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Found 5 papers, 3 papers with code
Nondeterminism and Instability in Neural Network Optimization
In this work, we establish an experimental protocol for understanding the effect of optimization nondeterminism on model diversity, allowing us to isolate the effects of a variety of sources of nondeterminism.
On Nondeterminism and Instability in Neural Network Optimization
In this work, we establish an experimental protocol for understanding the effect of optimization nondeterminism on model diversity, which allows us to study the independent effects of a variety of sources of nondeterminism.
Improved Adversarial Robustness via Logit Regularization Methods
While great progress has been made at making neural networks effective across a wide range of visual tasks, most models are surprisingly vulnerable.
Four Things Everyone Should Know to Improve Batch Normalization
A key component of most neural network architectures is the use of normalization layers, such as Batch Normalization.
Improved Mixed-Example Data Augmentation
In order to reduce overfitting, neural networks are typically trained with data augmentation, the practice of artificially generating additional training data via label-preserving transformations of existing training examples.
