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?
This article presents a novel approach using physics-informed graph neural networks to estimate hemodynamic flow fields in carotid arteri...
This paper presents a method for optimizing the allocation of observations between explainable and black box models, aiming to maximize e...
This paper presents a novel approach for fair community detection in heterogeneous graphical models, ensuring demographic representation ...
The paper introduces HARPOON, a novel method for generating tabular data using generalized manifold guidance, addressing limitations in e...
The paper presents GRXForm, a novel approach for molecular optimization using Group Relative Policy Optimization, addressing the limitati...
The paper introduces Temporal Pair Consistency (TPC), a novel approach to reduce variance in flow matching for continuous-time generative...
This article presents a reversible deep learning model for 13C NMR in chemoinformatics, utilizing an invertible neural network to predict...
This article presents a novel approach to multi-objective bandit problems through the Probe-then-Commit (PtC) strategy, demonstrating the...
This paper explores cooperative multi-objective bandits under adversarial corruption, presenting a communication-corruption coupling that...
This paper presents Adaptive Community Search (AdaptCS), a novel framework designed to improve community search in heterophilic graphs, o...
The paper explores how phase-space entropy at the acquisition stage can predict the learnability of downstream models, offering a new met...
This paper introduces Guided Transfer Learning (GTL) for discrete diffusion models, addressing challenges in small-data scenarios and off...
This paper introduces a novel technique for amortized posterior estimation using Normalizing Flows, enhancing inference in high-dimension...
This paper presents a novel framework for incomplete multi-view clustering using Hierarchical Semantic Alignment and Cooperative Completi...
The paper presents MIST, a novel approach for estimating mutual information using a neural network trained on a large dataset of syntheti...
The paper introduces FATE, a benchmark series for formal algebra, designed to assess large language models' capabilities in advanced math...
This study compares Physics-Informed Neural Networks (PINNs) and traditional physics models for non-invasive glucose monitoring under noi...
This paper introduces Flexible Evidential Deep Learning (F-EDL), enhancing uncertainty quantification in machine learning by extending th...
This article presents a novel method for accurately determining total oxidant concentration in non-thermal plasma systems using image pro...
This paper presents Gradient-Sign Masking, a method for transferring task vectors across pre-trained models without additional fine-tunin...
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