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The impact of randomness on model training is poorly understood.
SGD on Neural Networks Learns Functions of Increasing Complexity
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Best practices for convolutional neural networks applied to visual document analysis
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Learning multiple layers of features from tiny images
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Euclid in a taxicab: Sparse blind deconvolution with smoothed regularization
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Frequent directions: Simple and deterministic matrix sketching
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Deep residual learning for image recognition
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Tuning large neural networks via zero-shot hyperparameter transfer
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Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning
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