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UMKC Announces New Master of Science in Artificial Intelligence
Ai Infrastructure

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...

AI News - General · 4 min ·
[2603.13793] GhanaNLP Parallel Corpora: Comprehensive Multilingual Resources for Low-Resource Ghanaian Languages
Nlp

[2603.13793] GhanaNLP Parallel Corpora: Comprehensive Multilingual Resources for Low-Resource Ghanaian Languages

Abstract page for arXiv paper 2603.13793: GhanaNLP Parallel Corpora: Comprehensive Multilingual Resources for Low-Resource Ghanaian Langu...

arXiv - AI · 4 min ·
[2602.08482] CLEAR: A Knowledge-Centric Vessel Trajectory Analysis Platform
Llms

[2602.08482] CLEAR: A Knowledge-Centric Vessel Trajectory Analysis Platform

Abstract page for arXiv paper 2602.08482: CLEAR: A Knowledge-Centric Vessel Trajectory Analysis Platform

arXiv - AI · 3 min ·

All Content

[2602.22529] Generative Agents Navigating Digital Libraries
Llms

[2602.22529] Generative Agents Navigating Digital Libraries

The paper introduces Agent4DL, a simulator for user search behavior in digital libraries, leveraging large language models to generate re...

arXiv - AI · 3 min ·
[2602.22551] A Fast and Practical Column Generation Approach for Identifying Carcinogenic Multi-Hit Gene Combinations
Nlp

[2602.22551] A Fast and Practical Column Generation Approach for Identifying Carcinogenic Multi-Hit Gene Combinations

This paper presents a novel approach to identifying carcinogenic multi-hit gene combinations using a fast column generation method, signi...

arXiv - Machine Learning · 3 min ·
[2602.22547] Towards Dynamic Dense Retrieval with Routing Strategy
Machine Learning

[2602.22547] Towards Dynamic Dense Retrieval with Routing Strategy

The paper presents a novel approach to dense retrieval called Dynamic Dense Retrieval (DDR), which addresses limitations in adapting mode...

arXiv - Machine Learning · 4 min ·
[2602.22544] HARU-Net: Hybrid Attention Residual U-Net for Edge-Preserving Denoising in Cone-Beam Computed Tomography
Machine Learning

[2602.22544] HARU-Net: Hybrid Attention Residual U-Net for Edge-Preserving Denoising in Cone-Beam Computed Tomography

HARU-Net introduces a novel deep learning architecture for denoising cone-beam computed tomography (CBCT) images, enhancing edge preserva...

arXiv - Machine Learning · 4 min ·
[2602.22522] Efficient Dialect-Aware Modeling and Conditioning for Low-Resource Taiwanese Hakka Speech Processing
Machine Learning

[2602.22522] Efficient Dialect-Aware Modeling and Conditioning for Low-Resource Taiwanese Hakka Speech Processing

This article presents a novel framework for improving automatic speech recognition (ASR) for the low-resource Taiwanese Hakka language by...

arXiv - AI · 4 min ·
[2602.22533] A Synergistic Approach: Dynamics-AI Ensemble in Tropical Cyclone Forecasting
Machine Learning

[2602.22533] A Synergistic Approach: Dynamics-AI Ensemble in Tropical Cyclone Forecasting

This article presents a novel AI-driven ensemble forecasting system for tropical cyclones, optimizing computational efficiency while main...

arXiv - Machine Learning · 3 min ·
[2602.22521] TFPS: A Temporal Filtration-enhanced Positive Sample Set Construction Method for Implicit Collaborative Filtering
Machine Learning

[2602.22521] TFPS: A Temporal Filtration-enhanced Positive Sample Set Construction Method for Implicit Collaborative Filtering

The paper presents TFPS, a method for enhancing positive sample construction in implicit collaborative filtering through temporal filtrat...

arXiv - Machine Learning · 4 min ·
[2602.22486] Flow Matching is Adaptive to Manifold Structures
Machine Learning

[2602.22486] Flow Matching is Adaptive to Manifold Structures

The paper explores flow matching as a robust method for generative modeling, particularly in high-dimensional data concentrated near low-...

arXiv - Machine Learning · 4 min ·
[2602.22488] Explainability-Aware Evaluation of Transfer Learning Models for IoT DDoS Detection Under Resource Constraints
Machine Learning

[2602.22488] Explainability-Aware Evaluation of Transfer Learning Models for IoT DDoS Detection Under Resource Constraints

This article evaluates transfer learning models for IoT DDoS detection, focusing on explainability and resource constraints. It analyzes ...

arXiv - AI · 3 min ·
[2602.22432] LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees
Machine Learning

[2602.22432] LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees

LoBoost introduces a novel method for local conformal prediction in gradient-boosted trees, enhancing uncertainty quantification without ...

arXiv - Machine Learning · 3 min ·
[2602.22437] veScale-FSDP: Flexible and High-Performance FSDP at Scale
Llms

[2602.22437] veScale-FSDP: Flexible and High-Performance FSDP at Scale

The paper introduces veScale-FSDP, a new system for Fully Sharded Data Parallel (FSDP) that enhances flexibility and performance for larg...

arXiv - Machine Learning · 3 min ·
[2602.22434] GetBatch: Distributed Multi-Object Retrieval for ML Data Loading
Machine Learning

[2602.22434] GetBatch: Distributed Multi-Object Retrieval for ML Data Loading

GetBatch introduces a new object store API that enhances batch retrieval in machine learning data loading, achieving significant performa...

arXiv - Machine Learning · 3 min ·
[2602.22300] Testable Learning of General Halfspaces under Massart Noise
Ai Infrastructure

[2602.22300] Testable Learning of General Halfspaces under Massart Noise

This paper presents a novel algorithm for testably learning general Massart halfspaces under Gaussian noise, achieving near-optimal error...

arXiv - Machine Learning · 3 min ·
[2602.22289] What Topological and Geometric Structure Do Biological Foundation Models Learn? Evidence from 141 Hypotheses
Llms

[2602.22289] What Topological and Geometric Structure Do Biological Foundation Models Learn? Evidence from 141 Hypotheses

The paper investigates the geometric and topological structures learned by biological foundation models, analyzing 141 hypotheses through...

arXiv - Machine Learning · 4 min ·
[2602.22381] Enhancing Renal Tumor Malignancy Prediction: Deep Learning with Automatic 3D CT Organ Focused Attention
Machine Learning

[2602.22381] Enhancing Renal Tumor Malignancy Prediction: Deep Learning with Automatic 3D CT Organ Focused Attention

This article presents a novel deep learning framework for predicting malignancy in renal tumors using 3D CT images, eliminating the need ...

arXiv - AI · 4 min ·
[2602.22282] Differentially Private Truncation of Unbounded Data via Public Second Moments
Nlp

[2602.22282] Differentially Private Truncation of Unbounded Data via Public Second Moments

This paper presents a novel approach to differentially private data truncation using public second moments, enhancing privacy without com...

arXiv - Machine Learning · 4 min ·
[2602.22376] AeroDGS: Physically Consistent Dynamic Gaussian Splatting for Single-Sequence Aerial 4D Reconstruction
Machine Learning

[2602.22376] AeroDGS: Physically Consistent Dynamic Gaussian Splatting for Single-Sequence Aerial 4D Reconstruction

AeroDGS presents a novel framework for 4D reconstruction from monocular UAV videos, addressing challenges in depth ambiguity and motion e...

arXiv - AI · 4 min ·
[2602.22278] RETLLM: Training and Data-Free MLLMs for Multimodal Information Retrieval
Llms

[2602.22278] RETLLM: Training and Data-Free MLLMs for Multimodal Information Retrieval

The paper presents RETLLM, a novel framework for multimodal information retrieval (MMIR) that operates without the need for training or l...

arXiv - Machine Learning · 4 min ·
[2602.22275] Deep Accurate Solver for the Geodesic Problem
Nlp

[2602.22275] Deep Accurate Solver for the Geodesic Problem

This article presents a novel deep learning approach for accurately solving the geodesic problem on continuous surfaces, achieving third-...

arXiv - Machine Learning · 4 min ·
[2602.22239] VAE-MS: An Asymmetric Variational Autoencoder for Mutational Signature Extraction
Machine Learning

[2602.22239] VAE-MS: An Asymmetric Variational Autoencoder for Mutational Signature Extraction

The paper introduces VAE-MS, an Asymmetric Variational Autoencoder designed to enhance mutational signature extraction in cancer research...

arXiv - Machine Learning · 4 min ·
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