Towards fast hybrid deep kernel learning methods

El treball estudia la millor manera de crear xarxes neuronals híbrides amb mètodes kernel mitjançant dues aproximacions de kernel diferents, random Fourier features i el mètode Nystrom, i la millor manera d'entrenar-les, amb RMSprop i stochastic gradient descent

Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table

Mapping the geogenic radon potential for Germany by machine learning

The radioactive gas radon (Rn) is considered as an indoor air pollutant due to its detrimental effects on humanhealth. In fact, exposure to Rn belongs to the most important causes for lung cancer after tobacco smoking. Thedominant source of indoor Rn is the ground beneath the house. The geogenic Rn potential (GRP) - a functionof soil gas Rn concentration and soil gas permeability - quantifies what“earth delivers in terms of Rn”and rep-resents a hazard indicator for elevated indoor Rn concentration. In this study, we aim at developing an improvedspatial continuous GRP map based on 4448field measurements of GRP distributed across Germany. Wefittedthree different machine learning algorithms, multivariate adaptive regression splines, random forest and supportvector machines utilizing 36 candidate predictors. Predictor selection, hyperparameter tuning and performanceassessment were conducted using a spatial cross-validation where the data was iteratively left out by spatialblocks of 40 km*40 km. Thisprocedure counteracts the effectofspatial auto-correlation in predictorand responsedata and minimizes dependence of training and test data. The spatial cross-validated performance statistics re-vealed that random forest provided the most accurate predictions. The predictors selected as informative reflectgeology, climate (temperature,precipitation and soil moisture), soil hydraulic, soilphysical (field capacity, coarsefraction) and soil chemical properties (potassium and nitrogen concentration). Model interpretation techniquessuch as predictor importance as well as partial and spatial dependence plots confirmed the hypothesized domi-nant effect of geology on GRP, but also revealed significant contributions of the other predictors. Partial and spa-tial dependence plots gave further valuable insight into the quantitative predictor-response relationship and itsspatial distribution. A comparison with a previous version of the German GRP map using 1359 independent testdata indicates a significantly better performance of the random forest based map