Search:
Match:
7 results
Research Paper#Medical AI, Voice Analysis, Machine Learning🔬 ResearchAnalyzed: Jan 3, 2026 08:52

AI-Driven Voice Biomarker Classification of Voice Disorders

Published:Dec 31, 2025 05:04
1 min read
ArXiv

Analysis

This paper presents a novel hierarchical machine learning framework for classifying benign laryngeal voice disorders using acoustic features from sustained vowels. The approach, mirroring clinical workflows, offers a potentially scalable and non-invasive tool for early screening, diagnosis, and monitoring of vocal health. The use of interpretable acoustic biomarkers alongside deep learning techniques enhances transparency and clinical relevance. The study's focus on a clinically relevant problem and its demonstration of superior performance compared to existing methods make it a valuable contribution to the field.
Reference

“The proposed system consistently outperformed flat multi-class classifiers and pre-trained self-supervised models.”

PermalinkArXiv
Research Paper#Machine Learning, Classification, Class Imbalance🔬 ResearchAnalyzed: Jan 3, 2026 16:54

Improved Balanced Classification with Novel Loss Functions

Published:Dec 30, 2025 02:34
1 min read
ArXiv

Analysis

This paper addresses the challenge of class imbalance in multi-class classification, a common problem in machine learning. It introduces two new families of surrogate loss functions, GLA and GCA, designed to improve performance in imbalanced datasets. The theoretical analysis of consistency and the empirical results demonstrating improved performance over existing methods make this paper significant for researchers and practitioners working with imbalanced data.
Reference

“GCA losses are $H$-consistent for any hypothesis set that is bounded or complete, with $H$-consistency bounds that scale more favorably as $1/\sqrt{\mathsf p_{\min}}$, offering significantly stronger theoretical guarantees in imbalanced settings.”

PermalinkArXiv
Research Paper#Large Language Models (LLMs), Machine Learning, Multi-Expert Systems🔬 ResearchAnalyzed: Jan 3, 2026 19:28

Learning with Multi-Expert Deferral for LLMs

Published:Dec 28, 2025 11:33
1 min read
ArXiv

Analysis

This paper addresses critical challenges of Large Language Models (LLMs) such as hallucinations and high inference costs. It proposes a framework for learning with multi-expert deferral, where uncertain inputs are routed to more capable experts and simpler queries to smaller models. This approach aims to improve reliability and efficiency. The paper provides theoretical guarantees and introduces new algorithms with empirical validation on benchmark datasets.
Reference

“The paper introduces new surrogate losses and proves strong non-asymptotic, hypothesis set-specific consistency guarantees, resolving existing open questions.”

PermalinkArXiv
Research Paper#Machine Learning Theory🔬 ResearchAnalyzed: Jan 3, 2026 19:29

H-Consistency Bounds for Machine Learning

Published:Dec 28, 2025 11:02
1 min read
ArXiv

Analysis

This paper introduces and analyzes H-consistency bounds, a novel approach to understanding the relationship between surrogate and target loss functions in machine learning. It provides stronger guarantees than existing methods like Bayes-consistency and H-calibration, offering a more informative perspective on model performance. The work is significant because it addresses a fundamental problem in machine learning: the discrepancy between the loss optimized during training and the actual task performance. The paper's comprehensive framework and explicit bounds for various surrogate losses, including those used in adversarial settings, are valuable contributions. The analysis of growth rates and minimizability gaps further aids in surrogate selection and understanding model behavior.
Reference

“The paper establishes tight distribution-dependent and -independent bounds for binary classification and extends these bounds to multi-class classification, including adversarial scenarios.”

PermalinkArXiv
Paper#NLP, Hope Speech Detection, Multilingual, Low-Resource Languages, Transformers🔬 ResearchAnalyzed: Jan 3, 2026 16:22

Multilingual Hope Speech Detection Framework for Low-Resource Languages

Published:Dec 27, 2025 21:23
1 min read
ArXiv

Analysis

This paper addresses the under-representation of hope speech in NLP, particularly in low-resource languages like Urdu. It leverages pre-trained transformer models (XLM-RoBERTa, mBERT, EuroBERT, UrduBERT) to create a multilingual framework for hope speech detection. The focus on Urdu and the strong performance on the PolyHope-M 2025 benchmark, along with competitive results in other languages, demonstrates the potential of applying existing multilingual models in resource-constrained environments to foster positive online communication.
Reference

“Evaluations on the PolyHope-M 2025 benchmark demonstrate strong performance, achieving F1-scores of 95.2% for Urdu binary classification and 65.2% for Urdu multi-class classification, with similarly competitive results in Spanish, German, and English.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Dec 25, 2025 00:13

Zero-Shot Segmentation for Multi-Label Plant Species Identification via Prototype-Guidance

Published:Dec 24, 2025 05:00
1 min read
ArXiv AI

Analysis

This paper introduces a novel approach to multi-label plant species identification using zero-shot segmentation. The method leverages class prototypes derived from the training dataset to guide a segmentation Vision Transformer (ViT) on test images. By employing K-Means clustering to create prototypes and a customized ViT architecture pre-trained on individual species classification, the model effectively adapts from multi-class to multi-label classification. The approach demonstrates promising results, achieving fifth place in the PlantCLEF 2025 challenge. The small performance gap compared to the top submission suggests potential for further improvement and highlights the effectiveness of prototype-guided segmentation in addressing complex image analysis tasks. The use of DinoV2 for pre-training is also a notable aspect of the methodology.
Reference

“Our solution focused on employing class prototypes obtained from the training dataset as a proxy guidance for training a segmentation Vision Transformer (ViT) on the test set images.”

PermalinkArXiv AI
Research#AI in Healthcare📝 BlogAnalyzed: Jan 3, 2026 06:08

Presentation on DPC Coding at Applied AI R&D Meetup

Published:Nov 24, 2025 14:50
1 min read
Zenn NLP

Analysis

The article discusses a presentation on DPC/PDPS and Clinical Coding related to a hospital product. Clinical Coding involves converting medical records into standard classification codes, primarily ICD-10 for diseases and medical procedure codes in Japan. The task is characterized by a large number of classes, significant class imbalance (rare diseases), and is likely a multi-class classification problem.
Reference

“Clinical Coding is the technology that converts information from medical records regarding a patient's condition, diagnosis, treatment, etc., into codes of some standard classification system. In Japan, for diseases, it is mostly converted to ICD-10 (International Classification of Diseases, 10th edition), and for procedures, it is converted to codes from the medical treatment behavior master. This task is characterized by a very large number of classes, a significant bias in class occurrence rates (rare diseases occur in about one in several hundred thousand people), and...”

PermalinkZenn NLP