Heterogeneous information network (HIN) embedding has gained increasing interests recently. However, the current way of random-walk based HIN embedding methods have paid few attention to the higher-order Markov chain nature of meta-path guided random walks, especially to the stationarity issue. In this paper, we systematically formalize the meta-path guided random walk as a higher-order Markov chain process, and present a heterogeneous personalized spacey random walk to efficiently and effectively attain the expected stationary distribution among nodes. Then we propose a generalized scalable framework to leverage the heterogeneous personalized spacey random walk to learn embeddings for multiple types of nodes in an HIN guided by a meta-path, a meta-graph, and a meta-schema respectively. We conduct extensive experiments in several heterogeneous networks and demonstrate that our methods substantially outperform the existing state-of-the-art network embedding algorithms.

V arious factorization-based methods have been proposed to leverage second-order, or higher-order cross features for boosting the performance of predictive models. They generally enumerate all the cross features under a predefined maximum order, and then identify useful feature interactions through model training, which suffer from two drawbacks. First, they have to make a tradeoff between the expressiveness of higher-order cross features and the computational cost, resulting in suboptimal predictions. Second, enumerating all the cross features, including irrelevant ones, may introduce noisy feature combinations that degrade model performance. In this work, we propose the Adaptive Factorization Network (AFN), a new model that learns arbitrary-order cross features adaptively from data. The core of AFN is a logarithmic transformation layer to convert the power of each feature in a feature combination into the coefficient to be learned. The experimental results on four real datasets demonstrate the superior predictive performance of AFN against the start-of-the-arts. 1 Introduction Feature engineering is typically recognized as central to successful machine learning tasks, such as recommender systems (Lian et al. 2017), computational advertising (He et al. 2014) and search ranking (Lian and Xie 2016). Except for exploiting raw features, it is usually crucial to find effective transformations of raw features to boost the performance of predictive models. Cross features are a major type of feature transformations, where multiplication is performed over sparse raw features to form new features (Cheng et al. 2016). However, handcrafting useful cross features is inevitably expensive and time-consuming, and the results may not generalize to unseen feature interactions.

Hard optimisation problems such as Boolean Satisfiability typically have long solving times and can usually be solved by many algorithms, although the performance can vary widely in practice. Research has shown that no single algorithm outperforms all the others; thus, it is crucial to select the best algorithm for a given problem. Supervised machine learning models can accurately predict which solver is best for a given problem, but they require first to run every solver in the portfolio for all examples available to create labelled data. As this approach cannot scale, we developed an active learning framework that addresses this problem by constructing an optimal training set, so that the learner can achieve higher or equal performances with less training data. Our work proves that active learning is beneficial for algorithm selection techniques and provides practical guidance to incorporate into existing systems.

Maximum entropy deep reinforcement learning (RL) methods have been demonstrated on a range of challenging continuous tasks. However, existing methods either suffer from severe instability when training on large off-policy data or cannot scale to tasks with very high state and action dimensionality such as 3D humanoid locomotion. Besides, the optimality of desired Boltzmann policy set for non-optimal soft value function is not persuasive enough. In this paper, we first derive soft policy gradient based on entropy regularized expected reward objective for RL with continuous actions. Then, we present an off-policy actor-critic, model-free maximum entropy deep RL algorithm called deep soft policy gradient (DSPG) by combining soft policy gradient with soft Bellman equation. To ensure stable learning while eliminating the need of two separate critics for soft value functions, we leverage double sampling approach to making the soft Bellman equation tractable. The experimental results demonstrate that our method outperforms in performance over off-policy prior methods.

It also discussed a concept of overfitting, which essentially means, the model matches the training data exactly so losses its ability to "predict" or learning for things it hasn't seen.

I have been frequently asked about the tools for the Machine Learnign projects There are lot of them on the market so in my newest post you will find my view on them. I would like to start my first Machine Learning project. But I do not have tools. What are the tools I could use? I will give you some hints and advices based on the toolbox I use.

In this video, I use neural networks to predict whether a tumor in a woman's breasts is'malignant' or'benign'. This is a prime example of a classification problem. I implement both logistic and softmax regression models with the Keras API and the Tensorflow backend.

The financial services industry has often been at the forefront of using new technology to solve business problems. It's no surprise that many firms in this sector are embracing machine learning, especially now that increased compute power, network connectivity, and cloud infrastructure are cheaper and more accessible. This post will detail five important machine learning use cases that are currently providing value within financial services organizations. The cost of financial fraud for a financial services company jumped 9 percent between 2017 and 2018, resulting in a cost of $2.92 for every dollar of fraud. We have previously discussed machine learning applications in fraud detection in detail, but it's worth mentioning some additional reasons why this is one of the most important applications for machine learning in this sector.

In this paper, we study the problem of precision matrix estimation when the dataset contains sensitive information. In the differential privacy framework, we develop a differentially private ridge estimator by perturbing the sample covariance matrix. Then we develop a differentially private graphical lasso estimator by using the alternating direction method of multipliers (ADMM) algorithm. The theoretical results and empirical results that show the utility of the proposed methods are also provided. Keywords differential privacy, graphical model, ADMM algorithm 1 Introduction Precision matrix plays a fundamental role in many statistical inference problems. For example, in discriminant analysis, the precision matrix needs to be estimated to compute the classification rules[1]. In graphical models, the structure exploration of gaussian graphical model is equivalent to recover the support of the precision matrix[2]. Moreover, the precision matrix is useful for a wide range of applications including portfolio optimization, genomics and single processing, among many others. Therefore, it is of great importance to estimate the precision matrix.

In this paper, we aimed at reviewing present literature on employing nonlinear analysis in combination with machine learning methods, in depression detection or prediction task. We are focusing on an affordable data-driven approach, applicable for everyday clinical practice, and in particular, those based on electroencephalographic (EEG) recordings. Among those studies utilizing EEG, we are discussing a group of applications used for detecting the depression based on the resting state EEG (detection studies) and interventional studies (using stimulus in their protocols or aiming to predict the outcome of therapy). We conclude with a discussion and review of guidelines to improve the reliability of developed models that could serve the improvement of diagnostic and more accurate treatment of depression.