Data analysis, statistics, and data engineering
I have extensively searched on long video understanding datasets such as Video-MME, MLVU, VideoBench, LongVideoBench and etc. What I have...
UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...
MIT Professor Rafael Gómez-Bombarelli discusses the transformative potential of AI in scientific research, emphasizing its role in materi...
![[2602.22758] Decomposing Physician Disagreement in HealthBench](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This paper analyzes physician disagreement in the HealthBench dataset, identifying key factors contributing to variance in evaluations an...
The paper presents TabDLM, a novel framework for generating free-form tabular data using joint numerical-language diffusion, addressing c...
The paper presents Taesar, a data-centric framework designed to enhance recommendation model performance by addressing data sparsity and ...
This paper presents a framework for optimizing decision thresholds in machine learning to balance fairness and resource constraints, ensu...
The paper presents AHBid, a novel hierarchical bidding framework for cross-channel advertising that enhances budget allocation and adapta...
The paper presents AVDE, a novel framework for decoding visual information from EEG signals, addressing challenges in modality bridging a...
The paper presents Relatron, a system that automates relational machine learning over relational databases, addressing the challenges of ...
This paper explores the integration of psychometric rater models into AI evaluation, aiming to correct human label biases and improve the...
LUMOS introduces an innovative framework for scientific machine learning (SciML) that simplifies model design by integrating feature sele...
The paper introduces Persistent Nonnegative Matrix Factorization (pNMF), a novel approach that utilizes multi-scale graph regularization ...
The paper presents DyCausal, a framework for learning dynamic causal structures in time series data, addressing challenges of time-varyin...
The paper explores a machine learning approach to predict tennis serve directions, achieving around 49% accuracy for male players and 44%...
The paper presents TEFL, a novel framework for multi-horizon time series forecasting that utilizes prediction residuals to enhance accura...
This article reviews the integration of AI into life cycle assessment (LCA), highlighting trends, themes, and future directions using lar...
This paper presents a comprehensive analysis of masked diffusion models, focusing on their convergence rates and theoretical underpinning...
The paper presents ECHO, a scalable architecture for community detection in attributed networks, addressing limitations of topological al...
This paper explores the effectiveness of Chebyshev polynomial regressors and anisotropic RBF networks in tabular regression, benchmarking...
This article presents the Cognitive Abstraction and Reasoning Corpus (CogARC), a study exploring human abstract reasoning through problem...
This article presents a learning-based hybrid decision framework for matching systems, focusing on user departure detection to enhance ma...
MolFM-Lite introduces a multi-modal approach to molecular property prediction, integrating various molecular representations through adva...
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