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Deep learning regularization: Prevent overfitting effectively explained

Regularization in Deep Learning is very important to overcome overfitting. When your training accuracy is very high, but test ...
IEEE

A Hybrid Stochastic-Binary Computing Batch Normalization Engine for Low-Power On-Chip Learning Spiking Neural Networks

Abstract: Batch normalization (BN) has proven to be a critical component in speeding up the training of deep spiking neural networks in deep learning. However, conventional BN implementations face ...
The New York Times

This Kind of Sleep Is Essential for a Healthy Brain

Two particular phases in your nightly routine seem to play outsize roles in cognitive health. By Mohana Ravindranath A good night’s sleep isn’t just about the number of hours you log. Getting quality ...
GitHub

Add batch&group&instance&layer normalization

Batch Normalization (BN) is a widely used technique that helps to accelerate the training of deep neural networks and improve model performance. By normalizing the inputs to each layer so that they ...
Scientific Research Publishing

Zhao, C., Badrinarayanan, V., Lee, C.-Y. and Rabinovich, A. (2018) Gradnorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks. International ...

ABSTRACT: Delirium is a common yet critical condition among Intensive Care Unit (ICU) patients, characterized by acute cognitive disturbances that can lead to severe complications, prolonged hospital ...
eLife

Homeostatic synaptic normalization optimizes learning in network models of neural population codes

This work is an important contribution to the development of a biologically plausible theory of statistical modeling of spiking activity. The authors convincingly implemented the statistical inference ...
Scientific Research Publishing

Ioffe, S. and Szegedy, C. (2015) Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

ABSTRACT: Deep learning for time series sequence individual data instance classification can revolutionize computer assisted navigation by providing surgeons with accurate, real-time instrument ...
eLife

Homeostatic synaptic normalization optimizes learning in network models of neural population codes

Studying and understanding the code of large neural populations hinge on accurate statistical models of population activity. A novel class of models, based on learning to weigh sparse nonlinear Random ...
IEEE

Experimenting With Normalization Layers in Federated Learning on Non-IID Scenarios

Abstract: Training Deep Learning (DL) models require large, high-quality datasets, often assembled with data from different institutions. Federated Learning (FL) has been emerging as a method for ...