Time-Series Few-Shot Learning with Heterogeneous Channels
5 papers with code • 1 benchmarks • 1 datasets
These leaderboards are used to track progress in Time-Series Few-Shot Learning with Heterogeneous Channels
Most implemented papers
Generative Adversarial Networks
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.
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 Classification from Scratch with Deep Neural Networks: A Strong Baseline
We propose a simple but strong baseline for time series classification from scratch with deep neural networks.
Meta-learning from Tasks with Heterogeneous Attribute Spaces
We propose a heterogeneous meta-learning method that trains a model on tasks with various attribute spaces, such that it can solve unseen tasks whose attribute spaces are different from the training tasks given a few labeled instances.
Few-Shot Forecasting of Time-Series with Heterogeneous Channels
Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set.