Data Science

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

Scientists uncover new method to generate protein datasets for training AI

White-collar workers are quietly rebelling against AI as 80% outright refuse adoption mandates

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[2602.01445] A Meta-Knowledge-Augmented LLM Framework for Hyperparameter Optimization in Time-Series Forecasting

The paper introduces LLM-AutoOpt, a novel framework that enhances hyperparameter optimization in time-series forecasting by integrating l...

[2602.02201] Cardinality-Preserving Attention Channels for Graph Transformers in Molecular Property Prediction

This article presents a novel graph transformer model, incorporating cardinality-preserving attention channels, to enhance molecular prop...

[2602.01051] SwiftRepertoire: Few-Shot Immune-Signature Synthesis via Dynamic Kernel Codes

The paper presents SwiftRepertoire, a framework for synthesizing immune signatures using few-shot learning techniques, enabling efficient...

[2505.07755] Benchmarking of CPU-intensive Stream Data Processing in The Edge Computing Systems

This article evaluates CPU-intensive stream data processing in edge computing systems, highlighting performance and power consumption opt...

[2601.16905] GRIP: Algorithm-Agnostic Machine Unlearning for Mixture-of-Experts via Geometric Router Constraints

The paper presents GRIP, a novel algorithm-agnostic framework for machine unlearning in Mixture-of-Experts architectures, addressing the ...

[2601.16443] Endless Terminals: Scaling RL Environments for Terminal Agents

The paper presents 'Endless Terminals', a scalable reinforcement learning (RL) environment designed for training terminal agents through ...

[2601.13190] LAViG-FLOW: Latent Autoregressive Video Generation for Fluid Flow Simulations

LAViG-FLOW introduces a novel framework for generating fluid flow simulations, significantly improving efficiency and consistency in mode...

[2512.13228] ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Data

ModSSC is an open-source Python framework designed for semi-supervised classification, enhancing reproducibility and experimentation acro...

[2512.12832] Network Level Evaluation of Hangup Susceptibility of HRGCs using Deep Learning and Sensing Techniques: A Goal Towards Safer Future

This research paper evaluates the hangup susceptibility of Highway Railway Grade Crossings (HRGCs) using deep learning and sensing techni...

[2502.12581] The Majority Vote Paradigm Shift: When Popular Meets Optimal

The article explores the Majority Vote (MV) method for data labeling, analyzing its optimality in aggregating labels from multiple annota...

[2501.07575] Dataset Distillation via Committee Voting

The paper presents a novel method for dataset distillation called Committee Voting for Dataset Distillation (CV-DD), which enhances data ...

[2512.00499] ESPO: Entropy Importance Sampling Policy Optimization

The paper presents ESPO, a novel framework for optimizing reinforcement learning in large language models, addressing training stability ...

[2511.22693] Generative Anchored Fields: Controlled Data Generation via Emergent Velocity Fields and Transport Algebra

The paper introduces Generative Anchored Fields (GAF), a novel generative model that enhances data generation through controlled interpol...

[2412.00686] LVLM-COUNT: Enhancing the Counting Ability of Large Vision-Language Models

The paper presents LVLM-COUNT, a method to enhance the counting ability of large vision-language models (LVLMs) by using a divide-and-con...

[2511.06781] On the Mechanisms of Collaborative Learning in VAE Recommenders

This paper explores the mechanisms of collaborative learning in Variational Autoencoder (VAE) recommenders, highlighting the role of late...

[2511.02077] Beyond Static Cutoffs: One-Shot Dynamic Thresholding for Diffusion Language Models

This article presents One-Shot Dynamic Thresholding (OSDT) for diffusion language models, enhancing decoding efficiency and accuracy by c...

[2510.14581] Model-agnostic Selective Labeling with Provable Statistical Guarantees

The paper presents 'Conformal Labeling', a model-agnostic method that ensures high-quality AI-generated labels by controlling the false d...

[2510.13654] Challenges and Requirements for Benchmarking Time Series Foundation Models

This article discusses the challenges and requirements for benchmarking Time Series Foundation Models (TSFMs), highlighting issues of inf...

[2510.10854] Discrete State Diffusion Models: A Sample Complexity Perspective

This article presents a theoretical framework for discrete-state diffusion models, offering the first sample complexity bounds and insigh...

[2510.07182] Bridged Clustering: Semi-Supervised Sparse Bridging

The paper introduces Bridged Clustering, a semi-supervised framework that learns predictors from unpaired datasets by clustering inputs a...