Video games have proved to be a very defying laboratory to study machine-learning techniques, such as Deep Reinforcement Learning (DRL) algorithms. This paper presents a new approach for a DRL-based agent trained through Deep Q-Network (DQN) technique to perform free kicks in FIFA 18 game. The main motivation for choosing this case study is the fact that, like in many situations of the real life, FIFA represents a stochastic environment. Coping with this task, the main contributions of the present paper consist on: inspired on the OpenAI Gym and on the OpenAI Universe platforms, implementing a new user-friendly interface (in terms of portability and use simplicity) to connect the learning module with the 3D FIFA's game environment; implementing a DRL-based agent for free kicks in FIFA that uses two distinct data representations retrieved from lower cost computational procedures. The results were validated through two evaluative parameters: score of well succeed kicks and training time.

Automatically identifying implicit discourse relations requires an in-depth semantic understanding of the text fragments involved in such relations. While early work investigated the usefulness of different classes of input features, current state-of-the-art models mostly rely on standard pre-trained word embeddings to model the arguments of a discourse relation. In this paper, we introduce a method to compute contextualized representations of words, leveraging information from the sentence dependency parse, to improve argument representation. The resulting token embeddings encode the structure of the sentence from a dependency point of view in their representations. Experimental results show that the proposed representations achieve state-of-the-art results when input to standard neural network architectures, surpassing complex models that use additional data and consider the interaction between arguments.

In this paper we adapt the semi-supervised deep learning architecture known as Convolutional Ladder Networks, from the domain of computer vision, and explore how well it works for a semi-supervised Named Entity Recognition and Classification task with legal data. The idea of exploring a semi-supervised technique is to asses the impact of large amounts of unsupervised data (cheap to obtain) in specific tasks that have little annotated data, in order to develop robust models that are less prone to overfitting. In order to achieve this, first we must check the impact on a task that is easier to measure. We are presenting some preliminary results, however, the experiments carried out show some very interesting insights that foster further research in the topic.

Recent successes in deep learning have led to explorations of what makes these techniques so powerful. One goal of such studies is to determine whether such properties can be transferred to alternative learning methods and yield similar benefits. Since the generalization power of any learning algorithm depends upon the inductive bias(es) of that algorithm, we hypothesize that utilizing a bias incorporated by CNNs and other deep methods--spatial locality--can benefit other learning methods as well. We test this hypothesis by incorporating spatial structure when constructing random forests. Our experiments demonstrate that incorporating a spatial locality bias improves the performance of random forests on several image classification tasks.

Performing machine learning on structured data is complicated by the fact that such data does not have vectorial form. Therefore, multiple approaches have emerged to construct vectorial representations of structured data, from kernel and distance approaches to recurrent, recursive, and convolutional neural networks. Recent years have seen heightened attention in this demanding field of research and several new approaches have emerged, such as metric learning on structured data, graph convolutional neural networks, and recurrent decoder networks for structured data. In this contribution, we provide an high-level overview of the state-of-the-art in representation learning and embeddings for structured data across a wide range of machine learning fields.

From all different proposed We present DeepCABAC, a novel contextadaptive methods, sparsification followed by weight quantization and binary arithmetic coder for compressing entropy coding arguably belong to the set of most popular deep neural networks. It quantizes each weight parameter approaches, since very high compression ratios can be by minimizing a weighted rate-distortion achieved under such paradigm (Han et al., 2015a; Louizos function, which implicitly takes the impact of et al., 2017; Wiedemann et al., 2018a;b). Whereas much of quantization on to the accuracy of the network research has focused on the sparsification part, a substantially into account. Subsequently, it compresses the less amount have focused on improving the later two quantized values into a bitstream representation steps. In fact, most of the proposed (post-sparsity) compression with minimal redundancies. We show that Deep-algorithms come with at least one of the following CABAC is able to reach very high compression caveats: 1) they decouple the quantization procedure from ratios across a wide set of different network architectures the subsequent lossless compression algorithm, 2) ignore and datasets. For instance, we are correlations between the parameters and 3) apply a lossless able to compress by x63.6 the VGG16 ImageNet compression algorithm that produce a bitstream with more model with no loss of accuracy, thus being able to redundancies than principally needed (e.g.

This paper presents a module of vehicle reidentification based on make/model and color classification. It could be used by the Automated Vehicular Surveillance (AVS) or by the fast analysis of video data. Many of problems, that are related to this topic, had to be addressed. In order to facilitate and accelerate the progress in this subject, we will present our way to collect and to label a large scale data set. We used deeper neural networks in our training. They showed a good classification accuracy. We show the results of make/model and color classification on controlled and video data set. We demonstrate with the help of a developed application the re-identification of vehicles on video images based on make/model and color classification. This work was partially funded under the grant.

However, in recent years symbolic AI has been complemented and sometimes replaced by (Deep) Neural Networks and Machine Learning (ML) techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles--the'what' of AI ethics (beneficence, non-maleficence, autonomy, justice and explicability)--rather than on practices, the'how.' Awareness of the potential issues is increasing at a fast rate, but the AI community's ability to take action to mitigate the associated risks is still at its infancy. Therefore, our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers'apply ethics' at each stage of the pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs.

Neural networks (NN) are considered as black-boxes due to the lack of explainability and transparency of their decisions. This significantly hampers their deployment in environments where explainability is essential along with the accuracy of the system. Recently, significant efforts have been made for the interpretability of these deep networks with the aim to open up the black-box. However, most of these approaches are specifically developed for visual modalities. In addition, the interpretations provided by these systems require expert knowledge and understanding for intelligibility. This indicates a vital gap between the explainability provided by the systems and the novice user. To bridge this gap, we present a novel framework i.e. Time-Series eXplanation (TSXplain) system which produces a natural language based explanation of the decision taken by a NN. It uses the extracted statistical features to describe the decision of a NN, merging the deep learning world with that of statistics. The two-level explanation provides ample description of the decision made by the network to aid an expert as well as a novice user alike. Our survey and reliability assessment test confirm that the generated explanations are meaningful and correct. We believe that generating natural language based descriptions of the network's decisions is a big step towards opening up the black-box.

Current advances in Artificial Intelligence and machine learning in general, and deep learning in particular have reached unprecedented impact not only across research communities, but also over popular media channels. However, concerns about interpretability and accountability of AI have been raised by influential thinkers. In spite of the recent impact of AI, several works have identified the need for principled knowledge representation and reasoning mechanisms integrated with deep learning-based systems to provide sound and explainable models for such systems. Neural-symbolic computing aims at integrating, as foreseen by Valiant, two most fundamental cognitive abilities: the ability to learn from the environment, and the ability to reason from what has been learned. Neural-symbolic computing has been an active topic of research for many years, reconciling the advantages of robust learning in neural networks and reasoning and interpretability of symbolic representation. In this paper, we survey recent accomplishments of neural-symbolic computing as a principled methodology for integrated machine learning and reasoning. We illustrate the effectiveness of the approach by outlining the main characteristics of the methodology: principled integration of neural learning with symbolic knowledge representation and reasoning allowing for the construction of explainable AI systems. The insights provided by neural-symbolic computing shed new light on the increasingly prominent need for interpretable and accountable AI systems.