The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of the topic and the multitude of ingredients involved therein, besides the complexity of turning theory into practical implementations, limit the use of the Bayesian learning paradigm, preventing its widespread adoption across different fields and applications. This self-contained survey engages and introduces readers to the principles and algorithms of Bayesian Learning for Neural Networks. It provides an introduction to the topic from an accessible, practical-algorithmic perspective. Upon providing a general introduction to Bayesian Neural Networks, we discuss and present both standard and recent approaches for Bayesian inference, with an emphasis on solutions relying on Variational Inference and the use of Natural gradients. We also discuss the use of manifold optimization as a state-of-the-art approach to Bayesian learning. We examine the characteristic properties of all the discussed methods, and provide pseudo-codes for their implementation, paying attention to practical aspects, such as the computation of the gradients.

Deep visual models have widespread applications in high-stake domains. Hence, their black-box nature is currently attracting a large interest of the research community. We present the first survey in Explainable AI that focuses on the methods and metrics for interpreting deep visual models. Covering the landmark contributions along the state-of-the-art, we not only provide a taxonomic organization of the existing techniques, but also excavate a range of evaluation metrics and collate them as measures of different properties of model explanations. Along the insightful discussion on the current trends, we also discuss the challenges and future avenues for this research direction.

Archive collections are nowadays mostly available through search engines interfaces, which allow a user to retrieve documents by issuing queries. The study of these collections may be, however, impaired by some aspects of search engines, such as the overwhelming number of documents returned or the lack of contextual knowledge provided. New methods that could work independently or in combination with search engines are then required to access these collections. In this position paper, we propose to extend TimeLine Summarization (TLS) methods on archive collections to assist in their studies. We provide an overview of existing TLS methods and we describe a conceptual framework for an Archive TimeLine Summarization (ATLS) system, which aims to generate informative, readable and interpretable timelines.

The recent rapid development of deep learning has laid a milestone in visual anomaly detection (VAD). In this paper ... provide a comprehensive review of deep learning-based visual anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets.

Working in the UX industry means living in a constant state of flux. Every day seems to bring new technologies, skills, business challenges, and user expectations to absorb. That's why every December, I eagerly await the release of UX Collective's State of UX report, in which authors Fabricio Teixeira and Caio Braga (plus collaborators) synthesize thousands of articles in order to put the past year into perspective and highlight emerging themes for the road ahead. The underlying theme for this year's report was anxiety. Massive layoffs at tech stalwarts like Facebook, Google, and Amazon, along with headlines about an economic slowdown, have some designers thinking about how to recession-proof their jobs. With headcounts shrinking, design teams are expected to do more with less, and former managers are returning to hands-on work.

ChatGPT is an advanced artificial intelligence program that promises to revolutionize the legal profession. Developed by OpenAI, this cutting-edge technology utilizes natural language processing models to generate human-like text, solve complex coding issues and provide valuable insights into large data sets. The potential applications of this technology are limitless and have the potential to greatly improve the efficiency and accuracy of legal work, while also creating new problems that require thoughtful solutions.

Deep learning is a topic of considerable current interest. The availability of massive data collections and powerful software resources has led to an impressive amount of results in many application areas that reveal essential but hidden properties of the observations. System identification learns mathematical descriptions of dynamic systems from input-output data and can thus benefit from the advances of deep neural networks to enrich the possible range of models to choose from. For this reason, we provide a survey of deep learning from a system identification perspective. We cover a wide spectrum of topics to enable researchers to understand the methods, providing rigorous practical and theoretical insights into the benefits and challenges of using them. The main aim of the identified model is to predict new data from previous observations. This can be achieved with different deep learning based modelling techniques and we discuss architectures commonly adopted in the literature, like feedforward, convolutional, and recurrent networks. Their parameters have to be estimated from past data trying to optimize the prediction performance. For this purpose, we discuss a specific set of first-order optimization tools that is emerged as efficient. The survey then draws connections to the well-studied area of kernel-based methods. They control the data fit by regularization terms that penalize models not in line with prior assumptions. We illustrate how to cast them in deep architectures to obtain deep kernel-based methods. The success of deep learning also resulted in surprising empirical observations, like the counter-intuitive behaviour of models with many parameters. We discuss the role of overparameterized models, including their connection to kernels, as well as implicit regularization mechanisms which affect generalization, specifically the interesting phenomena of benign overfitting ...

Anomaly detection is critical for finding suspicious behavior in innumerable systems. We need to detect anomalies in real-time, i.e. determine if an incoming entity is anomalous or not, as soon as we receive it, to minimize the effects of malicious activities and start recovery as soon as possible. Therefore, online algorithms that can detect anomalies in a streaming manner are essential. We first propose MIDAS which uses a count-min sketch to detect anomalous edges in dynamic graphs in an online manner, using constant time and memory. We then propose two variants, MIDAS-R which incorporates temporal and spatial relations, and MIDAS-F which aims to filter away anomalous edges to prevent them from negatively affecting the internal data structures. We then extend the count-min sketch to a Higher-Order sketch to capture complex relations in graph data, and to reduce detecting suspicious dense subgraph problem to finding a dense submatrix in constant time. Using this sketch, we propose four streaming methods to detect edge and subgraph anomalies. Next, we broaden the graph setting to multi-aspect data. We propose MStream which detects explainable anomalies in multi-aspect data streams. We further propose MStream-PCA, MStream-IB, and MStream-AE to incorporate correlation between features. Finally, we consider multi-dimensional data streams with concept drift and propose MemStream. MemStream leverages the power of a denoising autoencoder to learn representations and a memory module to learn the dynamically changing trend in data without the need for labels. We prove a theoretical bound on the size of memory for effective drift handling. In addition, we allow quick retraining when the arriving stream becomes sufficiently different from the training data. Furthermore, MemStream makes use of two architecture design choices to be robust to memory poisoning.

There is an increasing need to develop artificial intelligence systems that assist groups of humans working on coordinated tasks. These systems must recognize and understand the plans and relationships between actions for a team of humans working toward a common objective. This article reviews the literature on team plan recognition and surveys the most recent logic-based approaches for implementing it. First, we provide some background knowledge, including a general definition of plan recognition in a team setting and a discussion of implementation challenges. Next, we explain our reasoning for focusing on logic-based methods. Finally, we survey recent approaches from two primary classes of logic-based methods (plan library-based and domain theory-based). We aim to bring more attention to this sparse but vital topic and inspire new directions for implementing team plan recognition.

Automated planning is a prominent area of Artificial Intelligence, and an important component for intelligent autonomous agents. A cornerstone of domain-independent planning is the separation between planning logic, i.e. the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reason upon a given problem to synthesise a solution plan. Such a separation enables the use of reformulation techniques, which transform how a model is represented in order to improve the efficiency of plan generation. Over the past decades, significant research effort has been devoted to the design of reformulation techniques. In this paper, we present a systematic review of the large body of work on reformulation techniques for classical planning, aiming to provide a holistic view of the field and to foster future research in the area. As a tangible outcome, we provide a qualitative comparison of the existing classes of techniques, that can help researchers gain an overview of their strengths and weaknesses.