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Found 20 papers, 7 papers with code
From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition
The advancement of deep learning in human activity recognition (HAR) using 3D skeleton data is critical for applications in healthcare, security, sports, and human-computer interaction.
AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture Search
This paper introduces AutoGCN, a generic Neural Architecture Search (NAS) algorithm for Human Activity Recognition (HAR) using Graph Convolution Networks (GCNs).
Ranked #56 on
Skeleton Based Action Recognition
on NTU RGB+D
Lecture Notes in Probabilistic Diffusion Models
The diffusion model learns the data manifold to which the original and thus the reconstructed data samples belong, by training on a large number of data points.
Information based explanation methods for deep learning agents -- with applications on large open-source chess models
With large chess-playing neural network models like AlphaZero contesting the state of the art within the world of computerised chess, two challenges present themselves: The question of how to explain the domain knowledge internalised by such models, and the problem that such models are not made openly available.
Concept backpropagation: An Explainable AI approach for visualising learned concepts in neural network models
Neural network models are widely used in a variety of domains, often as black-box solutions, since they are not directly interpretable for humans.
Against Algorithmic Exploitation of Human Vulnerabilities
Decisions such as which movie to watch next, which song to listen to, or which product to buy online, are increasingly influenced by recommender systems and user models that incorporate information on users' past behaviours, preferences, and digitally created content.
Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable Concepts
Self-trained autonomous agents developed using machine learning are showing great promise in a variety of control settings, perhaps most remarkably in applications involving autonomous vehicles.
Predicting tacrolimus exposure in kidney transplanted patients using machine learning
Tacrolimus is one of the cornerstone immunosuppressive drugs in most transplantation centers worldwide following solid organ transplantation.
Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations
Deep learning has in recent years achieved immense success in all areas of computer vision and has the potential of assisting medical doctors in analyzing visual content for disease and other abnormalities.
Interpretable machine learning in Physics
Adding interpretability to multivariate methods creates a powerful synergy for exploring complex physical systems with higher order correlations while bringing about a degree of clarity in the underlying dynamics of the system.
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization
The main contributions of this work are (1) significantly improving both the accuracy and the build time of a greedy approach for building LMTs by introducing ordering of features in the splitting of the tree, (2) giving an overview of the characteristics of the seafarer/operator and the developer as two different end-users of the agent and receiver of the explanations, and (3) suggesting a visualization of the docking agent, the environment, and the feature attributions given by the LMT for when the developer is the end-user of the system, and another visualization for when the seafarer or operator is the end-user, based on their different characteristics.
Explainable artificial intelligence
Reinforcement Learning (RL)
Explainability for identification of vulnerable groups in machine learning models
But can we know that this is what a model does?
BIG-bench Machine Learning
Explainable artificial intelligence
+2
Socioeconomic disparities and COVID-19: the causal connections
The analysis of causation is a challenging task that can be approached in various ways.
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning
This partial causal ordering defines the causal relations between the features, and we specify this using domain knowledge about the lever control task.
Explainable artificial intelligence
Reinforcement Learning (RL)
Artificial Intelligence in Dry Eye Disease
Although the term `AI' is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes.
Inferring feature importance with uncertainties in high-dimensional data
We build upon the recently published feature importance measure of SAGE (Shapley additive global importance) and introduce sub-SAGE which can be estimated without resampling for tree-based models.
Beyond Cuts in Small Signal Scenarios -- Enhanced Sneutrino Detectability Using Machine Learning
We investigate enhancing the sensitivity of new physics searches at the LHC by machine learning in the case of background dominance and a high degree of overlap between the observables for signal and background.
The social dilemma in artificial intelligence development and why we have to solve it
While the demand for ethical artificial intelligence (AI) systems increases, the number of unethical uses of AI accelerates, even though there is no shortage of ethical guidelines.
Shapley values for feature selection: The good, the bad, and the axioms
The Shapley value has become popular in the Explainable AI (XAI) literature, thanks, to a large extent, to a solid theoretical foundation, including four "favourable and fair" axioms for attribution in transferable utility games.
Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies
To the best of our knowledge, all existing game formulations in the machine learning and statistics literature fall into a category which we name the model-dependent category of game formulations.
