Search Results for author:
Found 11 papers, 3 papers with code
Multi-Objective Evolutionary Neural Architecture Search for Recurrent Neural Networks
Artificial neural network (NN) architecture design is a nontrivial and time-consuming task that often requires a high level of human expertise.
Distal Interference: Exploring the Limits of Model-Based Continual Learning
Continual learning is the sequential learning of different tasks by a machine learning model.
Empirical Loss Landscape Analysis of Neural Network Activation Functions
Activation functions play a significant role in neural network design by enabling non-linearity.
Cauchy Loss Function: Robustness Under Gaussian and Cauchy Noise
In supervised machine learning, the choice of loss function implicitly assumes a particular noise distribution over the data.
Genetic Micro-Programs for Automated Software Testing with Large Path Coverage
Ongoing progress in computational intelligence (CI) has led to an increased desire to apply CI techniques for the purpose of improving software engineering processes, particularly software testing.
Comparision Of Adversarial And Non-Adversarial LSTM Music Generative Models
Algorithmic music composition is a way of composing musical pieces with minimal to no human intervention.
Black-Box Saliency Map Generation Using Bayesian Optimisation
This is achieved by a sampling-based approach to model perturbations that seeks to localise salient regions of an image to the black-box model.
Loss Surface Modality of Feed-Forward Neural Network Architectures
An increase in the hidden layer width is shown to effectively reduce the number of local minima, and simplify the shape of the global attractor.
Visualising Basins of Attraction for the Cross-Entropy and the Squared Error Neural Network Loss Functions
Quantification of the stationary points and the associated basins of attraction of neural network loss surfaces is an important step towards a better understanding of neural network loss surfaces at large.
Unsupervised Change Detection in Satellite Images Using Convolutional Neural Networks
This paper proposes an efficient unsupervised method for detecting relevant changes between two temporally different images of the same scene.
Evolutionary Neural Architecture Search for Image Restoration
Convolutional neural network (CNN) architectures have traditionally been explored by human experts in a manual search process that is time-consuming and ineffectively explores the massive space of potential solutions.
