We propose a practical method for $L_0$ norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization....
Sparse Bayesian Neural Networks: Bridging Model and Parameter Uncertainty through Scalable Variational Inference 2024, Mathematics A statistical modelling approach to feedforward neural network model selection 2024, Statistical Modelling Bayesian Sparsification for Deep Neural Networks With Bayesian Model Reduction...
To overcome some difficulties, various researchers have also tried to investigate shallower network solutions: these can be sparse neural networks, instead of fully connected architectures, or more likely single hidden layers as ELM (Extreme Learning Machine) [66]. When compared to the shallow archite...
Deep learning is a complex machine learning algorithm that involves learning inherent rules and representation levels of sample data through large neural networks with multiple layers. It is popular for its automatic feature extraction capabilities and is applied in various areas such as CNN, LSTM, RN...
[12] proposes to prune the unimportant connections with small weights in trained neural networks. The resulting network’s weights are mostly zeros thus the storage space can be reduced by storing the model in a sparse format. However, these methods can only achieve speedup with dedicated sparse...
Throughout this analysis we will make heavy use of the singular value decomposition of the weight matrix, which defines matricesU,S, andVsuch thatW = USVT. The matrixSis diagonal, and the diagonal elements are called the singular valuesσi. TheUandVmatrices are orthonormal. ...
Behavioural feedback is critical for learning in the cerebral cortex. However, such feedback is often not readily available. How the cerebral cortex learns efficiently despite the sparse nature of feedback remains unclear. Inspired by recent deep learnin
[12] proposes to prune the unimportant connections with small weights in trained neural networks. The resulting network’s weights are mostly zeros thus the storage space can be reduced by storing the model in a sparse format. However, these methods can only achieve speedup with dedicated sparse...
Recurrent, sparse networks can transform temporal signals into a higher-dimensional dynamical feature space51,52, which is advantageous for machine learning applications involving dynamically evolving data53. Furthermore, the computational burden of training network weights can be circumvented altogether by ...
Spotting brain bleeding after sparse training Accurate and explainable detection, via deep learning, of acute intracranial haemorrhage from computed tomography images of the head is achievable with small amounts of data for model training. Michael C. Muelly ...