Deep learning methods have recently achieved great empirical success on machine translation, dialogue response generation, summarization, and other text generation tasks. At a high level, the technique has been to train end-to-end neural network models consisting of an encoder model to produce a hidden representation of the source text, followed by a decoder model to generate the target. While such models have significantly fewer pieces than earlier systems, significant tuning is still required to achieve good performance. For text generation models in particular, the decoder can behave in undesired ways, such as by generating truncated or repetitive outputs, outputting bland and generic responses, or in some cases producing ungrammatical gibberish. This paper is intended as a practical guide for resolving such undesired behavior in text generation models, with the aim of helping enable real-world applications.

Abstract--With the spreading prevalence of Big Data, many advances have recently been made in this field. Frameworks such as Apache Hadoop and Apache Spark have gained a lot of traction over the past decades and have become massively popular, especially in industries. It is becoming increasingly evident that effective big data analysis is key to solving artificial intelligence problems. Thus, a multi-algorithm library was implemented in the Spark framework, called MLlib. While this library supports multiple machine learning algorithms, there is still scope to use the Spark setup efficiently for highly timeintensive and computationally expensive procedures like deep learning. In this paper, we propose a novel framework that combines the distributive computational abilities of Apache Spark and the advanced machine learning architecture of a deep multilayer perceptron (MLP), using the popular concept of Cascade Learning. We conduct empirical analysis of our framework on two real world datasets. The results are encouraging and corroborate our proposed framework, in turn proving that it is an improvement over traditional big data analysis methods that use either Spark or Deep learning as individual elements. A. Overview I. INTRODUCTION With the amount of data growing at an exponential rate, it is necessary to develop tools that are able to harness that data and extract value from it.

Note: If you are looking for a review paper, this blog post is also available as an article on arXiv. Added derivations of AdaMax and Nadam. Gradient descent is one of the most popular algorithms to perform optimization and by far the most common way to optimize neural networks. At the same time, every state-of-the-art Deep Learning library contains implementations of various algorithms to optimize gradient descent (e.g. These algorithms, however, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by.

Frank Chen of a16z is highly regarded as one of the great creators of Silicon Valley. He is also one of the great teacher/mentors and this video on Artificial Intelligence proves that. Frank provides a fantastic overview of Artificial Intelligence (AI), including its history, its roots in human mythology and fiction, and its birthday! The term Artificial Intelligence, the birthday can be marked in the summer of 1956 when a group of researchers kicked off research at Dartmouth to research with the aim of creating an artificially intelligence being. This is the birth of the discipline.

The insurance industry – like many elements within Financial Services (FS) – has come under intense pressure over the past decade or so. The fintech revolution has meant that smaller and more agile startups are able to offer a variety of new services to consumers and businesses. These services are not only more interactive and based on the latest technologies, but they are also services that bigger insurance firms cannot easily offer. This increased competition from newer market entrants is a growing problem for more established insurance providers. A 2016 PwC survey revealed that 65 per cent of insurance chief executives see new market entrants as a threat to growth, while 69 per cent of insurance chiefs were concerned about the speed of technological change in their industry.

Arthur C. Clarke famously stated that "any sufficiently advanced technology is indistinguishable from magic." No current technology embodies this statement more than neural networks and deep learning. And like any good magic it not only dazzles and inspires but also puts fear into people's hearts. One known property of artificial neural networks (ANNs) is that they are universal function approximators. This means that any mathematical function can be represented by a neural network.

Artificial Intelligence (AI) remained the driving force of various industries in 2017. With so many tech giants and startups already delving into the AI ecosystem, it is expected to grow with better use cases in the year 2018. Considering the acceptance, development, and applications of AI, here we are with significant opportunities and perils that this ingenious technology will put forth in 2018. "Over the next few years every app, application and service will incorporate AI at some level." Artificial Intelligence (AI) is anticipated to be on the quiet in most of the web and mobile applications.

The European real estate sector continues to flourish in regions such as the UK and Germany, despite strong and unpredictable economic and political headwinds. Successful transactions depend on high quality and detailed due diligence, but competition for the most lucrative deals can sometimes lead organisations to compromise on this stage of the process. The biggest challenge is that the size of real estate transactions is increasing exponentially because of regulatory and compliance requirements and also because of the broader volumes and types of documents involved. This means that more manual processes are required simply to find the right data. The trend for higher volumes and larger transactions in real estate, including higher levels of risk and multiple languages, has important implications for the way in which investment professionals manage the greater complexity of due diligence.

Lexical Simplification is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning. This task has wide applicability both as an assistive technology for readers with cognitive impairments or disabilities, such as Dyslexia and Aphasia, and as a pre-processing tool for other Natural Language Processing tasks, such as machine translation and summarisation. The problem is commonly framed as a pipeline of four steps: the identification of complex words, the generation of substitution candidates, the selection of those candidates that fit the context, and the ranking of the selected substitutes according to their simplicity. In this survey we review the literature for each step in this typical Lexical Simplification pipeline and provide a benchmarking of existing approaches for these steps on publicly available datasets. We also provide pointers for datasets and resources available for the task.

Testing (conditional) independence of multivariate random variables is a task central to statistical inference and modelling in general - though unfortunately one for which to date there does not exist a practicable workflow. State-of-art workflows suffer from the need for heuristic or subjective manual choices, high computational complexity, or strong parametric assumptions. We address these problems by establishing a theoretical link between multivariate/conditional independence testing, and model comparison in the multivariate predictive modelling aka supervised learning task. This link allows advances in the extensively studied supervised learning workflow to be directly transferred to independence testing workflows - including automated tuning of machine learning type which addresses the need for a heuristic choice, the ability to quantitatively trade-off computational demand with accuracy, and the modern black-box philosophy for checking and interfacing. As a practical implementation of this link between the two workflows, we present a python package 'pcit', which implements our novel multivariate and conditional independence tests, interfacing the supervised learning API of the scikit-learn package. Theory and package also allow for straightforward independence test based learning of graphical model structure. We empirically show that our proposed predictive independence test outperform or are on par to current practice, and the derived graphical model structure learning algorithms asymptotically recover the 'true' graph. This paper, and the 'pcit' package accompanying it, thus provide powerful, scalable, generalizable, and easy-to-use methods for multivariate and conditional independence testing, as well as for graphical model structure learning.