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Found 6 papers, 1 papers with code
Revisiting the Negative Data of Distantly Supervised Relation Extraction
Next, we formulate the problem of relation extraction into as a positive unlabeled learning task to alleviate false negative problem.
Ranked #1 on
Relation Extraction
on NYT11-HRL
Enabling Highly Efficient Capsule Networks Processing Through A PIM-Based Architecture Design
In recent years, the CNNs have achieved great successes in the image processing tasks, e. g., image recognition and object detection.
Collective Loss Function for Positive and Unlabeled Learning
On the contrary, traditional machine learning algorithms often rely on negative examples, otherwise the model would be prone to collapse and always-true predictions.
Rule Mining over Knowledge Graphs via Reinforcement Learning
Knowledge graphs (KGs) are an important source repository for a wide range of applications and rule mining from KGs recently attracts wide research interest in the KG-related research community.
I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization
In this paper we propose a novel hardware accelerator for GCN inference, called I-GCN, that significantly improves data locality and reduces unnecessary computation.
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning
In this paper, we propose a scoring model to automatically learn a model-based reward, and an effective training strategy based on curriculum learning is further proposed to stabilize the training process.
