Data analysis, statistics, and data engineering
![[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?
Abstract page for arXiv paper 2509.09192: ReDef: Do Code Language Models Truly Understand Code Changes for Just-in-Time Software Defect P...
This paper explores the RKHS representation of algebraic convolutional filters using integral operators, establishing connections between...
TimeRadar introduces a novel approach to time series anomaly detection using a domain-rotatable foundation model that enhances the differ...
The paper presents NI-Tex, a method for generating non-isometric garment textures using a new dataset and advanced techniques for cross-p...
This paper explores iterative feedback loops in image generative models, introducing the concept of neural resonance and its implications...
The paper introduces DEFNet, a multitask-based deep evidential fusion network designed to enhance blind image quality assessment (BIQA) b...
This article presents a novel approach to inverse lithography using generative reinforcement learning, significantly improving mask quali...
This paper presents the Recurrent Structural Policy Gradient (RSPG) method for Partially Observable Mean Field Games (MFGs), achieving fa...
This paper investigates the complexity of training deep neural networks under a realistic bit-level model, contrasting it with traditiona...
The paper presents CoSiNE, a novel deep learning model for antibody sequence evolution that captures epistatic interactions and outperfor...
This paper presents a novel approach to gradient descent and stochastic gradient descent, demonstrating exponential convergence for separ...
The paper presents LoMime, a novel framework for membership inference attacks that operates efficiently under label-only conditions, sign...
The paper introduces Semi-Local Differential Privacy (SLDP), a framework that enhances privacy-preserving analytics by decoupling privacy...
The paper discusses OpenClaw, Moltbook, and ClawdLab, highlighting their role in creating a dataset for AI interactions and proposing Cla...
The paper presents HEHRGNN, a unified embedding model for knowledge graphs that incorporates hyperedges and hyper-relational edges, enhan...
This paper explores boosting techniques for vector-valued prediction and conditional density estimation, addressing theoretical gaps in a...
The paper introduces Hyperbolic Busemann Neural Networks, which enhance neural network components by adapting them to hyperbolic space, i...
The paper presents CFE, a multimodal benchmark for evaluating large language models' reasoning capabilities in STEM domains, highlightin...
This article explores how human-guided agentic AI can enhance multimodal clinical prediction, detailing its performance in the AgentDS He...
The paper presents L2G-Net, a novel spectral graph neural network that utilizes Cauchy factorizations to enhance the modeling of long-ran...
This paper presents a novel framework, Latent Dirichlet-Tree Allocation (LDTA), which enhances the traditional Latent Dirichlet Allocatio...
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