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...
![[2602.19502] Human-Guided Agentic AI for Multimodal Clinical Prediction: Lessons from the AgentDS Healthcare Benchmark](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
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...
This paper presents a novel approach to graph anomaly detection (GAD) that transitions from few-shot to zero-shot learning, enabling effe...
This article explores the integration of artificial intelligence with modeling and simulation in digital twins, highlighting their roles ...
CaliCausalRank presents a novel framework for optimizing multi-objective ad ranking systems, addressing challenges like score scale incon...
GLaDiGAtor is a novel graph neural network framework that enhances disease-gene association predictions by integrating language models an...
The paper presents ALPACA, a reinforcement learning environment designed for optimizing medication repurposing and treatment strategies i...
This article presents a novel approach to automate the generation of microfluidic netlists using large language models (LLMs), demonstrat...
The paper presents HONEST-CAV, a hierarchical framework for optimizing traffic flow in mixed environments of human-driven and automated v...
This article presents a novel approach to unsupervised multi-view clustering through Phase-Consistent Magnetic Spectral Learning, address...
This paper presents a comprehensive benchmark for Multi-Agent Reinforcement Learning (MARL) applied to urban energy management using the ...
This article explores how transformers can learn transfer operators for dynamical systems through in-context learning, enabling zero-shot...
This paper presents Large Causal Models (LCMs) designed for temporal causal discovery, addressing limitations of traditional dataset-spec...
The paper presents DoAtlas-1, a novel causal compilation paradigm for clinical AI that transforms medical evidence into executable code, ...
This paper explores the holographic encoding principle in neural networks, demonstrating that learned algorithms exhibit global low-rank ...
The paper presents InfoNoise, a data-adaptive noise scheduling method for diffusion training, enhancing efficiency and performance by uti...
The paper presents ARTIST, a novel approach to time series reasoning that utilizes adaptive segment selection to improve accuracy in answ...
The paper introduces Non-Interfering Weight Fields (NIWF), a novel framework that allows neural networks to extend capabilities without f...
The paper presents ETAP, a framework for predicting task affinity in multi-task learning, enhancing efficiency by grouping tasks that ben...
Get the latest news, tools, and insights delivered to your inbox.
Daily or weekly digest • Unsubscribe anytime