7 citations · 10 across the 2 of their papers we have counts for
5 papers
Pruning Convolutional Filters using Batch Bridgeout
Najeeb Khan, Ian Stavness
State-of-the-art computer vision models are rapidly increasing in capacity, where the number of parameters far exceeds the number required to fit the training set. This results in…
Sparseout: Controlling Sparsity in Deep Networks
Najeeb Khan, Ian Stavness
Dropout is commonly used to help reduce overfitting in deep neural networks. Sparsity is a potentially important property of neural networks, but is not explicitly controlled by Dr…
A parallel implementation of the covariance matrix adaptation evolution strategy
Najeeb Khan
In many practical optimization problems, the derivatives of the functions to be optimized are unavailable or unreliable. Such optimization problems are solved using derivative-free…
Bridgeout: stochastic bridge regularization for deep neural networks
Najeeb Khan, Jawad Shah, Ian Stavness
A major challenge in training deep neural networks is overfitting, i.e. inferior performance on unseen test examples compared to performance on training examples. To reduce overfit…
Prediction of Muscle Activations for Reaching Movements using Deep Neural Networks
Najeeb Khan, Ian Stavness
The motor control problem involves determining the time-varying muscle activation trajectories required to accomplish a given movement. Muscle redundancy makes motor control a chal…