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...
![[2602.23035] Learning Disease-Sensitive Latent Interaction Graphs From Noisy Cardiac Flow Measurements](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This paper presents a novel framework for modeling cardiac blood flow patterns using disease-sensitive latent interaction graphs, enhanci...
The CXReasonAgent integrates large language models with diagnostic tools for improved reasoning in chest X-ray interpretations, addressin...
This article examines the scaling laws of global weather models, focusing on the relationship between model performance, dataset size, an...
This study explores a machine learning approach to predict membrane fouling in patients undergoing Continuous Renal Replacement Therapy (...
The article presents MEDNA-DFM, a novel dual-view FiLM-MoE model designed for explainable DNA methylation prediction, highlighting its pe...
The paper presents PATRA, a novel model for Time Series Question Answering that enhances reasoning by incorporating pattern awareness and...
This article presents a novel hypernetwork-based framework for grid-independent functional data clustering, addressing limitations of exi...
This paper introduces AILS-AHD, a novel approach that utilizes Large Language Models to enhance the Capacitated Vehicle Routing Problem (...
This paper presents a reinforcement learning approach to optimize multi-agent race strategies in Formula 1, focusing on energy management...
The paper introduces KMLP, a hybrid architecture designed for scalable predictive modeling on web-scale tabular data, addressing challeng...
This study compares set-based and distribution-based representations of epistemic uncertainty in neural networks, highlighting their rela...
The paper introduces RepSPD, a novel geometric deep learning model that enhances the representation of EEG data through dynamic graphs, i...
The paper introduces Certified Circuits, a framework that enhances the stability and accuracy of circuit discovery in neural networks, ad...
The paper introduces Switch-Hurdle, a novel framework combining a Mixture-of-Experts encoder and an autoregressive hurdle decoder to impr...
This article presents a machine learning framework for forecasting antimicrobial resistance (AMR) trends using WHO GLASS data, highlighti...
The paper presents LEDA, a novel model for universal graph pre-training that addresses challenges in aligning diverse graph data and enha...
The paper presents MUG, a novel approach for universal heterogeneous graph pre-training, addressing challenges in encoding diverse graph ...
FlexMS is a new framework designed for benchmarking deep learning models used in mass spectrum prediction within metabolomics, addressing...
This paper presents Layer-wise MIA-risk-aware DP-SGD, a method to reduce Membership Inference Attack risks in machine learning models by ...
The paper introduces DP-aware AdaLN-Zero, a novel mechanism to mitigate heavy-tailed gradients in differentially private diffusion models...
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