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Found 5 papers, 3 papers with code
Towards Cheaper Inference in Deep Networks with Lower Bit-Width Accumulators
The majority of the research on the quantization of Deep Neural Networks (DNNs) is focused on reducing the precision of tensors visible by high-level frameworks (e. g., weights, activations, and gradients).
How do Minimum-Norm Shallow Denoisers Look in Function Space?
Neural network (NN) denoisers are an essential building block in many common tasks, ranging from image reconstruction to image generation.
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?
Deep neural networks are typically initialized with random weights, with variances chosen to facilitate signal propagation and stable gradients.
Is Feature Diversity Necessary in Neural Network Initialization?
Standard practice in training neural networks involves initializing the weights in an independent fashion.
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off
Reducing the precision of weights and activation functions in neural network training, with minimal impact on performance, is essential for the deployment of these models in resource-constrained environments.
