Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Generative Pre-Trained Transformer for Cardiac Abnormality Detection
ECG heartbeat classification plays a vital role in diagnosis of cardiac arrhythmia. The goal of the Physionet/CinC 2021 challenge was to accurately classify clinical diagnosis based on 12, 6, 4, 3 or 2-lead ECG recording…
Anomaly DetectionClassificationHeartbeat ClassificationMulti-Label Classification+3Tribuo: Machine Learning with Provenance in Java
Machine Learning models are deployed across a wide range of industries, performing a wide range of tasks. Tracking these models and ensuring they behave appropriately is becoming increasingly difficult as the number of d…
Anomaly DetectionBIG-bench Machine LearningMulti-class ClassificationMulti-Label Classification+1ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts
Reviewing contracts is a time-consuming procedure that incurs large expenses to companies and social inequality to those who cannot afford it. In this work, we propose "document-level natural language inference (NLI) for…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONNatural Language InferenceDeep Subspace analysing for Semi-Supervised multi-label classification of Diabetic Foot Ulcer
Diabetes is a global raising pandemic. Diabetes patients are at risk of developing foot ulcer that usually leads to limb amputation. In order to develop a self monitoring mobile application, in this work, we propose a no…
Data AugmentationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTransfer LearningSound Event Detection Transformer: An Event-based End-to-End Model for Sound Event Detection
Sound event detection (SED) has gained increasing attention with its wide application in surveillance, video indexing, etc. Existing models in SED mainly generate frame-level prediction, converting it into a sequence mul…
Audio TaggingBoundary DetectionEvent DetectionMulti-Label Classification+5Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees. Our calibration algorithms work with any underlying model and (unknown) d…
BIG-bench Machine LearningInstance SegmentationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2LexGLUE: A Benchmark Dataset for Legal Language Understanding in English
Laws and their interpretations, legal arguments and agreements\ are typically expressed in writing, leading to the production of vast corpora of legal text. Their analysis, which is at the center of legal practice, becom…
Multi-class ClassificationMulti-Label ClassificationMultiple Choice Question Answering (MCQA)Natural Language Understanding+1Predicting COVID-19 Patient Shielding: A Comprehensive Study
There are many ways machine learning and big data analytics are used in the fight against the COVID-19 pandemic, including predictions, risk management, diagnostics, and prevention. This study focuses on predicting COVID…
ManagementMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks
We use Hypergraph Attention Networks (HyperGAT) to recognize multiple labels of Chinese humor texts. We firstly represent a joke as a hypergraph. The sequential hyperedge and semantic hyperedge structures are used to con…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONIncorporating Domain Knowledge into Language Transformers for Multi-Label Classification of Chinese Medical Questions
In this paper, we propose a knowledge infusion mechanism to incorporate domain knowledge into language transformers. Weakly supervised data is regarded as the main source for knowledge acquisition. We pre-train the langu…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONM3TR: Multi-modal Multi-label Recognition with Transformer
Multi-label image recognition aims to recognize multiple objects simultaneously in one image. Recent ideas to solve this problem have focused on learning dependencies of label co-occurrences to enhance the high-level sem…
Multi-Label ClassificationMulti-Label Image RecognitionOn Riemannian Approach for Constrained Optimization Model in Extreme Classification Problems
We propose a novel Riemannian method for solving the Extreme multi-label classification problem that exploits the geometric structure of the sparse low-dimensional local embedding models. A constrained optimization probl…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRiemannian optimizationOne Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective
A deep hashing model typically has two main learning objectives: to make the learned binary hash codes discriminative and to minimize a quantization error. With further constraints such as bit balance and code orthogonal…
AllDeep HashingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Can multi-label classification networks know what they don't know?
Estimating out-of-distribution (OOD) uncertainty is a central challenge for safely deploying machine learning models in the open-world environment. Improved methods for OOD detection in multi-class classification have em…
ClassificationMulti-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Structured Energy Network as a dynamic loss function. Case study. A case study with multi-label Classification
We propose SEAL which utilizes this energy network as a trainable loss function for a simple feedfoward network. Structured prediction energy networks (SPENs) (Belanger & McCallum, 2016; Gygli et al., 2017) have shown th…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONStructured PredictionModeling Label Space Interactions in Multi-label Classification using Box Embeddings
Multi-label classification is a challenging structured prediction task in which a set of output class labels are predicted for each input. Real-world datasets often have natural or latent taxonomic relationships between …
ClassificationHierarchical Multi-label ClassificationInductive BiasMulti-Label Classification+2TailMix: Overcoming the Label Sparsity for Extreme Multi-label Classification
Extreme multi-label classification (XMC) aims at finding the most relevant labels from a huge label set at the industrial scale. The XMC problem inherently poses two challenges: data scalability and label sparsity. This …
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONContrastively Enforcing Distinctiveness for Multi-Label Classification
Recently, as an effective way of learning latent representations, contrastive learning has been increasingly popular and successful in various domains. The success of constrastive learning in single-label classifications…
ClassificationContrastive Learningimage-classificationImage Classification+3Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization
In this paper we show that a simple, data dependent way of setting the initial vector can be used to substantially speed up the training of linear one-versus-all (OVA) classifiers in extreme multi-label classification (X…
AllExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONIntegrating Unsupervised Clustering and Label-specific Oversampling to Tackle Imbalanced Multi-label Data
There is often a mixture of very frequent labels and very infrequent labels in multi-label datatsets. This variation in label frequency, a type class imbalance, creates a significant challenge for building efficient mult…
ClusteringMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION