Data Science

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UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

[D] Offering licensed Indian language speech datasets (with explicit contributor consent)

Hi everyone, I run a small data initiative where we collect speech datasets in multiple Indian languages directly from contributors who p...

[2411.01685] Reducing Biases in Record Matching Through Scores Calibration

This paper explores methods to reduce biases in record matching through score calibration, proposing two model-agnostic post-processing t...

[2602.20066] HeatPrompt: Zero-Shot Vision-Language Modeling of Urban Heat Demand from Satellite Images

The paper presents HeatPrompt, a zero-shot vision-language framework for estimating urban heat demand from satellite images, enhancing en...

[2410.07003] Mirror Bridges Between Probability Measures

The paper introduces a novel model called mirror bridges for conditional resampling from probability measures, addressing challenges in g...

[2404.16890] Layer Collapse Can be Induced by Unstructured Pruning

This paper explores how unstructured pruning can lead to layer collapse in neural networks, demonstrating that it can effectively reduce ...

[2310.01770] A simple connection from loss flatness to compressed neural representations

This article explores the relationship between loss flatness and compressed neural representations, introducing new measures and empirica...

[2602.20028] Descriptor: Dataset of Parasitoid Wasps and Associated Hymenoptera (DAPWH)

The article presents a curated dataset of parasitoid wasps and associated Hymenoptera, aimed at enhancing automated identification system...

[2602.20153] JUCAL: Jointly Calibrating Aleatoric and Epistemic Uncertainty in Classification Tasks

The paper presents JUCAL, a novel calibration method that jointly addresses aleatoric and epistemic uncertainty in classification tasks, ...

[2602.20151] Conformal Risk Control for Non-Monotonic Losses

This article presents a novel approach to conformal risk control for non-monotonic losses, extending traditional methods to multidimensio...

[2602.19946] When Pretty Isn't Useful: Investigating Why Modern Text-to-Image Models Fail as Reliable Training Data Generators

This paper investigates the limitations of modern text-to-image models as reliable training data generators, revealing a decline in class...

[2602.19881] Make Some Noise: Unsupervised Remote Sensing Change Detection Using Latent Space Perturbations

The paper presents MaSoN, an innovative framework for unsupervised change detection in remote sensing that generates diverse changes in l...

[2602.20001] FairFS: Addressing Deep Feature Selection Biases for Recommender System

The paper presents FairFS, a novel algorithm designed to address biases in feature selection for recommender systems, enhancing accuracy ...

[2602.19984] Multivariate time-series forecasting of ASTRI-Horn monitoring data: A Normal Behavior Model

This article presents a Normal Behavior Model (NBM) for forecasting monitoring data from the ASTRI-Horn telescope, demonstrating effectiv...

[2602.19919] Janus-Q: End-to-End Event-Driven Trading via Hierarchical-Gated Reward Modeling

The paper presents Janus-Q, an innovative framework for event-driven trading that leverages financial news events as primary decision-mak...

[2602.19822] Efficient endometrial carcinoma screening via cross-modal synthesis and gradient distillation

This article presents a novel deep learning framework for efficient endometrial carcinoma screening, utilizing cross-modal synthesis and ...

[2602.19818] SafePickle: Robust and Generic ML Detection of Malicious Pickle-based ML Models

The paper presents SafePickle, a machine-learning-based scanner designed to detect malicious Pickle-based ML models, achieving a high F1-...

[2602.19903] Rethinking Chronological Causal Discovery with Signal Processing

This paper explores the impact of sampling rates and window lengths on causal discovery methods, highlighting the sensitivity of these me...

[2602.19859] Dirichlet Scale Mixture Priors for Bayesian Neural Networks

This article introduces Dirichlet Scale Mixture (DSM) priors for Bayesian Neural Networks, addressing limitations in interpretability and...

[2602.19786] The Climate Change Knowledge Graph: Supporting Climate Services

The Climate Change Knowledge Graph integrates diverse datasets from climate models to enhance decision-making in climate services, offeri...

[2602.19851] Orthogonal Uplift Learning with Permutation-Invariant Representations for Combinatorial Treatments

This paper presents a novel framework for uplift estimation in combinatorial treatments, enhancing causal effect modeling through permuta...

[2602.19702] DReX: An Explainable Deep Learning-based Multimodal Recommendation Framework

DReX is a novel multimodal recommendation framework that enhances user and item representation through explainable deep learning, address...