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Found 15 papers, 9 papers with code
Enhancing Brain Source Reconstruction through Physics-Informed 3D Neural Networks
Traditional methods predominantly rely on manually crafted priors, missing the flexibility of data-driven learning, while recent deep learning approaches focus on end-to-end learning, typically using the physical information of the forward model only for generating training data.
Explainable AI needs formal notions of explanation correctness
This will lead to notions of explanation correctness that can be theoretically verified and objective metrics of explanation performance that can be assessed using ground-truth data.
GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanations
This gives rise to ground-truth 'world explanations' for gender classification tasks, enabling the objective evaluation of the correctness of XAI methods.
EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods
The evolving landscape of explainable artificial intelligence (XAI) aims to improve the interpretability of intricate machine learning (ML) models, yet faces challenges in formalisation and empirical validation, being an inherently unsupervised process.
XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance
The field of 'explainable' artificial intelligence (XAI) has produced highly cited methods that seek to make the decisions of complex machine learning (ML) methods 'understandable' to humans, for example by attributing 'importance' scores to input features.
Benchmark data to study the influence of pre-training on explanation performance in MR image classification
Here, we propose a benchmark dataset that allows for quantifying explanation performance in a realistic magnetic resonance imaging (MRI) classification task.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables
In recent years, the community of 'explainable artificial intelligence' (XAI) has created a vast body of methods to bridge a perceived gap between model 'complexity' and 'interpretability'.
Evaluating saliency methods on artificial data with different background types
Over the last years, many 'explainable artificial intelligence' (xAI) approaches have been developed, but these have not always been objectively evaluated.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Scrutinizing XAI using linear ground-truth data with suppressor variables
It has been demonstrated that some saliency methods can highlight features that have no statistical association with the prediction target (suppressor variables).
Explainable Artificial Intelligence (XAI)
Feature Importance
Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging
Several problems in neuroimaging and beyond require inference on the parameters of multi-task sparse hierarchical regression models.
Joint Learning of Full-structure Noise in Hierarchical Bayesian Regression Models
We consider hierarchical Bayesian (type-II maximum likelihood) regression models for observations with latent variables for source and noise, where parameters of priors for source and noise terms need to be estimated jointly from data.
A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI
In this study, we develop a linear state-space model to infer the effective connectivity in a distributed brain network based on simultaneously recorded EEG and fMRI data.
Correlated Components Analysis - Extracting Reliable Dimensions in Multivariate Data
How does one find dimensions in multivariate data that are reliably expressed across repetitions?
Validity of time reversal for testing Granger causality
Inferring causal interactions from observed data is a challenging problem, especially in the presence of measurement noise.
Estimating vector fields using sparse basis field expansions
We introduce a novel framework for estimating vector fields using sparse basis field expansions (S-FLEX).
