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Found 8 papers, 7 papers with code
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.
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.
Fast and Accurate Importance Weighting for Correcting Sample Bias
Bias in datasets can be very detrimental for appropriate statistical estimation.
Unsupervised Domain Adaptation for Constraining Star Formation Histories
The prevalent paradigm of machine learning today is to use past observations to predict future ones.
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.
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.
