Gradient-free Policy Architecture Search and Adaptation

We develop a method for policy architecture search and adaptation via gradient-free optimization which can learn to perform autonomous driving tasks. By learning from both demonstration and environmental reward we develop a model that can learn with relatively few early catastrophic failures. We first learn an architecture of appropriate complexity to perceive aspects of world state relevant to the expert demonstration, and then mitigate the effect of domain-shift during deployment by adapting a policy demonstrated in a source domain to rewards obtained in a target environment. We show that our approach allows safer learning than baseline methods, offering a reduced cumulative crash metric over the agent's lifetime as it learns to drive in a realistic simulated environment.Comment: Accepted in Conference on Robot Learning, 201

Three-Stage Adjusted Regression Forecasting (TSARF) for Software Defect Prediction

Software reliability growth models (SRGM) enable failure data collected during testing. Specifically, nonhomogeneous Poisson process (NHPP) SRGM are the most commonly employed models. While software reliability growth models are important, efficient modeling of complex software systems increases the complexity of models. Increased model complexity presents a challenge in identifying robust and computationally efficient algorithms to identify model parameters and reduces the generalizability of the models. Existing studies on traditional software reliability growth models suggest that NHPP models characterize defect data as a smooth continuous curve and fail to capture changes in the defect discovery process. Therefore, the model fits well under ideal conditions, but it is not adaptable and will only fit appropriately shaped data. Neural networks and other machine learning methods have been applied to greater effect [5], however limited due to lack of large samples of defect data especially at earlier stages of testing

Rock-burst occurrence prediction based on optimized naïve bayes models

Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence. © 2013 IEEE

Dynamic learning with neural networks and support vector machines

Neural network approach has proven to be a universal approximator for nonlinear continuous functions with an arbitrary accuracy. It has been found to be very successful for various learning and prediction tasks. However, supervised learning using neural networks has some limitations because of the black box nature of their solutions, experimental network parameter selection, danger of overfitting, and convergence to local minima instead of global minima. In certain applications, the fixed neural network structures do not address the effect on the performance of prediction as the number of available data increases. Three new approaches are proposed with respect to these limitations of supervised learning using neural networks in order to improve the prediction accuracy.;Dynamic learning model using evolutionary connectionist approach . In certain applications, the number of available data increases over time. The optimization process determines the number of the input neurons and the number of neurons in the hidden layer. The corresponding globally optimized neural network structure will be iteratively and dynamically reconfigured and updated as new data arrives to improve the prediction accuracy. Improving generalization capability using recurrent neural network and Bayesian regularization. Recurrent neural network has the inherent capability of developing an internal memory, which may naturally extend beyond the externally provided lag spaces. Moreover, by adding a penalty term of sum of connection weights, Bayesian regularization approach is applied to the network training scheme to improve the generalization performance and lower the susceptibility of overfitting. Adaptive prediction model using support vector machines . The learning process of support vector machines is focused on minimizing an upper bound of the generalization error that includes the sum of the empirical training error and a regularized confidence interval, which eventually results in better generalization performance. Further, this learning process is iteratively and dynamically updated after every occurrence of new data in order to capture the most current feature hidden inside the data sequence.;All the proposed approaches have been successfully applied and validated on applications related to software reliability prediction and electric power load forecasting. Quantitative results show that the proposed approaches achieve better prediction accuracy compared to existing approaches

Deep Learning: Our Miraculous Year 1990-1991

In 2020, we will celebrate that many of the basic ideas behind the deep learning revolution were published three decades ago within fewer than 12 months in our "Annus Mirabilis" or "Miraculous Year" 1990-1991 at TU Munich. Back then, few people were interested, but a quarter century later, neural networks based on these ideas were on over 3 billion devices such as smartphones, and used many billions of times per day, consuming a significant fraction of the world's compute.Comment: 37 pages, 188 references, based on work of 4 Oct 201

Modelling and trading the Greek stock market with gene expression and genetic programing algorithms

This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter