161 research outputs found
Explaining with Counter Visual Attributes and Examples
In this paper, we aim to explain the decisions of neural networks by
utilizing multimodal information. That is counter-intuitive attributes and
counter visual examples which appear when perturbed samples are introduced.
Different from previous work on interpreting decisions using saliency maps,
text, or visual patches we propose to use attributes and counter-attributes,
and examples and counter-examples as part of the visual explanations. When
humans explain visual decisions they tend to do so by providing attributes and
examples. Hence, inspired by the way of human explanations in this paper we
provide attribute-based and example-based explanations. Moreover, humans also
tend to explain their visual decisions by adding counter-attributes and
counter-examples to explain what is not seen. We introduce directed
perturbations in the examples to observe which attribute values change when
classifying the examples into the counter classes. This delivers intuitive
counter-attributes and counter-examples. Our experiments with both coarse and
fine-grained datasets show that attributes provide discriminating and
human-understandable intuitive and counter-intuitive explanations.Comment: arXiv admin note: substantial text overlap with arXiv:1910.07416,
arXiv:1904.0827
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
In this review, we examine the problem of designing interpretable and
explainable machine learning models. Interpretability and explainability lie at
the core of many machine learning and statistical applications in medicine,
economics, law, and natural sciences. Although interpretability and
explainability have escaped a clear universal definition, many techniques
motivated by these properties have been developed over the recent 30 years with
the focus currently shifting towards deep learning methods. In this review, we
emphasise the divide between interpretability and explainability and illustrate
these two different research directions with concrete examples of the
state-of-the-art. The review is intended for a general machine learning
audience with interest in exploring the problems of interpretation and
explanation beyond logistic regression or random forest variable importance.
This work is not an exhaustive literature survey, but rather a primer focusing
selectively on certain lines of research which the authors found interesting or
informative
Recent Advances in Variational Autoencoders With Representation Learning for Biomedical Informatics: A Survey
Variational autoencoders (VAEs) are deep latent space generative models that have been immensely successful in multiple exciting applications in biomedical informatics such as molecular design, protein design, medical image classification and segmentation, integrated multi-omics data analyses, and large-scale biological sequence analyses, among others. The fundamental idea in VAEs is to learn the distribution of data in such a way that new meaningful data with more intra-class variations can be generated from the encoded distribution. The ability of VAEs to synthesize new data with more representation variance at state-of-art levels provides hope that the chronic scarcity of labeled data in the biomedical field can be resolved. Furthermore, VAEs have made nonlinear latent variable models tractable for modeling complex distributions. This has allowed for efficient extraction of relevant biomedical information from learned features for biological data sets, referred to as unsupervised feature representation learning. In this article, we review the various recent advancements in the development and application of VAEs for biomedical informatics. We discuss challenges and future opportunities for biomedical research with respect to VAEs.https://doi.org/10.1109/ACCESS.2020.304830
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