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Found 9 papers, 2 papers with code
Curriculum Design Helps Spiking Neural Networks to Classify Time Series
Spiking Neural Networks (SNNs) have a greater potential for modeling time series data than Artificial Neural Networks (ANNs), due to their inherent neuron dynamics and low energy consumption.
Curricular and Cyclical Loss for Time Series Learning Strategy
Experiments on 3 kinds of tasks and 5 real-world datasets show the benefits of CRUCIAL for most deep learning models when learning time series.
Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural Networks
Continuous diagnosis and prognosis are essential for intensive care patients.
Confidence-Guided Learning Process for Continuous Classification of Time Series
In the real world, the class of a time series is usually labeled at the final time, but many applications require to classify time series at every time point.
ACCTS: an Adaptive Model Training Policy for Continuous Classification of Time Series
For this demand, we propose a new concept, Continuous Classification of Time Series (CCTS), to achieve the high-accuracy classification at every time.
GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization
Each local model is learned from the local data and aligns with its distribution for customization.
TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data
In this work, we propose a novel Time Encoding (TE) mechanism.
A Review of Designs and Applications of Echo State Networks
Echo State Network (ESN) is simple type of RNNs and has emerged in the last decade as an alternative to gradient descent training based RNNs.
A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data
Developing deep learning methods on EHRs data is critical for personalized treatment, precise diagnosis and medical management.
