Search Results for author:
Found 5 papers, 1 papers with code
Pruning Convolutional Filters using Batch Bridgeout
However, the huge size of contemporary models results in large inference costs and limits their use on resource-limited devices.
Sparseout: Controlling Sparsity in Deep Networks
We found that sparsity of the activations is favorable for language modelling performance while image classification benefits from denser activations.
A parallel implementation of the covariance matrix adaptation evolution strategy
In many practical optimization problems, the derivatives of the functions to be optimized are unavailable or unreliable.
Bridgeout: stochastic bridge regularization for deep neural networks
Stochastic methods such as Dropout and Shakeout, in expectation, are equivalent to imposing a ridge and elastic-net penalty on the model parameters, respectively.
Prediction of Muscle Activations for Reaching Movements using Deep Neural Networks
In this work, we investigate deep autoencoders for the prediction of muscle activation trajectories for point-to-point reaching movements.
