Data analysis, statistics, and data engineering
This article reviews the top 10 AI certifications and courses for 2026, highlighting their significance in a rapidly evolving field and t...
![[2603.18109] Discovery of Bimodal Drift Rate Structure in FRB 20240114A: Evidence for Dual Emission Regions](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Abstract page for arXiv paper 2603.18109: Discovery of Bimodal Drift Rate Structure in FRB 20240114A: Evidence for Dual Emission Regions
Abstract page for arXiv paper 2509.22367: What Is The Political Content in LLMs' Pre- and Post-Training Data?
The article explores the challenges AI faces in parsing PDFs, highlighting the limitations of current models and the innovative solutions...
The wait for SIGIR 2026 review scores feels unusually long this year, raising concerns about the impact on researchers' timelines and pro...
The article explores Chicago's extensive surveillance system, highlighting its implications for public safety and civil liberties, partic...
A recent Gallup poll reveals that AI adoption among American workers has surged, with 12% using it daily and nearly half using it at leas...
The paper presents a novel approach to Deep Linear Discriminant Analysis (Deep LDA) by introducing a constrained formulation that stabili...
The paper introduces Online Smoothed Demand Management (OSDM), a framework for optimizing energy purchasing and delivery in data centers,...
The paper presents a novel modeling framework for learning ordinary differential equations (ODEs) from limited and noisy data, enhancing ...
This paper establishes the minimax lower bound of Kernel Stein Discrepancy (KSD) estimation, demonstrating its optimality and implication...
This paper introduces Bayesian-TPNN, a Bayesian inference approach for the functional ANOVA model using Tensor Product Neural Networks, i...
This paper investigates the adversarial robustness of learning-based conformal novelty detection methods, revealing significant vulnerabi...
This article presents an ensemble-based graph representation method for classifying cognitive brain states using fMRI data, achieving hig...
This paper presents a comprehensive study on estimating discrete distributions using Kullback-Leibler divergence, establishing minimax ra...
This paper explores the asymptotic behavior of eigenvalues in large random matrices, particularly focusing on the impact of rank perturba...
AstroMLab 4 introduces a 70B-parameter AI model specialized for astronomy, achieving benchmark-topping performance in Q&A tasks, surpassi...
The paper presents a novel machine learning framework for inferring hidden statuses in social networks, enhancing the understanding of sp...
This article evaluates the effectiveness of textual data sanitization methods, revealing that current techniques may provide a false sens...
This article explores how different missing data mechanisms and handling techniques affect the fairness of machine learning algorithms, r...
This article presents a deep generative model that utilizes physical quantities to generate and retrieve solar magnetic active regions, e...
This article discusses the limitations of using Fréchet Inception Distance (FID) as an evaluation metric for generative models in retinal...
This paper presents FT-Topic, a novel approach for topic modeling that fine-tunes large language models (LLMs) using bags of sentences, o...
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