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Found 6 papers, 3 papers with code
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees
Gaussian processes (GPs) are a Bayesian machine learning approach widely used to construct surrogate models for the uncertainty quantification of computer simulation codes in industrial applications.
Shape Analysis of Euclidean Curves under Frenet-Serret Framework
Geometric frameworks for analyzing curves are common in applications as they focus on invariant features and provide visually satisfying solutions to standard problems such as computing invariant distances, averaging curves, or registering curves.
Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series
We propose a robust principal component analysis (RPCA) framework to recover low-rank and sparse matrices from temporal observations.
MAPIE: an open-source library for distribution-free uncertainty quantification
Estimating uncertainties associated with the predictions of Machine Learning (ML) models is of crucial importance to assess their robustness and predictive power.
Forgetting leads to chaos in attractor networks
We show that for a forgetting time scale that optimizes storage capacity, the qualitative features of the network's memory retrieval dynamics are age-dependent: most recent memories are retrieved as fixed-point attractors while older memories are retrieved as chaotic attractors characterized by strong heterogeneity and temporal fluctuations.
Bayesian reconstruction of memories stored in neural networks from their connectivity
The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions.
