Anastasia Borovykh December 19, 2019 Abstract We present a novel methodology based on a Taylor expansion of the network output for obtaining analytical expressions for the expected value of the network weights and output under stochastic training. Using these analytical expressions the effects of the hyperparameters and the noise variance of the optimization algorithm on the performance of the deep neural network are studied. In the early phases of training with a small noise coefficient, the output is equivalent to a linear model. In this case the network can generalize better due to the noise preventing the output from fully converging on the train data, however the noise does not result in any explicit regularization. In the later training stages, when higher order approximations are required, the impact of the noise becomes more significant, i.e. in a model which is nonlinear in the weights noise can regularize the output function resulting in better generalization as witnessed by its influence on the weight Hessian, a commonly used metric for generalization capabilities. Keywords: deep learning; Taylor expansion; stochastic gradient descent; regularization; generalization 1 Introduction With the large number of applications which are nowadays in some way using deep learning, it is of significant value to gain insight into the output evolution of a deep neural network and the effects that the model architecture and optimization algorithm have on it. A deep neural network is a complex model due to the nonlinear dependencies and the large number of parameters in the model. Understanding the network output and its generalization capabilities, i.e. how well a model optimized on train data will be able to perform on unseen test data, is thus a complex task. One way of gaining insight into the network is by studying it in a large-parameter limit, a setting in which its dynamics becomes analytically tractable. Such limits have been considered in e.g. The generalization capabilities and the definition of various quantities that measure these have been studied extensively. Previous work has shown that the norm [3], [27], [19], the width of a minimum in weight space [11], [34], the input sensitivity [28] and a model's compressibility [2] can be related (either theoretically or in practice) to the model's complexity and thus its ability to perform well on unseen data. Furthermore, it has been noted that the generalization capabilities can be influenced by the optimization algorithm used to train the model, e.g. it can be used to bias the model into configurations that are more robust to noise and have lower model complexity, see e.g. Furthermore, it has been observed that certain parameters of stochastic gradient descent (SGD) can be used to control the generalization error and the data fit, see e.g.

The ubiquitous availability of wearable sensors is responsible for driving the Internet-of-Things but is also making an impact on sport sciences and precision medicine. While human activity recognition from smartphone data or other types of inertial measurement units (IMU) has evolved to one of the most prominent daily life examples of machine learning, the underlying process of time-series feature engineering still seems to be time-consuming. This lengthy process inhibits the development of IMU-based machine learning applications in sport science and precision medicine. This contribution discusses a feature engineering workflow, which automates the extraction of time-series feature on based on the FRESH algorithm (FeatuRe Extraction based on Scalable Hypothesis tests) to identify statistically significant features from synchronized IMU sensors (IMeasureU Ltd, NZ). The feature engineering workflow has five main steps: time-series engineering, automated time-series feature extraction, optimized feature extraction, fitting of a specialized classifier, and deployment of optimized machine learning pipeline. The workflow is discussed for the case of a user-specific running-walking classification, and the generalization to a multi-user multi-activity classification is demonstrated.

A novel approach of applying deep reinforcement learning to an RF pulse design is introduced. This method, which is referred to as $DeepRF_{SLR}$, is designed to minimize the peak amplitude or, equivalently, minimize the pulse duration of a multiband refocusing pulse generated by the Shinar Le-Roux (SLR) algorithm. In the method, the root pattern of SLR polynomial, which determines the RF pulse shape, is optimized by iterative applications of deep reinforcement learning and greedy tree search. When tested for the designs of the multiband factors of three and seven RFs, $DeepRF_{SLR}$ demonstrated improved performance compared to conventional methods, generating shorter duration RF pulses in shorter computational time. In the experiments, the RF pulse from $DeepRF_{SLR}$ produced a slice profile similar to the minimum-phase SLR RF pulse and the profiles matched to that of the computer simulation. Our approach suggests a new way of designing an RF by applying a machine learning algorithm, demonstrating a machine-designed MRI sequence.

In this work, we propose a novel OeSNN-UAD (Online evolving Spiking Neural Networks for Unsupervised Anomaly Detection) approach for online anomaly detection in univariate time series data. Our approach is based on evolving Spiking Neural Networks (eSNN). Its distinctive feature is that the proposed eSNN architecture learns in the process of classifying input values to be anomalous or not. In fact, we offer an unsupervised learning method for eSNN, in which classification is carried out without earlier pre-training of the network with data with labeled anomalies. Unlike in a typical eSNN architecture, neurons in the output repository of our architecture are not divided into known a priori decision classes. Each output neuron is assigned its own output value, which is modified in the course of learning and classifying the incoming input values of time series data. To better adapt to the changing characteristic of the input data and to make their classification efficient, the number of output neurons is limited: the older neurons are replaced with new neurons whose output values and synapses' weights are adjusted according to the current input values of the time series. The proposed OeSNN-UAD approach was experimentally compared to the state-of-the-art unsupervised methods and algorithms for anomaly detection in stream data. The experiments were carried out on Numenta Anomaly Benchmark and Yahoo Anomaly Datasets. According to the results of these experiments, our approach significantly outperforms other solutions provided in the literature in the case of Numenta Anomaly Benchmark. Also in the case of real data files category of Yahoo Anomaly Benchmark, OeSNN-UAD outperforms other selected algorithms, whereas in the case of Yahoo Anomaly Benchmark synthetic data files, it provides competitive results to the results recently reported in the literature.

--Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and integration. When generating EA results, current embedding-based solutions treat entities independently and fail to take into account the interdependence between entities. In addition, most of embedding-based EA methods either fuse different features on representation-level and generate unified entity embedding for alignment, which potentially causes information loss, or aggregate features on outcome-level with hand-tuned weights, which is not practical with increasing numbers of features. T o tackle these deficiencies, we propose a collective embedding-based EA framework with adaptive feature fusion mechanism. We first employ three representative features, i.e., structural, semantic and string signals, for capturing different aspects of the similarity between entities in heterogeneous KGs. These features are then integrated at outcome-level, with dynamically assigned weights generated by our carefully devised adaptive feature fusion strategy. Eventually, in order to make collective EA decisions, we formulate EA as the classical stable matching problem between entities to be aligned, with preference lists constructed using fused feature matrix. It is further effectively solved by deferred acceptance algorithm. Our proposal is evaluated on both cross-lingual and monolingual EA benchmarks against state-of- the-art solutions, and the empirical results verify its effectiveness and superiority. We also perform ablation study to gain insights into framework modules. I NTRODUCTION Knowledge graph (KG) is playing an increasingly more important role in intelligent information services, e.g., information retrieval [27], automatic question answering [14] and recommendation systems [3]. Despite that a large number of KGs have been constructed over recent years, none of them can reach full coverage . These KGs, however, usually contain complementary contents, making it compelling to study the integration of heterogeneous KGs. To incorporate the knowledge from target KGs into the source KG, an indispensable step would be entity alignment (EA). EA aims to discover entities that have the same meaning but locate in different KGs.

"If you want to command a multiyear, seven-figure salary, you used to have only four career options: chief executive officer, banker, celebrity entertainer, or pro athlete. Imagine a glass with balls. This glass is a field of Computer science knowledge, and balls are various fields: back end, front end development, embedded. One of these balls is artificial intelligence, and it is special because there are other balls inside: machine learning, natural language processing, and a whole slew of other things. Each of these units individually -- a powerful force and new opportunities to change any sphere.

As one of the world's busiest airports, (ranked No. 3 in 2018 according to Airports Council International's world traffic report), Dubai International Airport is also a leader in using artificial intelligence (AI). In fact, the United Arab Emirates (UAE) leads the Arab world with its adoption of artificial intelligence in other sectors and areas of life and has a government that prioritizes artificial intelligence including an AI strategy and Ministry of Artificial Intelligence with a mandate to invest in technologies and AI tools. The Emirates Ministry of the Interior said that by 2020, immigration officers would no longer be needed in the UAE. They will be replaced by artificial intelligence. The plan is to have people just walk through an AI-powered security system to be scanned without taking off shoes or belts or emptying pockets.

If recent television shows are anything to go by, we're a little concerned about the consequences of technological development. Black Mirror projects the negative consequences of social media, while artificial intelligence turns rogue in The 100 and Better Than Us. The potential extinction of the human race is up for grabs in Travellers, and Altered Carbon frets over the separation of human consciousness from the body. And Humans and Westworld see trouble ahead for human-android relations. Narratives like these have a long lineage.

This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones. In the area of machine learning and data science, a tree-like model as that of decision tree is considered as one of the most popular classification techniques, which can be used to build a data-driven predictive model. The traditional decision tree model typically creates a number of leaf nodes as decision nodes that represent context-specific rigid decisions, and consequently may cause overfitting problem in behavior modeling. However, in many practical scenarios within the context-aware environment, the generalized outcomes could play an important role to effectively capture user behavior. In this paper, we propose a behavioral decision tree, "BehavDT" context-aware model that takes into account user behavior-oriented generalization according to individual preference level. The BehavDT model outputs not only the generalized decisions but also the context-specific decisions in relevant exceptional cases. The effectiveness of our BehavDT model is studied by conducting experiments on individual user real smartphone datasets. Our experimental results show that the proposed BehavDT context-aware model is more effective when compared with the traditional machine learning approaches, in predicting user diverse behaviors considering multi-dimensional contexts.

The main goal of this thesis is to point out that the bias-variance tradeoff is not always true (e.g. in neural networks). We advocate for this lack of universality to be acknowledged in textbooks and taught in introductory courses that cover the tradeoff. We first review the history of the bias-variance tradeoff, its prevalence in textbooks, and some of the main claims made about the bias-variance tradeoff. Through extensive experiments and analysis, we show a lack of a bias-variance tradeoff in neural networks when increasing network width. Our findings seem to contradict the claims of the landmark work by Geman et al. (1992). Motivated by this contradiction, we revisit the experimental measurements in Geman et al. (1992). We discuss that there was never strong evidence for a tradeoff in neural networks when varying the number of parameters. We observe a similar phenomenon beyond supervised learning, with a set of deep reinforcement learning experiments. We argue that textbook and lecture revisions are in order to convey this nuanced modern understanding of the bias-variance tradeoff.