Search Results for author:
Found 12 papers, 7 papers with code
Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos
Explainability and interpretability are two critical aspects of decision support systems.
One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies
In this paper, we present the results of our investigation of what are the most important factors to generate deep representations for the data and learning tasks in the music domain.
Transfer Learning of Artist Group Factors to Musical Genre Classification
The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy.
Are Nearby Neighbors Relatives?: Testing Deep Music Embeddings
The underlying assumption is that in case a deep representation is to be trusted, distance consistency between known related points should be maintained both in the input audio space and corresponding latent deep space.
ReproducedPapers.org: Openly teaching and structuring machine learning reproducibility
We present ReproducedPapers. org: an open online repository for teaching and structuring machine learning reproducibility.
Run, Forest, Run? On Randomization and Reproducibility in Predictive Software Engineering
To understand whether and how researchers in SE address these threats, we surveyed 45 recent papers related to three predictive tasks: defect prediction (DP), predictive mutation testing (PMT), and code smell detection (CSD).
Software Engineering
Social Inclusion in Curated Contexts: Insights from Museum Practices
Artificial intelligence literature suggests that minority and fragile communities in society can be negatively impacted by machine learning algorithms due to inherent biases in the design process, which lead to socially exclusive decisions and policies.
Hidden Author Bias in Book Recommendation
We find that popular books are mainly written by US citizens in the dataset, and that these books tend to be recommended disproportionally by popular collaborative filtering algorithms compared to the users' profiles.
Explaining Black-Box Models through Counterfactuals
We present CounterfactualExplanations. jl: a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box models in Julia.
Endogenous Macrodynamics in Algorithmic Recourse
Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse (AR) has largely focused on single individuals in a static environment: given some estimated model, the goal is to find valid counterfactuals for an individual instance that fulfill various desiderata.
Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals
We formalise this notion of faithfulness through the introduction of a tailored evaluation metric and propose a novel algorithmic framework for generating Energy-Constrained Conformal Counterfactuals that are only as plausible as the model permits.
Position Paper: Against Spurious Sparks $-$ Dovelating Inflated AI Claims
Humans have a tendency to see 'human'-like qualities in objects around them.
