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Found 13 papers, 7 papers with code
Towards Reproducible Machine Learning Research in Natural Language Processing
While recent progress in the field of ML has been significant, the reproducibility of these cutting-edge results is often lacking, with many submissions lacking the necessary information in order to ensure subsequent reproducibility.
Clifford-Steerable Convolutional Neural Networks
We present Clifford-Steerable Convolutional Neural Networks (CS-CNNs), a novel class of $\mathrm{E}(p, q)$-equivariant CNNs.
Semi-Supervised Object Detection in the Open World
Existing approaches for semi-supervised object detection assume a fixed set of classes present in training and unlabeled datasets, i. e., in-distribution (ID) data.
Explaining Predictions from Machine Learning Models: Algorithms, Users, and Pedagogy
Model explainability has become an important problem in machine learning (ML) due to the increased effect that algorithmic predictions have on humans.
Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
In this paper, we report on ongoing work regarding (i) the development of an AI system for flagging and explaining low-quality medical images in real-time, (ii) an interview study to understand the explanation needs of stakeholders using the AI system at OurCompany, and, (iii) a longitudinal user study design to examine the effect of including explanations on the workflow of the technicians in our clinics.
A Song of (Dis)agreement: Evaluating the Evaluation of Explainable Artificial Intelligence in Natural Language Processing
The validity of "attention as explanation" has so far been evaluated by computing the rank correlation between attention-based explanations and existing feature attribution explanations using LSTM-based models.
Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence
In this work, we explain the setup for a technical, graduate-level course on Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence (FACT-AI) at the University of Amsterdam, which teaches FACT-AI concepts through the lens of reproducibility.
Order in the Court: Explainable AI Methods Prone to Disagreement
By computing the rank correlation between attention weights and feature-additive explanation methods, previous analyses either invalidate or support the role of attention-based explanations as a faithful and plausible measure of salience.
To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions
In hybrid human-AI systems, users need to decide whether or not to trust an algorithmic prediction while the true error in the prediction is unknown.
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
In this work, we propose a method for generating CF explanations for GNNs: the minimal perturbation to the input (graph) data such that the prediction changes.
FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles
Model interpretability has become an important problem in machine learning (ML) due to the increased effect that algorithmic decisions have on humans.
Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting
Given a large error, MC-BRP determines (1) feature values that would result in a reasonable prediction, and (2) general trends between each feature and the target, both based on Monte Carlo simulations.
Explaining Predictions from Tree-based Boosting Ensembles
Understanding how "black-box" models arrive at their predictions has sparked significant interest from both within and outside the AI community.
