Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Adversarial Sampling for Active Learning
This paper proposes asal, a new GAN based active learning method that generates high entropy samples. Instead of directly annotating the synthetic samples, ASAL searches similar samples from the pool and includes them fo…
Active LearningGeneral ClassificationMulti-Label ClassificationJoint & Progressive Learning from High-Dimensional Data for Multi-Label Classification
Despite the fact that nonlinear subspace learning techniques (e.g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generaliza…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONVocal Bursts Intensity PredictionOff-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy
When learning from a batch of logged bandit feedback, the discrepancy between the policy to be learned and the off-policy training data imposes statistical and computational challenges. Unlike classical supervised learni…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONOff-policy evaluationRecommendation SystemsLearning Emotion-enriched Word Representations
Most word representation learning methods are based on the distributional hypothesis in linguistics, according to which words that are used and occur in the same contexts tend to possess similar meanings. As a consequenc…
Emotion ClassificationGeneral ClassificationMulti-Label ClassificationRepresentation Learning+1Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained with Noise Signals
Supervised learning based methods for source localization, being data driven, can be adapted to different acoustic conditions via training and have been shown to be robust to adverse acoustic environments. In this paper,…
Binary ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRecurrently Exploring Class-wise Attention in A Hybrid Convolutional and Bidirectional LSTM Network for Multi-label Aerial Image Classification
Aerial image classification is of great significance in remote sensing community, and many researches have been conducted over the past few years. Among these studies, most of them focus on categorizing an image into one…
General Classificationimage-classificationImage ClassificationMulti-Label Classification+1Learning Deep Network Representations with Adversarially Regularized Autoencoders
The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can cap…
Link PredictionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONNetwork Embedding+1Evaluating Word Embeddings in Multi-label Classification Using Fine-grained Name Typing
Embedding models typically associate each word with a single real-valued vector, representing its different properties. Evaluation methods, therefore, need to analyze the accuracy and completeness of these properties in …
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSentence+1Contextual Memory Trees
We design and study a Contextual Memory Tree (CMT), a learning memory controller that inserts new memories into an experience store of unbounded size. It is designed to efficiently query for memories from that store, sup…
General ClassificationImage CaptioningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Synthetic Sampling for Multi-Class Malignancy Prediction
We explore several oversampling techniques for an imbalanced multi-label classification problem, a setting often encountered when developing models for Computer-Aided Diagnosis (CADx) systems. While most CADx systems aim…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPrediction+1Fully Scalable Gaussian Processes using Subspace Inducing Inputs
We introduce fully scalable Gaussian processes, an implementation scheme that tackles the problem of treating a high number of training instances together with high dimensional input data. Our key idea is a representatio…
Extreme Multi-Label ClassificationGaussian ProcessesGeneral ClassificationMulti-Label Classification+1Active Testing: An Efficient and Robust Framework for Estimating Accuracy
Much recent work on visual recognition aims to scale up learning to massive, noisily-annotated datasets. We address the problem of scaling- up the evaluation of such models to large-scale datasets with noisy labels. Curr…
Instance SegmentationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSemantic SegmentationHierarchical Multi-Label Classification Networks
One of the most challenging machine learning problems is a particular case of data classification in which classes are hierarchically structured and objects can be assigned to multiple paths of the class hierarchy a…
ClassificationGeneral ClassificationHierarchical Multi-label ClassificationMulti-Label Classification+4Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Linear encoding of sparse vectors is widely popular, but is commonly data-independent -- missing any possible extra (but a priori unknown) structure beyond sparsity. In this paper we present a new method to learn linear …
compressed sensingDecoderExtreme Multi-Label ClassificationMulti-Label Classification+3SGM: Sequence Generation Model for Multi-label Classification
Multi-label classification is an important yet challenging task in natural language processing. It is more complex than single-label classification in that the labels tend to be correlated. Existing methods tend to ignor…
ClassificationDecoderGeneral Classificationmodel+2Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations
Many structured prediction problems (particularly in vision and language domains) are ambiguous, with multiple outputs being correct for an input - e.g. there are many ways of describing an image, multiple ways of transl…
DiversityMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONQuestion Generation+3On the Importance of Attention in Meta-Learning for Few-Shot Text Classification
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce. We address this problem by integrating a meta-learning procedure t…
ClassificationFew-Shot Text ClassificationGeneral ClassificationInductive Bias+7Relevant Emotion Ranking from Text Constrained with Emotion Relationships
Text might contain or invoke multiple emotions with varying intensities. As such, emotion detection, to predict multiple emotions associated with a given text, can be cast into a multi-label classification problem. We wo…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningTopic ModelsEMR Coding with Semi-Parametric Multi-Head Matching Networks
Coding EMRs with diagnosis and procedure codes is an indispensable task for billing, secondary data analyses, and monitoring health trends. Both speed and accuracy of coding are critical. While coding errors could lead t…
Few-Shot LearningGeneral ClassificationMulti-Label ClassificationMulti-Label Text Classification+2Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification
In this paper, we describe our contribution in SemEval-2018 contest. We tackled task 1 {``}Affect in Tweets{''}, subtask E-c {``}Detecting Emotions (multi-label classification){''}. A multilabel classification system Tw-…
ClassificationEmotion ClassificationGeneral ClassificationLemmatization+4