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Found 14 papers, 3 papers with code
Chinese WPLC: A Chinese Dataset for Evaluating Pretrained Language Models on Word Prediction Given Long-Range Context
Experiment results show that the Chinese pretrained language model PanGu-\alpha is 45 points behind human in terms of top-1 word prediction accuracy, indicating that Chinese WPLC is a challenging dataset.
Identity information based on human magnetocardiography signals
We have developed an individual identification system based on magnetocardiography (MCG) signals captured using optically pumped magnetometers (OPMs).
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.
TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series
Given the lack of data, limited resources, semantic context requirements, and so on, this work focuses on TS-for-LLM, where we aim to activate LLM's ability for TS data by designing a TS embedding method suitable for LLM.
EE-TTS: Emphatic Expressive TTS with Linguistic Information
EE-TTS contains an emphasis predictor that can identify appropriate emphasis positions from text and a conditioned acoustic model to synthesize expressive speech with emphasis and linguistic information.
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.
A Holistic Review on Advanced Bi-directional EV Charging Control Algorithms
The rapid growth of electric vehicles (EVs) has promised a next-generation transportation system with reduced carbon emission.
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.
