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Found 7 papers, 3 papers with code
Unlocking Deep Learning: A BP-Free Approach for Parallel Block-Wise Training of Neural Networks
This includes decisions on how to decouple network blocks and which auxiliary networks to use for each block.
Discovering Malicious Signatures in Software from Structural Interactions
Malware represents a significant security concern in today's digital landscape, as it can destroy or disable operating systems, steal sensitive user information, and occupy valuable disk space.
Leader-Follower Neural Networks with Local Error Signals Inspired by Complex Collectives
Motivated by the intricacy of these collectives, we propose a neural network (NN) architecture inspired by the rules observed in nature's collective ensembles.
Fractional dynamics foster deep learning of COPD stage prediction
The authors show that the fractional dynamic deep learning model (FDDLM) achieves a COPD prediction accuracy of 98. 66% and can serve as a robust alternative to spirometry.
Raising The Limit Of Image Rescaling Using Auxiliary Encoding
While the random sampling of latent variable $z$ is useful in generating diverse photo-realistic images, it is not desirable for image rescaling when accurate restoration of the HR image is more important.
Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations
Coupled partial differential equations (PDEs) are key tasks in modeling the complex dynamics of many physical processes.
Non-Markovian Reinforcement Learning using Fractional Dynamics
Reinforcement learning (RL) is a technique to learn the control policy for an agent that interacts with a stochastic environment.
