Papers Classification
“Classification” 태그가 달린 논문 12,815편 · 필터 해제
CACTUS as a Reliable Tool for Early Classification of Age-related Macular Degeneration
Machine Learning (ML) is used to tackle various tasks, such as disease classification and prediction. The effectiveness of ML models relies heavily on having large amounts of complete data. However, healthcare data is of…
ClassificationEarly ClassificationGraph Semi-Supervised Learning for Point Classification on Data Manifolds
We propose a graph semi-supervised learning framework for classification tasks on data manifolds. Motivated by the manifold hypothesis, we model data as points sampled from a low-dimensional manifold $\mathcal{M} \subset…
ClassificationGraph Neural Networkimage-classificationImage Classification+1Monotone Classification with Relative Approximations
In monotone classification, the input is a multi-set $P$ of points in $\mathbb{R}^d$, each associated with a hidden label from $\{-1, 1\}$. The goal is to identify a monotone function $h$, which acts as a classifier, map…
ClassificationMUDAS: Mote-scale Unsupervised Domain Adaptation in Multi-label Sound Classification
Unsupervised Domain Adaptation (UDA) is essential for adapting machine learning models to new, unlabeled environments where data distribution shifts can degrade performance. Existing UDA algorithms are designed for singl…
ClassificationDomain AdaptationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Adversarial Surrogate Risk Bounds for Binary Classification
A central concern in classification is the vulnerability of machine learning models to adversarial attacks. Adversarial training is one of the most popular techniques for training robust classifiers, which involves minim…
Binary ClassificationClassificationInherently Faithful Attention Maps for Vision Transformers
We introduce an attention-based method that uses learned binary attention masks to ensure that only attended image regions influence the prediction. Context can strongly affect object perception, sometimes leading to bia…
ClassificationRobust classificationHyperspectral Image Classification via Transformer-based Spectral-Spatial Attention Decoupling and Adaptive Gating
Deep neural networks face several challenges in hyperspectral image classification, including high-dimensional data, sparse distribution of ground objects, and spectral redundancy, which often lead to classification over…
ClassificationHyperspectral Image Classificationimage-classificationImage ClassificationCXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray
The CXR-LT series is a community-driven initiative designed to enhance lung disease classification using chest X-rays (CXR). It tackles challenges in open long-tailed lung disease classification and enhances the measurab…
ClassificationDiagnosticLung Disease ClassificationZero-shot Generalization+1TokenBreak: Bypassing Text Classification Models Through Token Manipulation
Natural Language Processing (NLP) models are used for text-related tasks such as classification and generation. To complete these tasks, input data is first tokenized from human-readable text into a format the model can …
Classificationtext-classificationText ClassificationQuantum Graph Transformer for NLP Sentiment Classification
Quantum machine learning is a promising direction for building more efficient and expressive models, particularly in domains where understanding complex, structured data is critical. We present the Quantum Graph Transfor…
ClassificationLanguage ModelingLanguage ModellingQuantum Machine Learning+2Bio-Inspired Classification: Combining Information Theory and Spiking Neural Networks -- Influence of the Learning Rules
Training of Spiking Neural Networks (SNN) is challenging due to their unique properties, including temporal dynamics, non-differentiability of spike events, and sparse event-driven activations. In this paper, we widely c…
ClassificationComputational EfficiencyDomain Adaptation Method and Modality Gap Impact in Audio-Text Models for Prototypical Sound Classification
Audio-text models are widely used in zero-shot environmental sound classification as they alleviate the need for annotated data. However, we show that their performance severely drops in the presence of background sound …
ClassificationDomain AdaptationEnvironmental Sound ClassificationSound ClassificationRecent Advances in Medical Image Classification
Medical image classification is crucial for diagnosis and treatment, benefiting significantly from advancements in artificial intelligence. The paper reviews recent progress in the field, focusing on three levels of solu…
ClassificationExplainable artificial intelligenceimage-classificationImage Classification+1Adaptive Differential Denoising for Respiratory Sounds Classification
Automated respiratory sound classification faces practical challenges from background noise and insufficient denoising in existing systems. We propose Adaptive Differential Denoising network, that integrates noise suppre…
Audio ClassificationClassificationDenoisingDiagnostic+1Quantifying task-relevant representational similarity using decision variable correlation
Previous studies have compared the brain and deep neural networks trained on image classification. Intriguingly, while some suggest that their representations are highly similar, others argued the opposite. Here, we prop…
Classificationimage-classificationImage ClassificationLeveraging AM and FM Rhythm Spectrograms for Dementia Classification and Assessment
This study explores the potential of Rhythm Formant Analysis (RFA) to capture long-term temporal modulations in dementia speech. Specifically, we introduce RFA-derived rhythm spectrograms as novel features for dementia c…
ClassificationregressionRhythmChannel-Imposed Fusion: A Simple yet Effective Method for Medical Time Series Classification
The automatic classification of medical time series signals, such as electroencephalogram (EEG) and electrocardiogram (ECG), plays a pivotal role in clinical decision support and early detection of diseases. Although Tra…
ClassificationEEGElectroencephalogram (EEG)Time Series+1Uncertainty-Aware Genomic Classification of Alzheimer's Disease: A Transformer-Based Ensemble Approach with Monte Carlo Dropout
INTRODUCTION: Alzheimer's disease (AD) is genetically complex, complicating robust classification from genomic data. METHODS: We developed a transformer-based ensemble model (TrUE-Net) using Monte Carlo Dropout for uncer…
ClassificationRobust classificationPatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches
Deep learning techniques have enabled vast improvements in computer vision technologies. Nevertheless, these models are vulnerable to adversarial patch attacks which catastrophically impair performance. The physically re…
Binary ClassificationClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONGeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language Models
Classifying geospatial imagery remains a major bottleneck for applications such as disaster response and land-use monitoring-particularly in regions where annotated data is scarce or unavailable. Existing tools (e.g., RS…
ClassificationDisaster Responseimage-classificationImage Classification+6