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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 ·
Machine Learning

Free tool I built to score dataset quality (LQS) — feedback welcome [D]

We built a Label Quality Score (LQS) system for our dataset marketplace and opened it up as a free standalone tool. Upload a dataset → ge...

Reddit - Machine Learning · 1 min ·
Data Science

"OpenAI quietly removed the one safety mechanism that could shut the whole thing down — and nobody is talking about it"

OpenAI was founded as a nonprofit for one specific reason — to ensure AI development couldn't be hijacked by profit motives. Their origin...

Reddit - Artificial Intelligence · 1 min ·

All Content

[2602.16167] Muon with Spectral Guidance: Efficient Optimization for Scientific Machine Learning
Machine Learning

[2602.16167] Muon with Spectral Guidance: Efficient Optimization for Scientific Machine Learning

The paper introduces SpecMuon, a novel optimizer that enhances the Muon optimizer for scientific machine learning by addressing challenge...

arXiv - Machine Learning · 4 min ·
[2602.16155] Differentially Private Non-convex Distributionally Robust Optimization
Machine Learning

[2602.16155] Differentially Private Non-convex Distributionally Robust Optimization

This paper presents a novel approach to differentially private non-convex distributionally robust optimization (DRO), addressing challeng...

arXiv - Machine Learning · 4 min ·
[2602.16147] ASPEN: Spectral-Temporal Fusion for Cross-Subject Brain Decoding
Machine Learning

[2602.16147] ASPEN: Spectral-Temporal Fusion for Cross-Subject Brain Decoding

The paper presents ASPEN, a novel architecture that enhances cross-subject brain decoding by integrating spectral and temporal features, ...

arXiv - AI · 3 min ·
[2602.16145] Investigating GNN Convergence on Large Randomly Generated Graphs with Realistic Node Feature Correlations
Machine Learning

[2602.16145] Investigating GNN Convergence on Large Randomly Generated Graphs with Realistic Node Feature Correlations

This paper investigates the convergence behavior of Graph Neural Networks (GNNs) on large random graphs with realistic node feature corre...

arXiv - Machine Learning · 4 min ·
[2602.16125] On the Power of Source Screening for Learning Shared Feature Extractors
Machine Learning

[2602.16125] On the Power of Source Screening for Learning Shared Feature Extractors

This paper explores the effectiveness of source screening in learning shared feature extractors, demonstrating that optimal subspace esti...

arXiv - Machine Learning · 4 min ·
[2602.16120] Feature-based morphological analysis of shape graph data
Data Science

[2602.16120] Feature-based morphological analysis of shape graph data

This paper presents a computational pipeline for analyzing shape graph datasets, focusing on geometric and topological features to enhanc...

arXiv - Machine Learning · 3 min ·
[2602.16101] Axle Sensor Fusion for Online Continual Wheel Fault Detection in Wayside Railway Monitoring
Machine Learning

[2602.16101] Axle Sensor Fusion for Online Continual Wheel Fault Detection in Wayside Railway Monitoring

This paper presents a novel framework for online continual wheel fault detection in railway systems using axle sensor fusion and machine ...

arXiv - Machine Learning · 4 min ·
[2602.16072] Omni-iEEG: A Large-Scale, Comprehensive iEEG Dataset and Benchmark for Epilepsy Research
Data Science

[2602.16072] Omni-iEEG: A Large-Scale, Comprehensive iEEG Dataset and Benchmark for Epilepsy Research

The Omni-iEEG dataset provides a comprehensive resource for epilepsy research, featuring 302 patients and 178 hours of high-resolution iE...

arXiv - AI · 4 min ·
[2602.16057] Extracting and Analyzing Rail Crossing Behavior Signatures from Videos using Tensor Methods
Machine Learning

[2602.16057] Extracting and Analyzing Rail Crossing Behavior Signatures from Videos using Tensor Methods

This article presents a novel multi-view tensor decomposition framework to analyze rail crossing behaviors from video data, revealing sig...

arXiv - Machine Learning · 4 min ·
[2602.16020] MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching
Machine Learning

[2602.16020] MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching

MolCrystalFlow introduces a novel flow-based generative model for predicting molecular crystal structures, addressing challenges in compu...

arXiv - Machine Learning · 4 min ·
[2602.16015] Geometry-Aware Uncertainty Quantification via Conformal Prediction on Manifolds
Nlp

[2602.16015] Geometry-Aware Uncertainty Quantification via Conformal Prediction on Manifolds

This paper introduces adaptive geodesic conformal prediction, a novel framework for uncertainty quantification on Riemannian manifolds, e...

arXiv - Machine Learning · 3 min ·
[2602.15961] R$^2$Energy: A Large-Scale Benchmark for Robust Renewable Energy Forecasting under Diverse and Extreme Conditions
Machine Learning

[2602.15961] R$^2$Energy: A Large-Scale Benchmark for Robust Renewable Energy Forecasting under Diverse and Extreme Conditions

The paper presents R$^2$Energy, a benchmark for robust renewable energy forecasting, addressing challenges posed by extreme weather and g...

arXiv - Machine Learning · 4 min ·
[2602.15972] Fast Online Learning with Gaussian Prior-Driven Hierarchical Unimodal Thompson Sampling
Machine Learning

[2602.15972] Fast Online Learning with Gaussian Prior-Driven Hierarchical Unimodal Thompson Sampling

This paper presents a novel approach to Multi-Armed Bandit problems using Gaussian prior-driven hierarchical unimodal Thompson Sampling, ...

arXiv - Machine Learning · 4 min ·
[2602.15984] Verifier-Constrained Flow Expansion for Discovery Beyond the Data
Machine Learning

[2602.15984] Verifier-Constrained Flow Expansion for Discovery Beyond the Data

This paper presents a method called Verifier-Constrained Flow Expansion (FE) to enhance flow models for scientific discovery by expanding...

arXiv - Machine Learning · 4 min ·
[2602.15883] Distributed physics-informed neural networks via domain decomposition for fast flow reconstruction
Machine Learning

[2602.15883] Distributed physics-informed neural networks via domain decomposition for fast flow reconstruction

This article presents a distributed framework for physics-informed neural networks (PINNs) aimed at efficient flow reconstruction, addres...

arXiv - Machine Learning · 4 min ·
[2602.15878] IT-OSE: Exploring Optimal Sample Size for Industrial Data Augmentation
Machine Learning

[2602.15878] IT-OSE: Exploring Optimal Sample Size for Industrial Data Augmentation

The paper presents IT-OSE, a method for estimating the optimal sample size for data augmentation in industrial settings, improving model ...

arXiv - AI · 4 min ·
[2602.15877] Genetic Generalized Additive Models
Machine Learning

[2602.15877] Genetic Generalized Additive Models

This article presents Genetic Generalized Additive Models (GGAMs), which utilize a multi-objective genetic algorithm to optimize model st...

arXiv - AI · 3 min ·
[2602.15879] BamaER: A Behavior-Aware Memory-Augmented Model for Exercise Recommendation
Machine Learning

[2602.15879] BamaER: A Behavior-Aware Memory-Augmented Model for Exercise Recommendation

The paper presents BamaER, a memory-augmented model designed for personalized exercise recommendations based on students' learning behavi...

arXiv - Machine Learning · 4 min ·
Machine Learning

Machine learning helps solve a central problem of quantum chemistry

The article discusses how machine learning techniques are being applied to address significant challenges in quantum chemistry, potential...

AI News - General · 1 min ·
Machine Learning

Machine learning algorithm fully reconstructs LHC particle collisions

A new machine learning algorithm has been developed to fully reconstruct particle collisions at the LHC, offering faster and more precise...

Reddit - Artificial Intelligence · 1 min ·
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