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Found 4 papers, 1 papers with code
Neural Machine Translation Models Can Learn to be Few-shot Learners
The emergent ability of Large Language Models to use a small number of examples to learn to perform in novel domains and tasks, also called in-context learning (ICL).
Early Warning Signals of Social Instabilities in Twitter Data
The goal of this project is to create and study novel techniques to identify early warning signals for socially disruptive events, like riots, wars, or revolutions using only publicly available data on social media.
The Topological BERT: Transforming Attention into Topology for Natural Language Processing
To the best of our knowledge, this work is the first to confront topological-based models with adversarial attacks in the context of NLP.
Persformer: A Transformer Architecture for Topological Machine Learning
In this article, we introduce $\texttt{Persformer}$, the first Transformer neural network architecture that accepts persistence diagrams as input.
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