Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Generation of Consistent Sets of Multi-Label Classification Rules with a Multi-Objective Evolutionary Algorithm
Multi-label classification consists in classifying an instance into two or more classes simultaneously. It is a very challenging task present in many real-world applications, such as classification of biology, image, vid…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONChallenging the adversarial robustness of DNNs based on error-correcting output codes
The existence of adversarial examples and the easiness with which they can be generated raise several security concerns with regard to deep learning systems, pushing researchers to develop suitable defense mechanisms. Th…
Adversarial AttackAdversarial RobustnessGeneral ClassificationMulti-Label Classification+1Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN
Recent advances in deep learning have provided procedures for learning one network to amalgamate multiple streams of knowledge from the pre-trained Convolutional Neural Network (CNN) models, thus reduce the annotation co…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-label natural language processing to identify diagnosis and procedure codes from MIMIC-III inpatient notes
In the United States, 25% or greater than 200 billion dollars of hospital spending accounts for administrative costs that involve services for medical coding and billing. With the increasing number of patient records, ma…
Language ModellingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONFrImCla: A Framework for Image Classification Using Traditional and Transfer Learning Techniques
Deep learning techniques are currently the state of the art approach to deal with image classification problems. Nevertheless, non-expert users might find challenging the use of these techniques due to several reasons, i…
ClassificationCPUGeneral Classificationimage-classification+4Minimal spiking neuron for solving multi-label classification tasks
The Multi-Spike Tempotron (MST) is a powerful single spiking neuron model that can solve complex supervised classification tasks. While powerful, it is also internally complex, computationally expensive to evaluate, and …
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels
Disease diagnosis on chest X-ray images is a challenging multi-label classification task. Previous works generally classify the diseases independently on the input image without considering any correlation among diseases…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONEnd-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification
The most common approach to speaker diarization is clustering of speaker embeddings. However, the clustering-based approach has a number of problems; i.e., (i) it is not optimized to minimize diarization errors directly,…
ClusteringGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Optimistic bounds for multi-output prediction
We investigate the challenge of multi-output learning, where the goal is to learn a vector-valued function based on a supervised data set. This includes a range of important problems in Machine Learning including multi-t…
General ClassificationMulti-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Enzyme promiscuity prediction using hierarchy-informed multi-label classification
As experimental efforts are costly and time consuming, computational characterization of enzyme capabilities is an attractive alternative. We present and evaluate several machine-learning models to predict which of 983 d…
BIG-bench Machine LearningClassificationGeneral ClassificationMulti-Label Classification+2Understanding patient complaint characteristics using contextual clinical BERT embeddings
In clinical conversational applications, extracted entities tend to capture the main subject of a patient's complaint, namely symptoms or diseases. However, they mostly fail to recognize the characterizations of a compla…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONCheXclusion: Fairness gaps in deep chest X-ray classifiers
Machine learning systems have received much attention recently for their ability to achieve expert-level performance on clinical tasks, particularly in medical imaging. Here, we examine the extent to which state-of-the-a…
AllDiagnosticFairnessMedical Diagnosis+2Description Based Text Classification with Reinforcement Learning
The task of text classification is usually divided into two stages: {\it text feature extraction} and {\it classification}. In this standard formalization categories are merely represented as indexes in the label vocabul…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+7Differentiable Forward and Backward Fixed-Point Iteration Layers
Recently, several studies proposed methods to utilize some classes of optimization problems in designing deep neural networks to encode constraints that conventional layers cannot capture. However, these methods are stil…
DenoisingImage DenoisingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Multi-label Relation Modeling in Facial Action Units Detection
This paper describes an approach to the facial action units detections. The involved action units (AU) include AU1 (Inner Brow Raiser), AU2 (Outer Brow Raiser), AU4 (Brow Lowerer), AU6 (Cheek Raise), AU12 (Lip Corner Pul…
General ClassificationMulti-Label ClassificationMulti-Label Text ClassificationRelation+1Dialogue-Based Simulation For Cultural Awareness Training
Existing simulations designed for cultural and interpersonal skill training rely on pre-defined responses with a menu option selection interface. Using a multiple-choice interface and restricting trainees' responses may …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Cultural Vocal Bursts Intensity PredictionManagement+5Beyond Classification: Whole Slide Tissue Histopathology Analysis By End-To-End Part Learning
An emerging technology in cancer care and research is the use of histopathology whole slide images (WSI). Leveraging computation methods to aid in WSI assessment poses unique challenges. WSIs, being extremely high resolu…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONwhole slide imagesBigEarthNet Dataset with A New Class-Nomenclature for Remote Sensing Image Understanding
This paper presents BigEarthNet that is a large-scale Sentinel-2 multispectral image dataset with a new class nomenclature to advance deep learning (DL) studies in remote sensing (RS). BigEarthNet is made up of 590,326 i…
Content-Based Image RetrievalImage RetrievalMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1ExEm: Expert Embedding using dominating set theory with deep learning approaches
A collaborative network is a social network that is comprised of experts who cooperate with each other to fulfill a special goal. Analyzing this network yields meaningful information about the expertise of these experts …
Graph EmbeddingLink PredictionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Residual Block-based Multi-Label Classification and Localization Network with Integral Regression for Vertebrae Labeling
Accurate identification and localization of the vertebrae in CT scans is a critical and standard preprocessing step for clinical spinal diagnosis and treatment. Existing methods are mainly based on the integration of mul…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONregression