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Found 10 papers, 3 papers with code
Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce.
TACTiS: Transformer-Attentional Copulas for Time Series
The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance.
Meta-learning framework with applications to zero-shot time-series forecasting
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?
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
We focus on solving the univariate times series point forecasting problem using deep learning.
Time Series
Time-Series Few-Shot Learning with Heterogeneous Channels
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Learning to Learn with Conditional Class Dependencies
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.
CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance
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.
Adversarially Learned Mixture Model
The Adversarially Learned Mixture Model (AMM) is a generative model for unsupervised or semi-supervised data clustering.
On the Importance of Attention in Meta-Learning for Few-Shot Text Classification
Based on the Model-Agnostic Meta-Learning framework (MAML), we introduce the Attentive Task-Agnostic Meta-Learning (ATAML) algorithm for text classification.
HeMIS: Hetero-Modal Image Segmentation
We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities.
Effective Bayesian Modeling of Groups of Related Count Time Series
Time series of counts arise in a variety of forecasting applications, for which traditional models are generally inappropriate.
