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...

[2602.12703] SWING: Unlocking Implicit Graph Representations for Graph Random Features

The paper presents SWING, a novel algorithm for computations involving Graph Random Features on implicit graphs, enhancing efficiency thr...

[2602.12651] Uncovering spatial tissue domains and cell types in spatial omics through cross-scale profiling of cellular and genomic interactions

This article presents CellScape, a deep learning framework for analyzing spatial transcriptomics data, addressing the challenges of noise...

[2602.12622] Efficient Personalized Federated PCA with Manifold Optimization for IoT Anomaly Detection

This article presents a novel method for anomaly detection in IoT networks using Efficient Personalized Federated PCA, addressing the cha...

[2602.12613] Coden: Efficient Temporal Graph Neural Networks for Continuous Prediction

The paper introduces Coden, an efficient Temporal Graph Neural Network (TGNN) model designed for continuous predictions, overcoming limit...

[2602.12605] Block-Sample MAC-Bayes Generalization Bounds

The paper introduces Block-Sample MAC-Bayes bounds, a new approach to generalization error estimation in machine learning, enhancing trad...

[2602.12606] RelBench v2: A Large-Scale Benchmark and Repository for Relational Data

RelBench v2 introduces a comprehensive benchmark for relational deep learning, featuring 11 datasets and new predictive tasks, enhancing ...

[2602.12591] Vehicle behaviour estimation for abnormal event detection using distributed fiber optic sensing

This article presents a method for detecting vehicle lane changes to identify single-lane abnormalities using distributed fiber optic sen...

[2602.12567] Fractional Order Federated Learning for Battery Electric Vehicle Energy Consumption Modeling

This article presents a novel approach to federated learning for Battery Electric Vehicles (BEVs) using Fractional-Order Roughness-Inform...

[2602.12527] Analytical Results for Two Exponential Family Distributions in Hierarchical Dirichlet Processes

This paper explores analytical results for two exponential family distributions within the Hierarchical Dirichlet Process (HDP), focusing...

[2602.12499] A Theoretical Analysis of Mamba's Training Dynamics: Filtering Relevant Features for Generalization in State Space Models

This article presents a theoretical analysis of Mamba's training dynamics, focusing on feature selection in state space models and their ...

[2602.12482] Geometric separation and constructive universal approximation with two hidden layers

This paper presents a geometric construction of neural networks capable of separating disjoint compact subsets in R^n, demonstrating a un...

[2602.12471] Tight Bounds for Logistic Regression with Large Stepsize Gradient Descent in Low Dimension

This paper presents a refined analysis of gradient descent for logistic regression in low dimensions, demonstrating improved bounds on lo...

[2602.12469] Regularized Meta-Learning for Improved Generalization

The paper presents a regularized meta-learning framework aimed at improving generalization in ensemble methods by addressing redundancy, ...

[2602.12449] Computationally sufficient statistics for Ising models

This paper explores computationally sufficient statistics for Ising models, addressing the challenges of learning Gibbs distributions wit...

[2602.12394] Synthetic Interaction Data for Scalable Personalization in Large Language Models

The paper introduces PersonaGym, a framework for generating synthetic interaction data to enhance personalization in large language model...

[2602.12391] High-dimensional Level Set Estimation with Trust Regions and Double Acquisition Functions

This paper presents TRLSE, a novel algorithm for high-dimensional level set estimation, enhancing sample efficiency through dual acquisit...

[2602.12379] Deep Doubly Debiased Longitudinal Effect Estimation with ICE G-Computation

This paper presents D3-Net, a novel framework for estimating longitudinal treatment effects using ICE G-computation, addressing error pro...

[2602.12368] A Machine Learning Approach to the Nirenberg Problem

This paper presents a novel machine learning framework, the Nirenberg Neural Network, to address the Nirenberg problem of prescribing Gau...

[2602.12323] The Appeal and Reality of Recycling LoRAs with Adaptive Merging

This article explores the effectiveness of adaptive merging methods for recycling LoRA modules in machine learning, revealing limited ben...

[2602.10947] Computational Phenomenology of Temporal Experience in Autism: Quantifying the Emotional and Narrative Characteristics of Lived Unpredictability

This article explores the emotional and narrative characteristics of temporal experience in autistic individuals, highlighting the unpred...