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Found 17 papers, 8 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.
Maximum Weight Entropy
Under this paradigm, the epistemic uncertainty is described by the weight distribution of maximal entropy that produces neural networks "consistent" with the training observations.
A Meta-Generation framework for Industrial System Generation
This use case can be quickly generated and used as a benchmark.
Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification
To leverage the advantages of different patch sizes and resolutions, a multi patch-size classifier and a multi-resolution classifier are proposed.
Cancer-no cancer per image classification
Image Classification
Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification
We derive a simple and practical approach to produce such ensembles, based on an original anti-regularization term penalizing small weights and a control process of the weight increase which maintains the in-distribution loss under an acceptable threshold.
Fixed-budget online adaptive learning for physics-informed neural networks. Towards parameterized problem inference
We show that FBOAL is able to identify the high-gradient locations and even give better predictions for some physical fields than the classical PINNs with collocation points sampled on a pre-adapted finite element mesh built thanks to numerical expert knowledge.
To tree or not to tree? Assessing the impact of smoothing the decision boundaries
The approach we propose starts with the rigid decision boundaries of a seed Decision Tree (seed DT), which is used to initialize a Neural DT (NDT).
Fast and Accurate Importance Weighting for Correcting Sample Bias
Bias in datasets can be very detrimental for appropriate statistical estimation.
Physics-informed neural networks for non-Newtonian fluid thermo-mechanical problems: an application to rubber calendering process
We also investigate the capability of PINNs to identify unknown physical parameters from the measurements captured by sensors.
A Binded VAE for Inorganic Material Generation
Designing new industrial materials with desired properties can be very expensive and time consuming.
ADAPT : Awesome Domain Adaptation Python Toolbox
In this paper, we introduce the ADAPT library, an open source Python API providing the implementation of the main transfer learning and domain adaptation methods.
Discrepancy-Based Active Learning for Domain Adaptation
The goal of the paper is to design active learning strategies which lead to domain adaptation under an assumption of Lipschitz functions.
Model family selection for classification using Neural Decision Trees
In our approach, this is realized by progressively relaxing the decision boundaries of the initial decision trees (the RMs) as long as this is beneficial in terms of performance measured on an analyzed dataset.
Adversarial Weighting for Domain Adaptation in Regression
We present a novel instance-based approach to handle regression tasks in the context of supervised domain adaptation under an assumption of covariate shift.
A clusterwise supervised learning procedure based on aggregation of distances
Nowadays, many machine learning procedures are available on the shelve and may be used easily to calibrate predictive models on supervised data.
Aggregation using input-output trade-off
We also show on simulated examples that this procedure mixing inputs and outputs is still robust to high dimensional inputs.
Statistical learning for wind power : a modeling and stability study towards forecasting
We focus on wind power modeling using machine learning techniques.
