Meta-learning considers the problem of learning an efficient learning pr...
Fast contextual adaptation has shown to be effective in improving Automa...
Humans can quickly associate stimuli to solve problems in novel contexts...
Self-supervised pre-training of transformer models has revolutionized NL...
Training a deep neural network requires a large amount of single-task da...
Machine learning models with both good predictability and high
interpret...
Recent advances in NLP demonstrate the effectiveness of training large-s...
We augment recurrent neural networks with an external memory mechanism t...
We propose a neural machine-reading model that constructs dynamic knowle...
We unify recent neural approaches to one-shot learning with older ideas ...
Sentence simplification aims to simplify the content and structure of co...
We describe a mechanism by which artificial neural networks can learn ra...
A hallmark of human intelligence and cognition is its flexibility. One o...
Neural networks have been successfully applied in applications with a la...
Deep neural networks (DNNs) have made significant progress in a number o...
Hypothesis testing is an important cognitive process that supports human...
Recurrent neural networks (RNNs) process input text sequentially and mod...
We present a memory augmented neural network for natural language
unders...