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Found 11 papers, 3 papers with code
Getting a CLUE: A Method for Explaining Uncertainty Estimates
Both uncertainty estimation and interpretability are important factors for trustworthy machine learning systems.
Continual Learning with Adaptive Weights (CLAW)
Approaches to continual learning aim to successfully learn a set of related tasks that arrive in an online manner.
Conditional Learning of Fair Representations
We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups in the classification setting.
Exploring Properties of the Deep Image Prior
Finally, we examine the adversarial invariancy of the early DIP outputs, and hypothesize that these outputs may remove non-robust image features.
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
We use a generative model which takes as input the representation in an existing (generative or discriminative) model, weakly supervised by limited side information.
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input.
Learning Bayesian Networks with Incomplete Data by Augmentation
To the best of our knowledge, this is the first exact algorithm for this problem.
Automatic Variational ABC
Approximate Bayesian Computation (ABC) is a framework for performing likelihood-free posterior inference for simulation models.
A Weakly Supervised Learning Approach based on Spectral Graph-Theoretic Grouping
In this study, a spectral graph-theoretic grouping strategy for weakly supervised classification is introduced, where a limited number of labelled samples and a larger set of unlabelled samples are used to construct a larger annotated training set composed of strongly labelled and weakly labelled samples.
Generative Multiple-Instance Learning Models For Quantitative Electromyography
We present a comprehensive study of the use of generative modeling approaches for Multiple-Instance Learning (MIL) problems.
