--In recent years, deep learning methods have outperformed other methods in image recognition. This has fostered imagination of potential application of deep learning technology including safety relevant applications like the interpretation of medical images or autonomous driving. The passage from assistance of a human decision maker to ever more automated systems however increases the need to properly handle the failure modes of deep learning modules. In this contribution, we review a set of techniques for the self-monitoring of machine-learning algorithms based on uncertainty quantification. In particular, we apply this to the task of semantic segmentation, where the machine learning algorithm decomposes an image according to semantic categories. We discuss false positive and false negative error modes at instance-level and review techniques for the detection of such errors that have been recently proposed by the authors. We also give an outlook on future research directions. The stunning success of deep learning technology, convolu-tional neural networks (CNN) in particular [1]-[3], has led to a rush towards technology development for new applications that ten years ago would have been considered unrealistic.

Here we present a holistic approach for data exploration on dense knowledge graphs as a novel approach with a proof-of-concept in biomedical research. Knowledge graphs are increasingly becoming a vital factor in knowledge mining and discovery as they connect data using technologies from the semantic web. In this paper we extend a basic knowledge graph extracted from biomedical literature by context data like named entities and relations obtained by text mining and other linked data sources like ontologies and databases. We will present an overview about this novel network. The aim of this work was to extend this current knowledge with approaches from graph theory. This method will build the foundation for quality control, validation of hypothesis, detection of missing data and time series analysis of biomedical knowledge in general. In this context we tried to apply multiple-valued decision diagrams to these questions. In addition this knowledge representation of linked data can be used as FAIR approach to answer semantic questions. This paper sheds new lights on dense and very large knowledge graphs and the importance of a graph-theoretic understanding of these networks.

Multi-agent reinforcement learning (MARL) has long been a significant and everlasting research topic in both machine learning and control. With the recent development of (single-agent) deep RL, there is a resurgence of interests in developing new MARL algorithms, especially those that are backed by theoretical analysis. In this paper, we review some recent advances a sub-area of this topic: decentralized MARL with networked agents. Specifically, multiple agents perform sequential decision-making in a common environment, without the coordination of any central controller. Instead, the agents are allowed to exchange information with their neighbors over a communication network. Such a setting finds broad applications in the control and operation of robots, unmanned vehicles, mobile sensor networks, and smart grid. This review is built upon several our research endeavors in this direction, together with some progresses made by other researchers along the line. We hope this review to inspire the devotion of more research efforts to this exciting yet challenging area.

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. This includes conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields. After giving a basic notion of machine learning methods and principles, examples are described of how statistical physics is used to understand methods in ML. This review then describes applications of ML methods in particle physics and cosmology, quantum many-body physics, quantum computing, and chemical and material physics. Research and development into novel computing architectures aimed at accelerating ML are also highlighted.

Our technical report provides an overview of the relevant parts of an ML lifecycle--selecting the right problem, the right data, and the right math and summarizing the model output for consumption--as well as questions that relate to those areas of focus. As the federally funded research and development center (FFRDC) known for AI engineering, and with its long experience in cybersecurity, the SEI has the expertise to advise you--the decision makers adopting these tools--on evaluating the adequacy of ML tools applied to cybersecurity. To that end, we structured the report around the questions you should ask about ML tools. We chose this framing, rather than proposing a detailed guide of how to build an ML system in cybersecurity, because we want to enable you to learn what a good tool looks like. When decision makers have difficulty identifying a good tool, the market will usually stop providing them.

The Department of Trade and Industry (DTI) sets the wheels in motion for the crafting of the country's artificial-intelligence sector road map as testament to the Philippine potential as an AI powerhouse in the Asean region. Trade Undersecretary Rafaelita M. Aldaba of the Competitiveness and Innovation Group led the formal signing of an agreement with distinguished data scientists, Dr. Christopher P. Monterola and Dr. Erika Fille T. Legara, for the formulation of an AI road map early this month. With the advent of the Fourth Industrial Revolution (4IR) where technology becomes more enmeshed with everyday life, AI advancement is seen as one of the key factors to help keep our country competitive. AI's importance is underscored as it is emerging to be a potential bright spot for our country with wide opportunities for growth for our competent work force. "The formulation of the AI road map is very important and timely. This effort provides the impetus that will move the country forward to keep up with the rapidly changing times," Aldaba emphasized.

Ethnic group classification is a well-researched problem, which has been pursued mainly during the past two decades via traditional approaches of image processing and machine learning. In this paper, we propose a method of classifying an image face into an ethnic group by applying transfer learning from a previously trained classification network for large-scale data recognition. Our proposed method yields state-of- the-art success rates of 99.02%, 99.76%, 99.2%, and 96.7%, respectively, for the four ethnic groups: African, Asian, Caucasian, and Indian. 1 Introduction Ethnic classification from facial images has been studied for the past two decades with the purpose of understanding how humans perceive and determine an ethnic group from a given image. The motivation stems, for example, from the fact that (gender and) ethnicity play an important role in face-related applications, such as advertising, social insensitive-based systems, etc. Furthermore, while facial features are subject to change (due to aging, for example), ethnicity is of interest due to its invariance over time. Recent works on demographic classification are divided conceptually into appearancebased methods (using, e.g., eigenface methods, fisherface methods, etc.) and geometry-based methods (relying, e.g., on geometric parameters, such as the distance between the eyes, face width and length, nose thickness, etc.). One of the main challenges of automatic demographic classification is to avoid any "noise", such as illumination, background distortion, and a subject's pose. In this paper, we introduce a deep learning-based method, that achieves state-of-the-art results for facial image representations and classification for the four ethnic groups: African, Asian, Caucasian, and Indian. 2 Related Work 2.1 Traditional ML-Based Techniques During the past two decades, there has been enormous progress on the topic of ethnic group classification, using various classical Machine Learning methods.

The rapid evolution of technology is both exciting and daunting, as businesses strive to identify and back the right technologies that will drive positive change within their organisation. Understanding which to explore and test and which to press on and implement can be challenging, particularly when new technologies are frequently announced with much fanfare far before benefits are truly realised. To help get an expert perspective on the matter, Savannah Group recently hosted an event for a selection of CIOs, CTOs and CDOs and invited Dr Jai Menon, Chief Scientist at Cloudistics to lead a discussion on the major technological changes driving change in organisations at the moment. Jai's distinguished career has seen him serve as CTO of some of the largest systems businesses in the world including IBM and Dell. He is recognised as an IBM Fellow and was a pioneer behind the creation of RAID technology – now a $20 billion industry.

Normalizing flows provide a general mechanism for defining expressive probability distributions, only requiring the specification of a (usually simple) base distribution and a series of bijective transformations. There has been much recent work on normalizing flows, ranging from improving their expressive power to expanding their application. We believe the field has now matured and is in need of a unified perspective. In this review, we attempt to provide such a perspective by describing flows through the lens of probabilistic modeling and inference. We place special emphasis on the fundamental principles of flow design, and discuss foundational topics such as expressive power and computational trade-offs. We also broaden the conceptual framing of flows by relating them to more general probability transformations. Lastly, we summarize the use of flows for tasks such as generative modeling, approximate inference, and supervised learning.

We introduce a Gaussian process-based model for handling of non-stationarity. The warping is achieved non-parametrically, through imposing a prior on the relative change of distance between subsequent observation inputs. The model allows the use of general gradient optimization algorithms for training and incurs only a small computational overhead on training and prediction. The model finds its applications in forecasting in non-stationary time series with either gradually varying volatility, presence of change points, or a combination thereof. We evaluate the model on synthetic and real-world time series data comparing against both baseline and known state-of-the-art approaches and show that the model exhibits state-of-the-art forecasting performance at a lower implementation and computation cost.