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Found 5 papers, 4 papers with code
Relation Specific Transformations for Open World Knowledge Graph Completion
We propose an open-world knowledge graph completion model that can be combined with common closed-world approaches (such as ComplEx) and enhance them to exploit text-based representations for entities unseen in training.
GVFs in the Real World: Making Predictions Online for Water Treatment
In this paper we investigate the use of reinforcement-learning based prediction approaches for a real drinking-water treatment plant.
Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks
We show that by either decomposing the network into independent modules or learning the network in stages, we can make RTRL scale linearly with the number of parameters.
An Open-World Extension to Knowledge Graph Completion Models
We present a novel extension to embedding-based knowledge graph completion models which enables them to perform open-world link prediction, i. e. to predict facts for entities unseen in training based on their textual description.
Distillation Techniques for Pseudo-rehearsal Based Incremental Learning
We discuss the biases in current Generative Adversarial Networks (GAN) based approaches that learn the classifier by knowledge distillation from previously trained classifiers.
