What is the relationship between the number of active experts per token in SoftMoE and spatial reasoning perfo

Abstract

Abstract: Abstract Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself. PINNs are nowadays used to solve PDEs, fractional equations, integral-differential equations, and stochastic PDEs. This novel methodology has arisen as a multi-task learning framework in which a NN must fit observed data while reducing a PDE residual. This article provides a comprehensive review of the literature on PINNs: while the primary goal of the study was to characterize these networks and their re

Research Question

What is the relationship between the number of active experts per token in SoftMoE and spatial reasoning performance on SEED-Bench across varying image resolutions?

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