Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce.
The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance.
Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new TS coming from different datasets?
We focus on solving the univariate times series point forecasting problem using deep learning.
Neural networks can learn to extract statistical properties from data, but they seldom make use of structured information from the label space to help representation learning.
We introduce CASED, a novel curriculum sampling algorithm that facilitates the optimization of deep learning segmentation or detection models on data sets with extreme class imbalance.
The Adversarially Learned Mixture Model (AMM) is a generative model for unsupervised or semi-supervised data clustering.
Based on the Model-Agnostic Meta-Learning framework (MAML), we introduce the Attentive Task-Agnostic Meta-Learning (ATAML) algorithm for text classification.
We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities.