Neural Collaborative Filtering

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -- collaborative filtering -- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.Comment: 10 pages, 7 figure

Air quality and urban sustainable development: the application of machine learning tools

[EN] Air quality has an efect on a population¿s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is refected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in diferent urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artifcial neural networks and support vector machines are the most widely used to predict diferent types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.Molina-Gómez, NI.; Díaz-Arévalo, JL.; López Jiménez, PA. (2021). Air quality and urban sustainable development: the application of machine learning tools. 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Generative Adversarial Neural Networks: A Pictorial Approach

This current work explores the basis of the ambit of Generative Modeling approached through Adversarial Neural Networks. The project starts by introducing Discriminative and Generative Modelling, both explained and set apart from one another, so that the latest, which is the main focus of this work, can be properly understood. Then, examples of Deep Learning architectures modeled as means to approach this kind of modeling, through a setting inspired in Game Theory, are showcased. In the end, a proof of concept to showcase the capabilities of this kind of approaches, capable of learning to reproduce the work of pictorial artist, referred to as PictorialGAN, is presented. In the annex there are references to the theoretical ambit over which this work rises, a whole detailed explanation of Multilayer Perceptrons, as well as the practical bricks used during the development of the project, those being the computational environment and the datasets used to test the models

Digital Camera Identification Using Neural Network Algorithm And Pattern Noise In Imaging Sensors

In forensic investigation of criminal cases like child pornography, image forgery, identity theft, steganography, movie piracy, insurance claims, and other cases of scientific frauds, some of the most significant challenges may be to detect the origin of an image or the photographing camera, detect forged images or hidden messages in images from retrieved digital evidence. There has been much interest in developing camera fingerprints for the forensic task of digital camera identification; that is, to be able to tie an image to it\u27s photographing camera with high certainty or good assurance metrics, specially when the camera is not present in the crime scene. Inspired by the existing approaches of camera fingerprint forensics, this paper explores a novel approach for camera identification, based on PRNU noise fingerprint, using Artificial Neural Network (ANN) algorithm. While statistical algorithms produce probabilistic inferences based on statistical problem data, artificial neural network algorithm learns features about the problem from training data. Based on correctness of feature representations and complex mathematical processing on the training data, the neural network is able to learn or approximate any non-linear distribution very easily. As it trains on different examples, it\u27s generalization performance on new inputs improves. In currently proposed work, first the reference fingerprint and test fingerprint are estimated based on a simple kernel based processing algorithm for PRNU coefficient estimation. Then an artificial neural network is set up in C programming language for PRNU pattern recognition based on the estimated feature values from the reference pattern data. The network is presented with training inputs and desired outputs, and based on formulated assumptions and hypothesis described in later sections, the expectation is that the ANN will be able to recognize PRNU fingerprint in images taken by the same camera whose fingerprint the ANN got trained on. A low Mean Square Error (MSE) during ANN training and testing is an indication that the ANN could report with high confidence, a match between the camera fingerprint pattern and the pattern in test image. Multilayer Perceptron (MLP) ANN with single hidden layer is proved to be a universal non-linear function approximator and can be applied to solve any complex non-linear problem. Current approach uses back propagation MLP ANN algorithm for fingerprint detection or camera identification

Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction

In this paper we develop a novel neural network model for predicting implied volatility surface. Prior financial domain knowledge is taken into account. A new activation function that incorporates volatility smile is proposed, which is used for the hidden nodes that process the underlying asset price. In addition, financial conditions, such as the absence of arbitrage, the boundaries and the asymptotic slope, are embedded into the loss function. This is one of the very first studies which discuss a methodological framework that incorporates prior financial domain knowledge into neural network architecture design and model training. The proposed model outperforms the benchmarked models with the option data on the S&P 500 index over 20 years. More importantly, the domain knowledge is satisfied empirically, showing the model is consistent with the existing financial theories and conditions related to implied volatility surface.Comment: 8 pages, SIGKDD 202

Contributions to the study of Austism Spectrum Brain conectivity

164 p.Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this Thesis we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines