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Found 19 papers, 8 papers with code
Macroscopic auxiliary asymptotic preserving neural networks for the linear radiative transfer equations
We develop a Macroscopic Auxiliary Asymptotic-Preserving Neural Network (MA-APNN) method to solve the time-dependent linear radiative transfer equations (LRTEs), which have a multi-scale nature and high dimensionality.
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.
A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations
We propose a model-data asymptotic-preserving neural network(MD-APNN) method to solve the nonlinear gray radiative transfer equations(GRTEs).
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.
Optical Flow for Video Super-Resolution: A Survey
Video super-resolution is currently one of the most active research topics in computer vision as it plays an important role in many visual applications.
Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition
In recent years, graph convolutional networks (GCNs) play an increasingly critical role in skeleton-based human action recognition.
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.
MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
Electrocardiography (ECG) signals are commonly used to diagnose various cardiac abnormalities.
RDPD: Rich Data Helps Poor Data via Imitation
In many situations, we need to build and deploy separate models in related environments with different data qualities.
GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions.
Generative Adversarial Network for Abstractive Text Summarization
In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization.
Ranked #5 on
Text Summarization
on CNN / Daily Mail (Anonymized)
Abstractive Text Summarization
Generative Adversarial Network
+2
ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks
We propose ENCASE to combine expert features and DNNs (Deep Neural Networks) together for ECG classification.
Ranked #1 on
Time Series Classification
on Physionet 2017 Atrial Fibrillation
(F1 (Hidden Test Set) metric)
