Generating a long, coherent text such as a paragraph requires a high-level control of different levels of relations between sentences (e.g., tense, coreference). We call such a logical connection between sentences as a (paragraph) flow. In order to produce a coherent flow of text, we explore two forms of intersentential relations in a paragraph: one is a human-created linguistical relation that forms a structure (e.g., discourse tree) and the other is a relation from latent representation learned from the sentences themselves. Our two proposed models incorporate each form of relations into document-level language models: the former is a supervised model that jointly learns a language model as well as discourse relation prediction, and the latter is an unsupervised model that is hierarchically conditioned by a recurrent neural network (RNN) over the latent information. Our proposed models with both forms of relations outperform the baselines in partially conditioned paragraph generation task. Our codes and data are publicly available.

Learning target side syntactic structure has been shown to improve Neural Machine Translation (NMT). However, incorporating syntax through latent variables introduces additional complexity in inference, as the models need to marginalize over the latent syntactic structures. To avoid this, models often resort to greedy search which only allows them to explore a limited portion of the latent space. In this work, we introduce a new latent variable model, LaSyn, that captures the co-dependence between syntax and semantics, while allowing for effective and efficient inference over the latent space. LaSyn decouples direct dependence between successive latent variables, which allows its decoder to exhaustively search through the latent syntactic choices, while keeping decoding speed proportional to the size of the latent variable vocabulary. We implement LaSyn by modifying a transformer-based NMT system and design a neural expectation maximization algorithm that we regularize with part-of-speech information as the latent sequences. Evaluations on four different MT tasks show that incorporating target side syntax with LaSyn improves both translation quality, and also provides an opportunity to improve diversity.

Processing human affective behavior is important for developing intelligent agents that interact with humans in complex interaction scenarios. A large number of current approaches that address this problem focus on classifying emotion expressions by grouping them into known categories. Such strategies neglect, among other aspects, the impact of the affective responses from an individual on their interaction partner thus ignoring how people empathize towards each other. This is also reflected in the datasets used to train models for affective processing tasks. Most of the recent datasets, in particular, the ones which capture natural interactions ("in-the-wild" datasets), are designed, collected, and annotated based on the recognition of displayed affective reactions, ignoring how these displayed or expressed emotions are perceived. In this paper, we propose a novel dataset composed of dyadic interactions designed, collected and annotated with a focus on measuring the affective impact that eight different stories have on the listener. Each video of the dataset contains around 5 minutes of interaction where a speaker tells a story to a listener. After each interaction, the listener annotated, using a valence scale, how the story impacted their affective state, reflecting how they empathized with the speaker as well as the story. We also propose different evaluation protocols and a baseline that encourages participation in the advancement of the field of artificial empathy and emotion contagion.

To be a real full-stack data scientist, or what many blog posts and employers call a "unicorn," you've to master every step of the data science process -- all the way from storing your data, to putting your finished product (typically a predictive model) in production. But the bulk of data science training focuses on machine/deep learning techniques; data management knowledge is often treated as an afterthought. Data science students usually learn modeling skills with processed and cleaned data in text files stored on their laptop, ignoring how the data sausage is made. Students often don't realize that in industry settings, getting the raw data from various sources to be ready for modeling is usually 80% of the work. And because enterprise projects usually involve a massive amount of data that their local machine is not equipped to handle, the entire modeling process often takes place in the cloud, with most of the applications and databases hosted on servers in data centers elsewhere.

Our sequential non-text information is best harnessed in a Bidirectional LSTM – a type of sequential model described in more detail here and here – that allows the model to learn end-of-sequence and beginning-of-sequence behavior. This maps to domain experts' knowledge that distinctive behavior at the end of the subscription period presages churn. It also captures the patterns in the progression of events over time that can be used to predict eventual churn. On the other hand our textual and categorical data need a separate model to learn from this differently structured data. We have several options here.

Our training programs are practical fast-paced programs to get you into Artificial Intelligence and Data Science domain and its sub-fields immediately. Our training programs consist of four Courses: Big Data Management, Data Analytics & Visualization, Machine Learning, Deep Learning and Computer Vision. Please leave this field empty. Please leave this field empty. Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing.

Artificial intelligence (AI) is improving everyday solutions, driving efficiency in ways we never imagined possible. From self-driving cars to intelligent analytics, the far-reaching impacts of Deep Learning-based technology empower human operators to achieve results more effectively while investing fewer resources and less time. By introducing AI, solutions are not merely powered by data, but they also generate valuable intelligence. Systems which were once leveraged for a narrow, dedicated purpose, can suddenly be engaged broadly across an organization, because the previously under-utilized data can be harnessed for enhancing productivity and performance. When it comes to physical security, for instance, video surveillance is a standard solution.

Amazon's Werner Vogels announced last week that the deep learning library MXNet would be their officially adopted deep learning framework, and that AWS would contribute to its long-term success through increased open-source code contributions, improved documentation, and supporting tools for visualization, development and migration from other frameworks. Vogels noted the class of Machine learning, known as deep learning, increasingly addresses a range of computing tasks where programming explicit algorithms is infeasible. These are domains like fraud detection, recommendation pipelines, inventory and product review auditing. Machine leaning is also extensively used in search, autonomous drones, robotics in fulfillment centers, text and speech recognition. Vogels noted three factors used to consider deep learning framework choices; the ability to scale, development speed, and portability.

International Data Corporation (IDC) recently published an IDC Innovators report profiling five companies that offer compelling and differentiated computer vision software. The five companies are Algolux, Deep Vision AI, Sighthound, ViSenze, and Umbo CV. Computer vision is an AI technology that allows computers to understand and label images. Use cases include video surveillance, driverless car testing, daily medical diagnostics, and monitoring the health of crops and livestock. AI is used for pattern recognition and learning techniques driven largely by machine learning (ML) and deep learning (DL) algorithms that bring visual understanding capabilities in a growing variety of hardware and software applications.

Abstract: This paper describes neural-fortran, a parallel Fortran framework for neural networks and deep learning. It features a simple interface to construct feed-forward neural networks of arbitrary structure and size, several activation functions, and stochastic gradient descent as the default optimization algorithm. Neural-fortran also leverages the Fortran 2018 standard collective subroutines to achieve data-based parallelism on shared- or distributed-memory machines. First, I describe the implementation of neural networks with Fortran derived types, whole-array arithmetic, and collective sum and broadcast operations to achieve parallelism. Second, I demonstrate the use of neural-fortran in an example of recognizing hand-written digits from images.