Abstract: Data augmentation helps improve generalization capabilities of deep neural networks when only limited ground-truth training data are available. In this letter, we propose test-time ...

Our results show that enabled mixed precision can gain up to a $1.9\times $ speedup compared to the most common and default float32 data type on GPUs. Deploying the models on Edge TPU further boosted ...

Approximate Bayesian inference ... of the training data. Consequently, in a Bayesian neural network, these weights will be sampled from the prior. A MAP solution on the other hand will set these ...

Recurrent Neural Networks (RNNs), on the other hand, have linear complexity, which increases their computational efficiency. However, due to the constraints placed on their hidden state, which needs ...

The Training 4.0 test ... one half of neural network performance, the other half being so-called inference, where the finished neural network makes predictions as it receives new data.