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Found 4 papers, 1 papers with code
Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection
We present an end-to-end differentiable neural network architecture to perform anomaly detection in multivariate time series by incorporating a Sequential Probability Ratio Test on the prediction residual.
A geometric interpretation of stochastic gradient descent using diffusion metrics
Stochastic gradient descent (SGD) is a key ingredient in the training of deep neural networks and yet its geometrical significance appears elusive.
Second-order Shape Optimization for Geometric Inverse Problems in Vision
We develop a method for optimization in shape spaces, i. e., sets of surfaces modulo re-parametrization.
Volumetric Reconstruction Applied to Perceptual Studies of Size and Weight
We explore the application of volumetric reconstruction from structured-light sensors in cognitive neuroscience, specifically in the quantification of the size-weight illusion, whereby humans tend to systematically perceive smaller objects as heavier.
