Speeding up diffraction analysis using machine learning

Speeding Up Evolutionary Learning Algorithms using GPUs

This paper propose a multithreaded Genetic Programming classification evaluation model using NVIDIA CUDA GPUs to reduce the computational time due to the poor performance in large problems. Two different classification algorithms are benchmarked using UCI Machine Learning data sets. Experimental results compare the performance using single and multithreaded Java, C and GPU code and show the eff...

Speeding Up Syntactic Learning Using Contextual Information

It has been shown in (Angluin and Becerra-Bonache, 2010, 2011) that interactions between a learner and a teacher can help language learning. In this paper, we make use of additional contextual information in a pairwise-based generative approach aiming at learning (situation,sentence)-pair-hidden markov models. We show that this allows a significant speed-up of the convergence of the syntactic l...

Speeding-up ProbLog's Parameter Learning

ProbLog is a state-of-art combination of logic programming and probabilities; in particular ProbLog offers parameter learning through a variant of the EM algorithm. However, the resulting learning algorithm is rather slow, even when the data are complete. In this short paper we offer some insights that lead to orders of magnitude improvements in ProbLog’s parameter learning speed with complete ...

Speeding-up Reinforcement Learning with

Speeding Up Dataflow Analysis Using Flow-Insensitive Pointer Analysis

In recent years, static analysis has increasingly been applied to the problem of program verification. Systems for program verification typically use precise and expensive interprocedural dataflow algorithms that are difficult to scale to large programs. An attractive way to scale these analyses is to use a preprocessing step to reduce the number of dataflow facts propagated by the analysis and...