A number of techniques have been proposed for aspect discovery using part of speech tagging (Hu and Liu, 2004), syntactic parsing (Lu et al., 2009), clustering (Mei et al., 2007; Titov and McDonald, 2008b), data mining (Ku et al., 2006), and information extraction (Popescu and Etzioni, 2005). Various lexicon and rule-based methods (Hu and Liu, 2004; Ku et al., 2006; Blair-Goldensohn et al., 2008) have been adopted for sentiment prediction together with a few learning approaches (Lu et al., 2009; Pappas and Popescu-Belis, 2017; Angelidis and Lapata, 2018). As for the summaries, a common format involves a list of aspects and the number of positive and negative opinions for each (Hu and Liu, 2004). While this format gives an overall idea of people's opinion, reading the actual text might be necessary to gain a better understanding of specific details. Textual summaries are created following mostly extractive methods (but see Ganesan et al. 2010 for an abstractive approach), and various formats ranging from lists of words (Popescu and Etzioni, 2005), to phrases (Lu et al., 2009), and sentences (Mei et al., 2007; Blair-Goldensohn et al., 2008; Lerman et al., 2009; Wang and Ling, 2016). In this paper, we present a neural framework for opinion extraction from product reviews. We follow the standard architecture for aspect-based summarization, while taking advantage of the success of neural network models in learning continuous features without recourse to preprocessing tools or linguistic annotations.

In a timely new paper, Young and colleagues discuss some of the recent trends in deep learning based natural language processing (NLP) systems and applications. The focus of the paper is on the review and comparison of models and methods that have achieved state-of-the-art (SOTA) results on various NLP tasks such as visual question answering (QA) and machine translation. In this comprehensive review, the reader will get a detailed understanding of the past, present, and future of deep learning in NLP. In addition, readers will also learn some of the current best practices for applying deep learning in NLP. Natural language processing (NLP) deals with building computational algorithms to automatically analyze and represent human language. NLP-based systems have enabled a wide range of applications such as Google's powerful search engine, and more recently, Amazon's voice assistant named Alexa.

With the rising influence of machine learning algorithms on many important aspects of our daily lives, there are growing concerns that biases inherent in data can lead the behavior of these algorithms to discriminate against certain populations [1, 2, 4, 6, 8, 28, 29, 15]. In recent years, substantial research effort has been devoted to the development of mathematical definitions of bias, or its opposite, fairness, in algorithms and in data [15, 18, 26, 23, 19, 32]. In this work, we focus on the fairness scenario where there are multiple protected attributes that we aim to ensure fairness for, and which may potentially overlap with each other, such as gender, race, and sexual orientation. Our guiding principle is intersectionality, the core theoretical framework underlying the thirdwave feminist movement [13]. The principle of intersectionality states that racism, sexism, and other social systems which harm marginalized groups are interlocking in their effects, such that the lived experience of, e.g., black women, is very different than that of, e.g., white women. Intersectionality was defined by Kimberlé Crenshaw in the 1980's [13] and popularized in the 1990's, e.g. by Patricia Hill Collins [10], although the ideas are much older [11, 35]. In the context of machine learning and fairness, intersectionality was recently considered by [7], who studied the impact of the intersection of gender and skin color on computer vision performance, and by [23, 19], who aimed to protect certain subgroups in order to prevent "fairness gerrymandering."

Real-world networks usually have community structure, that is, nodes are grouped into densely connected communities. Community detection is one of the most popular and best-studied research topics in network science and has attracted attention in many different fields, including computer science, statistics, social sciences, among others. Numerous approaches for community detection have been proposed in literature, from ad-hoc algorithms to systematic model-based approaches. The large number of available methods leads to a fundamental question: whether a certain method can provide consistent estimates of community labels. The stochastic blockmodel (SBM) and its variants provide a convenient framework for the study of such problems. This article is a survey on the recent theoretical advances of community detection. The authors review a number of community detection methods and their theoretical properties, including graph cut methods, profile likelihoods, the pseudo-likelihood method, the variational method, belief propagation, spectral clustering, and semidefinite relaxations of the SBM. The authors also briefly discuss other research topics in community detection such as robust community detection, community detection with nodal covariates and model selection, as well as suggest a few possible directions for future research.

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that an edge-based deployment can also be used as an enabler of advanced Machine Learning (ML) applications in cellular networks, thanks to the balance it strikes between a completely distributed and a centralized approach. First, we will present an edge-controller-based architecture for cellular networks. Second, by using real data from hundreds of base stations of a major U.S. national operator, we will provide insights on how to dynamically cluster the base stations under the domain of each controller. Third, we will describe how these controllers can be used to run ML algorithms to predict the number of users, and a use case in which these predictions are used by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that prediction accuracy improves when based on machine learning algorithms that exploit the controllers' view with respect to when it is based only on the local data of each single base station. The next generation of cellular networks (5G) is being designed to satisfy the massive growth in capacity demand, number of connections and the evolving use cases of a connected society for 2020 and beyond [1]. Michele Polese and Michele Zorzi are with the Department of Information Engineering (DEI), University of Padova, Italy. In order to meet these requirements, a new approach in the design of the network is required, and new paradigms have recently emerged [3]. First, the densification of the network will increase the spatial reuse and, combined with the usage of mmWave frequencies, the available throughput. On the other hand, this will introduce new challenges related to mobility management [4].

For example, given a view of a chair with two rear legs occluded by front legs, humans are easily able to guess the most likely shape behind the visible parts. Recent advances in deep neural networks and data driven approaches show promising results in dealing with such a task. In this paper, we aim to acquire the complete and highresolution 3D shape of an object given a single depth view. By leveraging the high performance of 3D convolutional neural nets and large open datasets of 3D models, our approach learns a smooth function that maps a 2.5D view to a complete and dense 3D shape. In particular, we train an endto-end model which estimates full volumetric occupancy from a single 2.5D depth view of an object. As a result, the learnt 3D structure tends to be coarse and inaccurate. In order to generate higher resolution 3D objects with efficient computation, Octree representation has been recently introduced in [13] [14] [15]. However, increasing the density of output 3D shapes would also inevitably pose a great challenge to learn the geometric details for high resolution 3D structures, which has yet to be explored. Recently, deep generative models achieve impressive success in modeling complex high-dimensional data distributions, among which Generative Adversarial Networks (GANs) [16] and Variational Autoencoders (VAEs) [17] emerge as two powerful frameworks for generative learning, including image and text generation [18] [19], and latent space learning [20] [21]. In the past few years, a number of works [22] [23] [24] [25] applied such generative models to learn latent space to represent 3D object shapes, in order to solve tasks such as new image generation, object classification, recognition and shape retrieval. Abstract--In this paper, we propose a novel approach, 3D-RecGAN, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks.

Aug 17, 2018 (Heraldkeeper via COMTEX) -- The market research report on the global Artificial Intelligence In Healthcare industry provides a comprehensive study of the various techniques and materials used in the production of Artificial Intelligence In Healthcare market products. Starting from industry chain analysis to cost structure analysis, the report analyzes multiple aspects, including the production and end-use segments of the Artificial Intelligence In Healthcare market products. The latest trends in the pharmaceutical industry have been detailed in the report to measure their impact on the production of Artificial Intelligence In Healthcare market products. Get Sample PDF with Global Trends! https://goo.gl/5UpbvT Results of the recent scientific undertakings towards the development of new Artificial Intelligence In Healthcare products have been studied.

Spatiotemporal systems are common in the real-world. Forecasting the multi-step future of these spatiotemporal systems based on the past observations, or, Spatiotemporal Sequence Forecasting (STSF), is a significant and challenging problem. Although lots of real-world problems can be viewed as STSF and many research works have proposed machine learning based methods for them, no existing work has summarized and compared these methods from a unified perspective. This survey aims to provide a systematic review of machine learning for STSF. In this survey, we define the STSF problem and classify it into three subcategories: Trajectory Forecasting of Moving Point Cloud (TF-MPC), STSF on Regular Grid (STSF-RG) and STSF on Irregular Grid (STSF-IG). We then introduce the two major challenges of STSF: 1) how to learn a model for multi-step forecasting and 2) how to adequately model the spatial and temporal structures. After that, we review the existing works for solving these challenges, including the general learning strategies for multi-step forecasting, the classical machine learning based methods for STSF, and the deep learning based methods for STSF. We also compare these methods and point out some potential research directions.

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent.

Sessions are subject to change. Day & Time: Wednesday, 11:00 AM – 11:45 AM, Grand Oaks A&B Presenter: Jim Breen, Doug Foster Need to get your team trained on InterSystems products quickly? Attend this session to learn how you can get your employees up to speed and add value to your company – fast! Hear how other InterSystems' clients have created successful teams using Learning Services content as one piece of the puzzle, and how you can too! Takeaway: InterSystems Learning Services can help me quickly onboard new employees and grow the skill sets of existing employees. Day & Time: Monday, 2:00 PM – 2:45 PM, Grand Oaks E&F Tuesday, 2:00 PM – 2:45 PM, Grand Oaks C&D Presenter: Andreas Dieckow This session provides an overview of what it takes to move an existing Caché or Ensemble application to InterSystems IRIS Data Platform. You will learn that migration is not urgent (unless you want to take advantage of new features in InterSystems IRIS) but that it is often less complex than you might expect.