Data Science

Data analysis, statistics, and data engineering

This Week's Best | Monthly Best | Guide | Trending

Top This Week

UMKC Announces New Master of Science in Artificial Intelligence
Ai Infrastructure

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...

AI News - General · 4 min ·
Accelerating science with AI and simulations
Machine Learning

Accelerating science with AI and simulations

MIT Professor Rafael Gómez-Bombarelli discusses the transformative potential of AI in scientific research, emphasizing its role in materi...

AI News - General · 10 min ·
Data Science

~77% of all new "Success" self-help books on Amazon are likely written by AI, with 1 author, Noah Felix Bennett, publishing a stunning 74 books in mid-2025 alone, at a rate of >1 per day. Richard Trillion Mantey, who has published hundreds of books, was assessed to have used AI for every single book

"Ironically, one of the 844 books in this dataset is called 'How to Write for Humans in an AI World: Cutting Through Digital Noise and Re...

Reddit - Artificial Intelligence · 1 min ·

All Content

[2602.15708] Outer Diversity of Structured Domains
Machine Learning

[2602.15708] Outer Diversity of Structured Domains

The paper introduces the concept of outer diversity in ordinal preference domains, analyzing its implications for various structured doma...

arXiv - AI · 3 min ·
[2507.12202] Sparse Autoencoders for Sequential Recommendation Models: Interpretation and Flexible Control
Machine Learning

[2507.12202] Sparse Autoencoders for Sequential Recommendation Models: Interpretation and Flexible Control

This article presents a novel approach using Sparse Autoencoders (SAE) for enhancing the interpretability and control of sequential recom...

arXiv - AI · 4 min ·
[2505.12254] MMS-VPR: Multimodal Street-Level Visual Place Recognition Dataset and Benchmark
Data Science

[2505.12254] MMS-VPR: Multimodal Street-Level Visual Place Recognition Dataset and Benchmark

The MMS-VPR paper introduces a comprehensive multimodal dataset for street-level visual place recognition, addressing gaps in existing da...

arXiv - AI · 4 min ·
[2602.15660] Bayesian Optimization for Design Parameters of 3D Image Data Analysis
Machine Learning

[2602.15660] Bayesian Optimization for Design Parameters of 3D Image Data Analysis

This paper presents a novel 3D data Analysis Optimization Pipeline that utilizes Bayesian Optimization to enhance segmentation and classi...

arXiv - AI · 4 min ·
[2504.14696] Reveal-or-Obscure: A Differentially Private Sampling Algorithm for Discrete Distributions
Machine Learning

[2504.14696] Reveal-or-Obscure: A Differentially Private Sampling Algorithm for Discrete Distributions

The paper presents a differentially private sampling algorithm, Reveal-or-Obscure (ROO), for generating samples from discrete distributio...

arXiv - Machine Learning · 4 min ·
[2504.05654] Curved representational Bregman divergences and their applications
Data Science

[2504.05654] Curved representational Bregman divergences and their applications

This article introduces curved representational Bregman divergences, exploring their mathematical foundations and applications in informa...

arXiv - Machine Learning · 4 min ·
[2502.17812] Can Multimodal LLMs Perform Time Series Anomaly Detection?
Llms

[2502.17812] Can Multimodal LLMs Perform Time Series Anomaly Detection?

The paper explores the potential of multimodal large language models (MLLMs) for time series anomaly detection (TSAD), introducing a new ...

arXiv - Machine Learning · 4 min ·
[2502.07397] Linear Bandits beyond Inner Product Spaces, the case of Bandit Optimal Transport
Machine Learning

[2502.07397] Linear Bandits beyond Inner Product Spaces, the case of Bandit Optimal Transport

This paper explores linear bandits beyond traditional inner product spaces, focusing on optimal transport problems. It proposes a refined...

arXiv - Machine Learning · 4 min ·
[2602.15600] The geometry of online conversations and the causal antecedents of conflictual discourse
Llms

[2602.15600] The geometry of online conversations and the causal antecedents of conflictual discourse

This article explores the dynamics of conflictual discourse in online conversations, particularly focusing on climate change discussions....

arXiv - AI · 4 min ·
[2602.15579] Intracoronary Optical Coherence Tomography Image Processing and Vessel Classification Using Machine Learning
Machine Learning

[2602.15579] Intracoronary Optical Coherence Tomography Image Processing and Vessel Classification Using Machine Learning

This paper presents a machine learning-based pipeline for automated segmentation and classification of vessels in Intracoronary Optical C...

arXiv - AI · 3 min ·
[2405.20178] Non-intrusive data-driven model order reduction for circuits based on Hammerstein architectures
Machine Learning

[2405.20178] Non-intrusive data-driven model order reduction for circuits based on Hammerstein architectures

This paper presents a non-intrusive data-driven model order reduction method for circuits using Hammerstein architectures, demonstrating ...

arXiv - Machine Learning · 4 min ·
[2602.11618] How Well Do Large-Scale Chemical Language Models Transfer to Downstream Tasks?
Llms

[2602.11618] How Well Do Large-Scale Chemical Language Models Transfer to Downstream Tasks?

This paper evaluates the effectiveness of large-scale Chemical Language Models (CLMs) in transferring knowledge to downstream molecular p...

arXiv - Machine Learning · 4 min ·
[2602.10706] Reducing Estimation Uncertainty Using Normalizing Flows and Stratification
Machine Learning

[2602.10706] Reducing Estimation Uncertainty Using Normalizing Flows and Stratification

This paper presents a novel approach to reducing estimation uncertainty in statistical analysis using normalizing flows and stratified sa...

arXiv - Machine Learning · 3 min ·
[2602.05999] On the Role of Iterative Computation in Reinforcement Learning
Machine Learning

[2602.05999] On the Role of Iterative Computation in Reinforcement Learning

This paper explores how the amount of compute available to reinforcement learning (RL) policies influences their learning capabilities an...

arXiv - Machine Learning · 4 min ·
[2602.00834] Don't Forget Its Variance! The Minimum Path Variance Principle for Accurate and Stable Score-Based Models
Machine Learning

[2602.00834] Don't Forget Its Variance! The Minimum Path Variance Principle for Accurate and Stable Score-Based Models

This paper introduces the Minimum Path Variance (MinPV) Principle, addressing the paradox of score-based methods in machine learning by m...

arXiv - Machine Learning · 3 min ·
[2602.00240] Green-NAS: A Global-Scale Multi-Objective Neural Architecture Search for Robust and Efficient Edge-Native Weather Forecasting
Machine Learning

[2602.00240] Green-NAS: A Global-Scale Multi-Objective Neural Architecture Search for Robust and Efficient Edge-Native Weather Forecasting

Green-NAS presents a multi-objective neural architecture search framework aimed at optimizing weather forecasting models for low-resource...

arXiv - Machine Learning · 4 min ·
[2601.19720] Improving Policy Exploitation in Online Reinforcement Learning with Instant Retrospect Action
Machine Learning

[2601.19720] Improving Policy Exploitation in Online Reinforcement Learning with Instant Retrospect Action

The paper presents a novel algorithm, Instant Retrospect Action (IRA), aimed at enhancing policy exploitation in online reinforcement lea...

arXiv - Machine Learning · 4 min ·
[2601.11440] GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance
Machine Learning

[2601.11440] GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance

The paper presents GenDA, a generative data assimilation framework for reconstructing urban wind fields from sparse sensor data, enhancin...

arXiv - AI · 4 min ·
[2601.02799] Stratified Hazard Sampling: Minimal-Variance Event Scheduling for CTMC/DTMC Discrete Diffusion and Flow Models
Machine Learning

[2601.02799] Stratified Hazard Sampling: Minimal-Variance Event Scheduling for CTMC/DTMC Discrete Diffusion and Flow Models

This article presents Stratified Hazard Sampling (SHS), a novel method for improving event scheduling in discrete diffusion and flow mode...

arXiv - Machine Learning · 4 min ·
[2601.01297] ARGUS: Adaptive Rotation-Invariant Geometric Unsupervised System
Data Science

[2601.01297] ARGUS: Adaptive Rotation-Invariant Geometric Unsupervised System

The paper introduces ARGUS, a novel framework for detecting distributional drift in high-dimensional data streams, emphasizing geometric ...

arXiv - AI · 4 min ·
Previous Page 119 Next

Related Topics

Machine Learning Large Language Models Generative AI Natural Language Processing Computer Vision Robotics & Embodied AI

Stay updated with AI News

Get the latest news, tools, and insights delivered to your inbox.

Subscribe to Newsletter

Daily or weekly digest • Unsubscribe anytime