Cross-Domain Collaborative Filtering (CDCF) provides a way to alleviate data sparsity and cold-start problems present in recommendation systems by exploiting the knowledge from related domains. Existing CDCF models are either based on matrix factorization or deep neural networks. Either of the techniques in isolation may result in suboptimal performance for the prediction task. Also, most of the existing models face challenges particularly in handling diversity between domains and learning complex non-linear relationships that exist amongst entities (users/items) within and across domains. In this work, we propose an end-to-end neural network model -- NeuCDCF, to address these challenges in a cross-domain setting. More importantly, NeuCDCF follows a wide and deep framework and it learns the representations combinedly from both matrix factorization and deep neural networks. We perform experiments on four real-world datasets and demonstrate that our model performs better than state-of-the-art CDCF models.

Personalized Route Recommendation (PRR) aims to generate user-specific route suggestions in response to users' route queries. Early studies cast the PRR task as a pathfinding problem on graphs, and adopt adapted search algorithms by integrating heuristic strategies. Although these methods are effective to some extent, they require setting the cost functions with heuristics. In addition, it is difficult to utilize useful context information in the search procedure. To address these issues, we propose using neural networks to automatically learn the cost functions of a classic heuristic algorithm, namely A* algorithm, for the PRR task. Our model consists of two components. First, we employ attention-based Recurrent Neural Networks (RNN) to model the cost from the source to the candidate location by incorporating useful context information. Instead of learning a single cost value, the RNN component is able to learn a time-varying vectorized representation for the moving state of a user. Second, we propose to use a value network for estimating the cost from a candidate location to the destination. For capturing structural characteristics, the value network is built on top of improved graph attention networks by incorporating the moving state of a user and other context information. The two components are integrated in a principled way for deriving a more accurate cost of a candidate location. Extensive experiment results on three real-world datasets have shown the effectiveness and robustness of the proposed model.

A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions. Building such models from data often involves the application of some form of machine learning. Thus, there is an ever growing demand in work force with the necessary skill set to do so. This demand has given rise to a new research topic concerned with fitting machine learning models fully automatically - AutoML. This paper gives an overview of the state of the art in AutoML with a focus on practical applicability in a business context, and provides recent benchmark results on the most important AutoML algorithms.

Generative Adversarial Networks, or GANs for short, are a deep learning technique for training generative models. The study and application of GANs are only a few years old, yet the results achieved have been nothing short of remarkable. Because the field is so young, it can be challenging to know how to get started, what to focus on, and how to best use the available techniques. In this crash course, you will discover how you can get started and confidently develop deep learning Generative Adversarial Networks using Python in seven days. Note: This is a big and important post. You might want to bookmark it. How to Get Started With Generative Adversarial Networks (7-Day Mini-Course) Photo by Matthias Ripp, some rights reserved.

Update (July 18): We've sent beta invites to all existing customers of TabNine who signed up for the beta. Follow us on Twitter for more updates. TL;DR: TabNine is an autocompleter that helps you write code faster. We're adding a deep learning model which significantly improves suggestion quality. You can see videos below and you can sign up for it here.

In this tutorial, you will learn how to perform video classification using Keras, Python, and Deep Learning. This tutorial will serve as an introduction to the concept of working with deep learning in a temporal nature, paving the way for when we discuss Long Short-term Memory networks (LSTMs) and eventually human activity recognition. To learn how to perform video classification with Keras and Deep learning, just keep reading! Videos can be understood as a series of individual images; and therefore, many deep learning practitioners would be quick to treat video classification as performing image classification a total of N times, where N is the total number of frames in a video. Video classification is more than just simple image classification -- with video we can typically make the assumption that subsequent frames in a video are correlated with respect to their semantic contents.

Deepfakes have gained a lot of negative attention recently. Be it the hugely criticized DeepNude AI app which removes clothing from pictures of women or the FakeApp that swaps the faces of celebrities with porn stars in videos. Deep-learning algorithms are excellent at detecting matching patterns in images. This capability can be used to train neural nets to detect different types of cancer in a CT scan, identify diseases in MRIs, and spot abnormalities in an x-ray. While the idea of implementing deepfake AI for medical purposes sounds great, researchers don't have enough data to train a model -- simply because of privacy concerns.

Data scientists are expected to know a lot -- machine learning, computer science, statistics, mathematics, data visualization, communication, and deep learning. Within those areas there are dozens of languages, frameworks, and technologies data scientists could learn. How should data scientists who want to be in demand by employers spend their learning budget? I scoured job listing websites to find which skills are most in demand for data scientists. I looked at general data science skills and at specific languages and tools separately.

Prediction of toxicity levels of chemical compounds is an important issue in Quantitative Structure-Activity Relationship (QSAR) modeling. Although toxicity prediction has achieved significant progress in recent times through deep learning, prediction accuracy levels obtained by even very recent methods are not yet very high. We propose a multimodal deep learning method using multiple heterogeneous neural network types and data representations. We represent chemical compounds by strings, images, and numerical features. We train fully connected, convolutional, and recurrent neural networks and their ensembles. Each data representation or neural network type has its own strengths and weaknesses. Our motivation is to obtain a collective performance that could go beyond individual performance of each data representation or each neural network type. On a standard toxicity benchmark, our proposed method obtains significantly better accuracy levels than that by the state-of-the-art toxicity prediction methods.

Precise scientific analysis in collider-based particle physics is possible because of complex simulations that connect fundamental theories to observable quantities. The significant computational cost of these programs limits the scope, precision, and accuracy of Standard Model measurements and searches for new phenomena. We therefore introduce Deep neural networks using Classification for Tuning and Reweighting (DCTR), a neural network-based approach to reweight and fit simulations using the full phase space. DCTR can perform tasks that are currently not possible with existing methods, such as estimating non-perturbative fragmentation uncertainties. The core idea behind the new approach is to exploit powerful high-dimensional classifiers to reweight phase space as well as to identify the best parameters for describing data. Numerical examples from $e^+e^-\rightarrow\text{jets}$ demonstrate the fidelity of these methods for simulation parameters that have a big and broad impact on phase space as well as those that have a minimal and/or localized impact. The high fidelity of the full phase-space reweighting enables a new paradigm for simulations, parameter tuning, and model systematic uncertainties across particle physics and possibly beyond.