This course is available on the BSc in Mathematics and Economics, BSc in Mathematics with Data Science, BSc in Mathematics with Economics, BSc in Mathematics, Statistics and Business, Erasmus ...
TL;DR: We introduce a scalable solver for the mechanistic neural networks that reduces time and memory costs from cubic/quadratic to linear in the sequence length, enabling long-horizon scientific ...
Abstract: Neural Ordinary Differential Equations (NODEs) revolutionize the way we view residual networks as solvers for initial value problems (IVPs), with layer depth serving as the time step. In ...
We also prove that the two sets of Maxwell equations only depend on the non-linear elations of the conformal group of ...
Stochastic differential equations (SDEs) are used to describe a wide variety of complex stochastic dynamical systems. Learning the hidden physics within SDEs is crucial for unraveling fundamental ...
Abstract: Most structures of deep neural networks (DNN) are with a fixed complexity of both computational cost (parameters and FLOPs) and the expressiveness. In this work, we experimentally ...
Recent advances in high-throughput microbiome profiling have generated expansive data sets that offer unprecedented ...
Small Medium Enterprise, Tangible Resources, Product Innovation Performance, Innovation, SME’s Age Share and Cite: Bakar, L. (2025) The Age-Resource Dynamic and Product Innovation Performance. Open ...
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