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Found 5 papers, 1 papers with code
Spurious Correlations and Where to Find Them
Spurious correlations occur when a model learns unreliable features from the data and are a well-known drawback of data-driven learning.
On the Biometric Capacity of Generative Face Models
Our capacity estimates indicate that (a) under ArcFace representation at a false acceptance rate (FAR) of 0. 1%, StyleGAN3 and DCFace have a capacity upper bound of $1. 43\times10^6$ and $1. 190\times10^4$, respectively; (b) the capacity reduces drastically as we lower the desired FAR with an estimate of $1. 796\times10^4$ and $562$ at FAR of 1% and 10%, respectively, for StyleGAN3; (c) there is no discernible disparity in the capacity w. r. t gender; and (d) for some generative models, there is an appreciable disparity in the capacity w. r. t age.
Neuro-DynaStress: Predicting Dynamic Stress Distributions in Structural Components
Therefore, to reduce computational cost while preserving accuracy, a deep learning model, Neuro-DynaStress, is proposed to predict the entire sequence of stress distribution based on finite element simulations using a partial differential equation (PDE) solver.
Physics Informed Neural Network for Dynamic Stress Prediction
Therefore, to reduce computational cost while maintaining accuracy, a Physics Informed Neural Network (PINN), PINN-Stress model, is proposed to predict the entire sequence of stress distribution based on Finite Element simulations using a partial differential equation (PDE) solver.
Neural Architecture Transfer
At the same time, the architecture search and transfer is orders of magnitude more efficient than existing NAS methods.
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