Papers Time-Series Few-Shot Learning with Heterogeneous Channels

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Few-Shot Forecasting of Time-Series with Heterogeneous Channels

2022-04-07 · Lukas Brinkmeyer, Rafael Rego Drumond, Johannes Burchert, Lars Schmidt-Thieme

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. Leveraging learning experience with similar datasets is a well-establ…

Classification Time Series Time Series Analysis Time-Series Few-Shot Learning with Heterogeneous Channels+1

Meta-learning from Tasks with Heterogeneous Attribute Spaces

2020-12-01 · NeurIPS 2020 12 · Tomoharu Iwata, Atsutoshi Kumagai

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 lab…

Attribute Meta-Learning Time-Series Few-Shot Learning with Heterogeneous Channels

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

2019-05-24 · ICLR 2020 1 · Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio

We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected l…

Time Series Time Series Analysis Time-Series Few-Shot Learning with Heterogeneous Channels Time Series Forecasting+1

Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline

2016-11-20 · Zhiguang Wang, Weizhong Yan, Tim Oates

We propose a simple but strong baseline for time series classification from scratch with deep neural networks. Our proposed baseline models are pure end-to-end without any heavy preprocessing on the raw data or feature c…

General Classification Time Series Time Series Analysis Time Series Classification+1

Generative Adversarial Networks

2014-06-10 · Proceedings of the 27th International Conference on Neural Information Processing Systems 2014 12 · Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu 외

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 …

Graph Property Prediction Super-Resolution Time-Series Few-Shot Learning with Heterogeneous Channels