
Aleksandar TerziÄ, Nicolas Menet, Michael Hersche +2
Modern state-space models (SSMs) often utilize transition matrices which enable efficient computation but pose restrictions on the model's expressivity, as measured in terms of the…
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…
I-RAVEN-X: Benchmarking Generalization and Robustness of Analogical and Mathematical Reasoning in Large Language and Reasoning Models
Giacomo Camposampiero, Michael Hersche, Roger Wattenhofer +2
We introduce I-RAVEN-X, a symbolic benchmark designed to evaluate generalization and robustness in analogical and mathematical reasoning for Large Language Models (LLMs) and Large…
A foundation model with multi-variate parallel attention to generate neuronal activity
Francesco Carzaniga, Michael Hersche, Abu Sebastian +2
Learning from multi-variate time-series with heterogeneous channel configurations remains a fundamental challenge for deep neural networks, particularly in clinical domains such as…
On the Expressiveness and Length Generalization of Selective State-Space Models on Regular Languages
Aleksandar TerziÄ, Michael Hersche, Giacomo Camposampiero +3
Selective state-space models (SSMs) are an emerging alternative to the Transformer, offering the unique advantage of parallel training and sequential inference. Although these mode…