2 citations · 2 across the 2 of their papers we have counts for
3 papers
Constitutive State-Space Modeling of Path-Dependent Plasticity: A Resolution-Consistent and Parallelizable Computational Framework
Rui Barreira, Taylan Soydan, Francesco Scipione +2
Data-driven constitutive models for path-dependent plasticity are commonly formulated using nonlinear recurrent neural networks, whose sequential state evolution limits parallel tr…
TIDES: Implicit Time-Awareness in Selective State Space Models
Taylan Soydan, Miguel A. Bessa, Dirk Mohr +1
Selective state space models (SSMs), such as Mamba, achieve strong per-token expressivity by making the time discretization step $\TildeΔ$ a learned function of the input. However,…
S7: Selective and Simplified State Space Layers for Sequence Modeling
Taylan Soydan, Nikola Zubić, Nico Messikommer +2
A central challenge in sequence modeling is efficiently handling tasks with extended contexts. While recent state-space models (SSMs) have made significant progress in this area, t…