Data analysis, statistics, and data engineering
UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...
Analysis of AI dominance in 2025 venture capital, its effects on market valuations, and strategic considerations for investors.
Hey all, I recently built an end-to-end fraud detection project using a large banking dataset: Trained an XGBoost model Used Databricks f...
![[2602.19130] Detecting labeling bias using influence functions](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This article explores the use of influence functions to detect labeling bias in datasets, demonstrating their effectiveness in identifyin...
This paper presents a robust Bayesian approach to random feature regression, addressing prior and likelihood misspecification through Hub...
The paper explores dynamic sample pruning techniques for spatio-temporal training, aiming to enhance training efficiency and model perfor...
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...
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