Long Short-Term Memory (LSTM) Networks and Convolutional Neural Networks (CNN) have become very common and are used in many fields as they were effective in solving many problems where the general neural networks were inefficient. They were applied to various problems mostly related to images and sequences. Since LSTMs and CNNs take inputs of the same length and dimension, input images and sequences are padded to maximum length while testing and training. This padding can affect the way the networks function and can make a great deal when it comes to performance and accuracies. This paper studies this and suggests the best way to pad an input sequence. This paper uses a simple sentiment analysis task for this purpose. We use the same dataset on both the networks with various padding to show the difference. This paper also discusses some preprocessing techniques done on the data to ensure effective analysis of the data.

This paper describes a reference architecture for self-maintaining systems that can learn continually, as data arrives. In environments where data evolves, we need architectures that manage Machine Learning (ML) models in production, adapt to shifting data distributions, cope with outliers, retrain when necessary, and adapt to new tasks. This represents continual AutoML or Automatically Adaptive Machine Learning. We describe the challenges and proposes a reference architecture.

Artificial Intelligence (AI) is arguably the most revolutionary technology that is seen in several decades having the potential to completely turn the world upside down and then re-shape it with new contours. In the coming years, we will continue to witness the disruption what deep learning and AI-related technologies can bring to create an impact not only to the software and the internet industry but also to other verticals such as manufacturing, automobile, agriculture, and healthcare and so on. AI will reinvent everything from the nature of work to the way we communicate. The disruptive destruction unleashed by AI would make a turbulent impact on the current skills making jobs redundant while opening avenues for new skills. With the rise of AI-enabled chips, convergence of IoT and AI at the edge, and interoperability among neural networks, automated machine learning will gain prominence.

Here at The Next Platform, we've touched on the convergence of machine learning, HPC, and enterprise requirements looking at ways that vendors are trying to reduce the barriers to enable enterprises to leverage AI and machine learning to better address the rapid changes brought about by such emerging trends as the cloud, edge computing and mobility. At the SC17 show in November 2017, Dell EMC unveiled efforts underway to bring AI, machine learning and deep learning into the mainstream, similar to how the company and other vendors in recent years have been working to make it easier for enterprises to adopt HPC techniques for their environments. For Dell EMC, that means in part doing so through bundled, engineered systems. IBM has strategies underway, including through the integration of its PowerAI deep learning enterprise software with its Data Science Experience. Both offerings are aimed at making it easier for enterprises to embrace advance AI technologies and for developers and data scientists to develop and train machine learning models.

Silos have always been considered a bad thing for enterprise IT environments, and today's push for artificial intelligence and other cognitive technologies is no exception. A recent survey shows fewer than 50% of enterprises have deployed any of the "intelligent automation technologies" -- such as artificial intelligence (AI) and robotic process automation (RPA). IT leaders participating in the survey say data and applications within their companies are too siloed to make it work. That's the gist of a survey of 500 IT executives, conducted by IDG in partnership with Appian. The majority of executives, 86%, say they seek to achieve high levels of integration between human work, AI, and RPA over the coming year.

In traditional generative modeling, good data representation is very often a base for a good machine learning model. It can be linked to good representations encoding more explanatory factors that are hidden in the original data. With the invention of Generative Adversarial Networks (GANs), a subclass of generative models that are able to learn representations in an unsupervised and semi-supervised fashion, we are now able to adversarially learn good mappings from a simple prior distribution to a target data distribution. This paper presents an overview of recent developments in GANs with a focus on learning latent space representations.

Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a "thumbs up" icon.

With new technologies like faster payments taking hold, the explosion of readily available data, and the ever-changing regulatory landscape, staying ahead of financial crime and compliance risk has become more complex and expensive than ever before. As these trends show no sign of abating, the compliance operations and monitoring staff of a financial institution often find themselves a major cost center. Financial institutions must manage compliance budgets without losing sight of primary functions and quality control. To answer this, many have made the move to automating time-intensive, rote tasks like data gathering and sorting through alerts by adopting innovative technologies like AI and machine learning to free up time-strapped analysts for more informed and precise decision-making processes. As financial institutions often benchmark themselves against their competitors, they are increasingly interested in seeing how these technologies are performing, and are asking themselves how to leverage artificial intelligence and machine learning to increase insight, reduce false positives and decrease compliance spend.

Neural networks [15] have been widely used in many applications, such as image classification and understanding [17], language processing [24], and control of autonomous systems [26]. These networks represent functions that map inputs to outputs through a sequence of layers. At each layer, the input to that layer undergoes an affine transformation followed by a simple nonlinear transformation before being passed to the next layer. These nonlinear transformations are often called activation functions, and a common example is the rectified linear unit (ReLU), which transforms the input by setting any negative values to zero. Although the computation involved in a neural network is quite simple, these networks can represent complex nonlinear functions by appropriately choosing the matrices that define the affine transformations.

Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models. To address this, we propose a probabilistic programming approach to affective computing, which models psychological-grounded theories as generative models of emotion, and implements them as stochastic, executable computer programs. We first review probabilistic approaches that integrate reasoning about emotions with reasoning about other latent mental states (e.g., beliefs, desires) in context. Recently-developed probabilistic programming languages offer several key desidarata over previous approaches, such as: (i) flexibility in representing emotions and emotional processes; (ii) modularity and compositionality; (iii) integration with deep learning libraries that facilitate efficient inference and learning from large, naturalistic data; and (iv) ease of adoption. Furthermore, using a probabilistic programming framework allows a standardized platform for theory-building and experimentation: Competing theories (e.g., of appraisal or other emotional processes) can be easily compared via modular substitution of code followed by model comparison. To jumpstart adoption, we illustrate our points with executable code that researchers can easily modify for their own models. We end with a discussion of applications and future directions of the probabilistic programming approach.