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Found 5 papers, 1 papers with code
Contrastive Learning on Multimodal Analysis of Electronic Health Records
To accommodate the statistical analysis of multimodal EHR data, in this paper, we propose a novel multimodal feature embedding generative model and design a multimodal contrastive loss to obtain the multimodal EHR feature representation.
A New Measure of Model Redundancy for Compressed Convolutional Neural Networks
While recently many designs have been proposed to improve the model efficiency of convolutional neural networks (CNNs) on a fixed resource budget, theoretical understanding of these designs is still conspicuously lacking.
Rethinking Compressed Convolution Neural Network from a Statistical Perspective
Many designs have recently been proposed to improve the model efficiency of convolutional neural networks (CNNs) at a fixed resource budget, while there is a lack of theoretical analysis to justify them.
Do RNN and LSTM have Long Memory?
The LSTM network was proposed to overcome the difficulty in learning long-term dependence, and has made significant advancements in applications.
Compact Autoregressive Network
Autoregressive networks can achieve promising performance in many sequence modeling tasks with short-range dependence.
