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Found 29 papers, 18 papers with code
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks
Despite significant progress, evaluation of explainable artificial intelligence remains elusive and challenging.
Unity by Diversity: Improved Representation Learning in Multimodal VAEs
Such architectures impose hard constraints on the model.
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?
Recently, interpretable machine learning has re-explored concept bottleneck models (CBM), comprising step-by-step prediction of the high-level concepts from the raw features and the target variable from the predicted concepts.
Benchmarking the Fairness of Image Upsampling Methods
Recent years have witnessed a rapid development of deep generative models for creating synthetic media, such as images and videos.
This Reads Like That: Deep Learning for Interpretable Natural Language Processing
Prototype learning, a popular machine learning method designed for inherently interpretable decisions, leverages similarities to learned prototypes for classifying new data.
The Mixtures and the Neural Critics: On the Pointwise Mutual Information Profiles of Fine Distributions
Mutual information quantifies the dependence between two random variables and remains invariant under diffeomorphisms.
M(otion)-mode Based Prediction of Ejection Fraction using Echocardiograms
Early detection of cardiac dysfunction through routine screening is vital for diagnosing cardiovascular diseases.
Beyond Normal: On the Evaluation of Mutual Information Estimators
Mutual information is a general statistical dependency measure which has found applications in representation learning, causality, domain generalization and computational biology.
(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Comprehensibility
Ante-hoc interpretability has become the holy grail of explainable artificial intelligence for high-stakes domains such as healthcare; however, this notion is elusive, lacks a widely-accepted definition and depends on the operational context.
Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss
We propose Signal is Harder (SiH), a variational-autoencoder-based method that simultaneously trains a biased and unbiased classifier using a novel, disentangling reweighting scheme inspired by the focal loss.
Differentiable Random Partition Models
Partitioning a set of elements into an unknown number of mutually exclusive subsets is essential in many machine learning problems.
Identifiability Results for Multimodal Contrastive Learning
Our work generalizes previous identifiability results by redefining the generative process in terms of distinct mechanisms with modality-specific latent variables.
Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis
Appendicitis is among the most frequent reasons for pediatric abdominal surgeries.
Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge
Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets.
On the Identifiability and Estimation of Causal Location-Scale Noise Models
We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the effect $Y$ can be written as a function of the cause $X$ and a noise source $N$ independent of $X$, which may be scaled by a positive function $g$ over the cause, i. e., $Y = f(X) + g(X)N$.
Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods
In addition, we compare several intra- and post-processing approaches applied to debiasing deep chest X-ray classifiers.
Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models
The introduced method takes advantage of the periodic nature of the heart cycle to learn three variants of a variational latent trajectory model (TVAE).
How Robust is Unsupervised Representation Learning to Distribution Shift?
As such, there is a lack of insight on the robustness of the representations learned from unsupervised methods, such as self-supervised learning (SSL) and auto-encoder based algorithms (AE), to distribution shift.
Learning Group Importance using the Differentiable Hypergeometric Distribution
We introduce reparameterizable gradients to learn the importance between groups and highlight the advantage of explicitly learning the size of subsets in two typical applications: weakly-supervised learning and clustering.
On the Limitations of Multimodal VAEs
Multimodal variational autoencoders (VAEs) have shown promise as efficient generative models for weakly-supervised data.
Deep Conditional Gaussian Mixture Model for Constrained Clustering
Constrained clustering has gained significant attention in the field of machine learning as it can leverage prior information on a growing amount of only partially labeled data.
A Deep Variational Approach to Clustering Survival Data
In this work, we study the problem of clustering survival data $-$ a challenging and so far under-explored task.
Generalized Multimodal ELBO
Multiple data types naturally co-occur when describing real-world phenomena and learning from them is a long-standing goal in machine learning research.
Interpretable Models for Granger Causality Using Self-explaining Neural Networks
Exploratory analysis of time series data can yield a better understanding of complex dynamical systems.
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
In this review, we examine the problem of designing interpretable and explainable machine learning models.
Counterfactual Explanation
Explainable artificial intelligence
+1
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
Learning from different data types is a long-standing goal in machine learning research, as multiple information sources co-occur when describing natural phenomena.
Generation of Differentially Private Heterogeneous Electronic Health Records
In this work, we explore using Generative Adversarial Networks to generate synthetic, heterogeneous EHRs with the goal of using these synthetic records in place of existing data sets for downstream classification tasks.
Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies
To demonstrate the utility of our approach, we perform an association study of clinical features with somatic mutation profiles from 4, 007 cancer patients and their tumors.
Probabilistic Clustering of Time-Evolving Distance Data
We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points.
