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Found 14 papers, 2 papers with code
Distantly Supervised Relation Extraction using Multi-Layer Revision Network and Confidence-based Multi-Instance Learning
Distantly supervised relation extraction is widely used in the construction of knowledge bases due to its high efficiency.
Augmenting Document-level Relation Extraction with Efficient Multi-Supervision
Despite its popularity in sentence-level relation extraction, distantly supervised data is rarely utilized by existing work in document-level relation extraction due to its noisy nature and low information density.
Style Generation in Robot Calligraphy with Deep Generative Adversarial Networks
Robot calligraphy is an emerging exploration of artificial intelligence in the fields of art and education.
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
Graph-based semi-supervised learning (GSSL) has long been a hot research topic.
GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification
To resolve the problem, in this paper we seek to automatically augment the minority classes from the massive unlabelled nodes of the graph.
AIDA: Legal Judgment Predictions for Non-Professional Fact Descriptions via Partial-and-Imbalanced Domain Adaptation
In this paper, we study the problem of legal domain adaptation problem from an imbalanced source domain to a partial target domain.
Field-aware Variational Autoencoders for Billion-scale User Representation Learning
Moreover, we deploy the proposed method in real-world applications and conduct online A/B tests in a look-alike system.
NI-UDA: Graph Adversarial Domain Adaptation from Non-shared-and-Imbalanced Big Data to Small Imbalanced Applications
We propose a new general Graph Adversarial Domain Adaptation (GADA) based on semantic knowledge reasoning of class structure for solving the problem of unsupervised domain adaptation (UDA) from the big data with non-shared and imbalanced classes to specified small and imbalanced applications (NI-UDA), where non-shared classes mean the label space out of the target domain.
Recurrent Coupled Topic Modeling over Sequential Documents
The abundant sequential documents such as online archival, social media and news feeds are streamingly updated, where each chunk of documents is incorporated with smoothly evolving yet dependent topics.
Zero-shot Node Classification with Decomposed Graph Prototype Network
Node classification is a central task in graph data analysis.
Expanding Semantic Knowledge for Zero-shot Graph Embedding
Zero-shot graph embedding is a major challenge for supervised graph learning.
Micro-supervised Disturbance Learning: A Perspective of Representation Probability Distribution
To explore the representation learning capability under the continuous stimulation of the SPI, we present a deep Micro-supervised Disturbance Learning (Micro-DL) framework based on the Micro-DGRBM and Micro-DRBM models and compare it with a similar deep structure which has not any external stimulation.
Multi-local Collaborative AutoEncoder
In mcrRBM and mcrGRBM models, the structure and multi-local collaborative relationships of unlabeled data are integrated into their encoding procedure.
Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM
In this paper, we present a novel unsupervised feature learning architecture, which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering integration RBM (MIRBM).
