MUlTI-LABEL-ClASSIFICATION
390 papers with code • 0 benchmarks • 0 datasets
multilabel graph classification with highest result
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Use these libraries to find MUlTI-LABEL-ClASSIFICATION models and implementationsMost implemented papers
Graph Attention Networks
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
node2vec: Scalable Feature Learning for Networks
Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
We propose sparsemax, a new activation function similar to the traditional softmax, but able to output sparse probabilities.
Learning to diagnose from scratch by exploiting dependencies among labels
The field of medical diagnostics contains a wealth of challenges which closely resemble classical machine learning problems; practical constraints, however, complicate the translation of these endpoints naively into classical architectures.
Multi-Task Learning as Multi-Objective Optimization
These algorithms are not directly applicable to large-scale learning problems since they scale poorly with the dimensionality of the gradients and the number of tasks.
SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis
In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair.
Asymmetric Loss For Multi-Label Classification
In this paper, we introduce a novel asymmetric loss ("ASL"), which operates differently on positive and negative samples.
SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations
We present a feature vector formation technique for documents - Sparse Composite Document Vector (SCDV) - which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation.
Extremely Randomized CNets for Multi-label Classification
Promising approaches for MLC are those able to capture label dependencies by learning a single probabilistic model—differently from other competitive approaches requiring to learn many models.
ML-Net: multi-label classification of biomedical texts with deep neural networks
Due to this nature, the multi-label text classification task is often considered to be more challenging compared to the binary or multi-class text classification problems.