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Found 4 papers, 1 papers with code
Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks
On the other hand, symbolic rules such as regular expressions are interpretable, require no training, and often achieve decent accuracy; but rules cannot benefit from labeled data when available and hence underperform neural networks in rich-resource scenarios.
Learning Numeral Embedding
Numeral embeddings represented in this manner can be plugged into existing word embedding learning approaches such as skip-gram for training.
Learning Numeral Embeddings
Numeral embeddings represented in this manner can be plugged into existing word embedding learning approaches such as skip-gram for training.
Latent Dependency Forest Models
Probabilistic modeling is one of the foundations of modern machine learning and artificial intelligence.
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