Many practical machine learning tasks can be framed as Structured prediction problems, where several output variables are predicted and considered interdependent. Recent theoretical advances in structured prediction have focused on obtaining fast rates convergence guarantees, especially in the Implicit Loss Embedding (ILE) framework. PAC-Bayes has gained interest recently for its capacity of producing tight risk bounds for predictor distributions. This work proposes a novel PAC-Bayes perspective on the ILE Structured prediction framework. We present two generalization bounds, on the risk and excess risk, which yield insights into the behavior of ILE predictors. Two learning algorithms are derived from these bounds.

One way to understand how the brain adapts to its environment is to view it as a type of generative pattern-creation model [20], one that is engaged in a never-ending process of self-correction, often without external teaching signals (or labels) [53]. Under this perspective, the brain is continuously making predictions about elements of its environment, a process that allows it to infer useful representations of the sensory data it receives [56] as well as to synthesize novel patterns, which could serve as the potential basis for long-term planning and imagination itself [12]. From the theoretical viewpoint of predictive processing, the brain could be likened to a hierarchical model whose levels are implemented by neurons (or clusters of neurons). If levels are likened to regions of the brain, the neurons at one level (region) attempt to predict the state of neurons at another level (region) and adjust/correct their local model synaptic parameters based on how different their predictions were from the observed signal. Furthermore, these neurons utilize various mechanisms to laterally stimulate/suppress each other [40] to facilitate contextual processing (such as grouping/segmenting visual components of objects in a scene).

Albeit worryingly underrated in the recent literature on machine learning in general (and, on deep learning in particular), multivariate density estimation is a fundamental task in many applications, at least implicitly, and still an open issue. With a few exceptions, deep neural networks (DNNs) have seldom been applied to density estimation, mostly due to the unsupervised nature of the estimation task, and (especially) due to the need for constrained training algorithms that ended up realizing proper probabilistic models that satisfy Kolmogorov's axioms. Moreover, in spite of the well-known improvement in terms of modeling capabilities yielded by mixture models over plain single-density statistical estimators, no proper mixtures of multivariate DNN-based component densities have been investigated so far. The paper fills this gap by extending our previous work on Neural Mixture Densities (NMMs) to multivariate DNN mixtures. A maximum-likelihood (ML) algorithm for estimating Deep NMMs (DNMMs) is handed out, which satisfies numerically a combination of hard and soft constraints aimed at ensuring satisfaction of Kolmogorov's axioms. The class of probability density functions that can be modeled to any degree of precision via DNMMs is formally defined. A procedure for the automatic selection of the DNMM architecture, as well as of the hyperparameters for its ML training algorithm, is presented (exploiting the probabilistic nature of the DNMM). Experimental results on univariate and multivariate data are reported on, corroborating the effectiveness of the approach and its superiority to the most popular statistical estimation techniques.

"Computers are able to see, hear and learn. The biggest advancement that software technology has achieved in the last decade has to be the concept of artificial intelligence. Today, we encounter the perks of artificial intelligence at almost every step in our day-to-day life and also, in professional scenario. As per research, $28.5 billion has been already allocated to artificial intelligence worldwide during the first quarter of 2019. The monetary and human investment in artificial intelligence proves that it forms a core part of future technologies.

Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing tasks and other aspects derived from the published studies. Such multi-source information fusion plays a fundamental role in mining of people's social opinions from social media platforms. These can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. Future research directions are presented, whereas further research and development has the potential of leaving a wider academic and societal impact.

A key impediment to the use of AI is the lacking of transparency, especially in safety/security critical applications. The black-box nature of AI systems prevents humans from direct explanations on how the AI makes predictions, which stimulated Explainable AI (XAI) -- a research field that aims at improving the trust and transparency of AI systems. In this paper, we introduce a novel XAI technique, BayLIME, which is a Bayesian modification of the widely used XAI approach LIME. BayLIME exploits prior knowledge to improve the consistency in repeated explanations of a single prediction and also the robustness to kernel settings. Both theoretical analysis and extensive experiments are conducted to support our conclusions.

We introduce the Graph Mixture Density Network, a new family of machine learning models that can fit multimodal output distributions conditioned on arbitrary input graphs. By combining ideas from mixture models and graph representation learning, we address a broad class of challenging regression problems that rely on structured data. Our main contribution is the design and evaluation of our method on large stochastic epidemic simulations conditioned on random graphs. We show that there is a significant improvement in the likelihood of an epidemic outcome when taking into account both multimodality and structure. In addition, we investigate how to \textit{implicitly} retain structural information in node representations by computing the distance between distributions of adjacent nodes, and the technique is tested on two structure reconstruction tasks with very good accuracy. Graph Mixture Density Networks open appealing research opportunities in the study of structure-dependent phenomena that exhibit non-trivial conditional output distributions.

A new machine learning approach offers important insights into catalysis, a fundamental process that makes it possible to reduce the emission of toxic exhaust gases or produce essential materials like fabric. In a report published in Nature Communications, Hongliang Xin, associate professor of chemical engineering at Virginia Tech, and his team of researchers developed a Bayesian learning model of chemisorption, or Bayeschem for short, aiming to use artificial intelligence to unlock the nature of chemical bonding at catalyst surfaces. "It all comes down to how catalysts bind with molecules," said Xin. "The interaction has to be strong enough to break some chemical bonds at reasonably low temperatures, but not too strong that catalysts would be poisoned by reaction intermediates. This rule is known as the Sabatier principle in catalysis." Understanding how catalysts interact with different intermediates and determining how to control their bond strengths so that they are within that'goldilocks zone' is the key to designing efficient catalytic processes, Xin said. The research provides a tool for that purpose.

Artificial intelligence (AI) provides a wide range of current society applications, including predicting, classifying, and solving both social and scientific problems. As one of the oldest and most traditional engineering disciplines, civil engineering covers various aspects of the built environment, from design and construction to maintenance. Civil engineering offers ample practical scope for applications of AI. In turn, AI can improve human life quality and originate novel approaches to solving engineering problems. AI methods and techniques, including neural networks, evolutionary computation, fuzzy logic systems, and deep learning, have rapidly evolved over the past few years.

We consider the problem of learning the underlying graph of a sparse Ising model with $p$ nodes from $n$ i.i.d. samples. The most recent and best performing approaches combine an empirical loss (the logistic regression loss or the interaction screening loss) with a regularizer (an L1 penalty or an L1 constraint). This results in a convex problem that can be solved separately for each node of the graph. In this work, we leverage the cardinality constraint L0 norm, which is known to properly induce sparsity, and further combine it with an L2 norm to better model the non-zero coefficients. We show that our proposed estimators achieve an improved sample complexity, both (a) theoretically -- by reaching new state-of-the-art upper bounds for recovery guarantees -- and (b) empirically -- by showing sharper phase transitions between poor and full recovery for graph topologies studied in the literature -- when compared to their L1-based counterparts.