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Automated Sizing and Training of Efficient Deep Autoencoders using Second Order Algorithms
We propose a multi-step training method for designing generalized linear classifiers.
Optimizing Performance of Feedforward and Convolutional Neural Networks through Dynamic Activation Functions
Deep learning training training algorithms are a huge success in recent years in many fields including speech, text, image video etc.
Optimal Input Gain: All You Need to Supercharge a Feed-Forward Neural Network
Starting from equivalent networks, it is shown that pre-processing inputs using linear transformation are equivalent to multiplying the negative gradient matrix with an autocorrelation matrix per training iteration.
Teacher-Student Knowledge Distillation for Radar Perception on Embedded Accelerators
Many radar signal processing methodologies are being developed for critical road safety perception tasks.
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