Data Science

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...

Scientists uncover new method to generate protein datasets for training AI

6 Months Using AI for Actual Work: What's Incredible, What's Overhyped, and What's Quietly Dangerous

Six months ago I committed to using AI tools for everything I possibly could in my work. Every day, every task, every workflow. Here's th...

[2510.17406] Multi-Window Temporal Analysis for Enhanced Arrhythmia Classification: Leveraging Long-Range Dependencies in Electrocardiogram Signals

This paper presents S4ECG, a novel deep learning architecture that enhances arrhythmia classification by analyzing multiple ECG windows, ...

[2510.09416] What Do Temporal Graph Learning Models Learn?

This paper evaluates the effectiveness of temporal graph learning models in capturing key characteristics of temporal graphs, revealing b...

[2510.01022] VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks

The paper introduces Vector Diffusion Wavelets (VDWs) for geometric graph neural networks (VDW-GNNs), demonstrating their effectiveness o...

[2509.25826] Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models

The paper presents Kairos, a novel time series foundation model that enhances zero-shot generalization by decoupling temporal heterogenei...

[2509.14585] Online reinforcement learning via sparse Gaussian mixture model Q-functions

This paper presents an innovative online reinforcement learning framework using sparse Gaussian mixture model Q-functions, enhancing expl...

[2509.08759] Fourier Learning Machines: Nonharmonic Fourier-Based Neural Networks for Scientific Machine Learning

The paper introduces Fourier Learning Machines (FLMs), a novel neural network architecture that utilizes nonharmonic Fourier series for s...

[2508.10587] Self-Supervised Temporal Super-Resolution of Energy Data using Generative Adversarial Transformer

This paper presents a novel self-supervised method for temporal super-resolution of energy data using Generative Adversarial Transformers...

[2508.01504] Instruction-based Time Series Editing

The paper introduces Instruction-based Time Series Editing, a novel approach that allows users to modify time series data using natural l...

[2507.09043] GAGA: Gaussianity-Aware Gaussian Approximation for Efficient 3D Molecular Generation

The paper presents GAGA, a method enhancing the efficiency of 3D molecular generation by leveraging Gaussian approximations, improving bo...

[2506.13652] PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning

The paper introduces PeakWeather, a comprehensive dataset of weather measurements from MeteoSwiss, aimed at enhancing spatiotemporal deep...

[2506.05325] Quasiparticle Interference Kernel Extraction with Variational Autoencoders via Latent Alignment

This article presents an AI-based framework for extracting quasiparticle interference (QPI) kernels from complex scattering images, impro...

[2506.04166] N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion

The paper presents N$^2$, a Python package for nearest neighbor-based matrix completion, emphasizing its modular design and superior perf...

[2505.16308] Beyond All-to-All: Causal-Aligned Transformer with Dynamic Structure Learning for Multivariate Time Series Forecasting

This article presents a novel approach to multivariate time series forecasting using a Causal Decomposition Transformer (CDT) that learns...

[2505.11846] Learning on a Razor's Edge: Identifiability and Singularity of Polynomial Neural Networks

This paper investigates the identifiability and singularity of polynomial neural networks, focusing on MLPs and CNNs, and explores their ...

[2504.16585] Leveraging Noisy Manual Labels as Useful Information: An Information Fusion Approach for Enhanced Variable Selection in Penalized Logistic Regression

This paper explores how noisy manual labels can enhance variable selection in penalized logistic regression, proposing a novel algorithm ...

[2011.07687] DART: aDaptive Accept RejecT for non-linear top-K subset identification

The paper presents DART, a novel algorithm for non-linear top-K subset identification in bandit problems, achieving efficient performance...

[2602.13181] Selection of CMIP6 Models for Regional Precipitation Projection and Climate Change Assessment in the Jhelum and Chenab River Basins

This study evaluates the selection of CMIP6 models for projecting regional precipitation and assessing climate change impacts in the Jhel...

[2602.13177] Improved Regret Guarantees for Online Mirror Descent using a Portfolio of Mirror Maps

This paper presents improved regret guarantees for Online Mirror Descent (OMD) by utilizing a portfolio of mirror maps, enhancing perform...

[2602.13104] Random Forests as Statistical Procedures: Design, Variance, and Dependence

This paper presents a design-based perspective on random forests, emphasizing their statistical properties and variance characteristics, ...

[2602.13112] AdaGrad-Diff: A New Version of the Adaptive Gradient Algorithm

The paper introduces AdaGrad-Diff, an adaptive gradient algorithm that improves upon the traditional AdaGrad by adjusting the stepsize ba...