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...
![[2602.23188] Efficient Real-Time Adaptation of ROMs for Unsteady Flows Using Data Assimilation](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This article presents a novel retraining strategy for Reduced Order Models (ROMs) that enhances real-time adaptation for unsteady flows u...
This paper explores how deep learning can better handle tabular data by addressing its limitations compared to tree-based methods, partic...
This article explores how protein language models (PLMs) detect repeating segments in protein sequences, revealing mechanisms for identif...
DS SERVE is a framework designed to enhance neural retrieval systems by efficiently processing large-scale text datasets, achieving low l...
The paper presents the FinSurvival 2025 Challenge, focusing on benchmarking temporal Web3 intelligence using 21.8 million transaction rec...
This article evaluates misinformation exposure on the Chinese web by comparing traditional search engines, LLMs, and AI-generated overvie...
The paper discusses a method for recovering meter-scale surface weather data by integrating sparse surface measurements with high-resolut...
This paper presents a hybrid tensor completion method for predicting temperature-dependent diffusion coefficients in binary mixtures, enh...
The paper presents DyGnROLE, a transformer-based model for dynamic graphs that distinguishes between source and destination nodes to impr...
The paper presents new disagreement-based certificates for generalization bounds in deep learning models, addressing limitations of exist...
The paper presents Taxoria, a novel pipeline that enhances existing taxonomies using Large Language Models (LLMs), addressing issues of l...
This paper presents a novel physics-informed training framework for neural operators that enhances their ability to generalize beyond tra...
This article presents a multi-agent LLM framework for financial trading, emphasizing fine-grained task decomposition to enhance decision-...
This article examines the effectiveness of large language models (LLMs) in enhancing novice users' performance on complex biological task...
This paper introduces a physics-informed neural particle flow method for the Bayesian update step, addressing computational challenges in...
The paper presents GRAVE2, GRAVER, and GRAVER2, enhanced algorithms for Generalized Rapid Action Value Estimation, addressing memory cons...
RhythmBERT is a novel self-supervised language model designed for ECG waveform analysis, enhancing heart disease detection by treating EC...
This article presents a novel approach to reducing epistemic uncertainty in AI models through invariant transformation and resampling tec...
The paper presents a novel constrained optimization framework for training deep state-space models (DSSMs), introducing the extended Kalm...
The paper presents ODEBrain, a Neural ODE framework designed to model dynamic brain networks using continuous-time EEG data, improving fo...
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