In end-to-end learning of visuomotor behaviors, algorithms such as imitation learning, reinforcement learning (RL), or a combination of both, have achieved remarkable successes in video games [27], board games [36, 37], and robot manipulation tasks [23, 29]. One major issue of using RL alone is its sample efficiency, hence in practice human demonstration can be used to speedup learning [36, 5, 14]. Imitation learning, or learning from demonstration, follows a student-teacher paradigm, in which a learning agent learns from the demonstration of human teachers [1]. A popular approach is behavior cloning, i.e., training an agent to predict (imitate) demonstrated behaviors with supervised learning methods. Imitation learning research mainly focuses on the student-advancing our understanding of the learning agent-while very little effort is made to understand the human teacher. In this work, we argue that understanding and modeling the human teacher is also an important research issue in this paradigm. Specifically, in visuomotor learning tasks, a key component of human intelligence-the visual attention mechanism-encodes a wealth of information that can be exploited by a learning algorithm. Modeling human visual attention and guiding the learning agent with a learned attention model could lead to significant improvement in task performance. We propose the Attention Guided Imitation Learning (AGIL) framework, in which a learning agent first learns a visual attention model from human gaze data, then learns how to perform the visuomotor task from human decision data.

Data living on manifolds commonly appear in many applications. We show that under certain conditions, it is possible to construct an intrinsic and isometric data representation, which respects an underlying latent intrinsic manifold geometry. Namely, instead of learning the structure of the observed manifold, we view the observed data only as a proxy and learn the structure of a latent unobserved intrinsic manifold. For this purpose, we build a new metric and propose a method for robust estimation by assuming mild statistical priors and by using artificial neural networks as a mechanism for metric regularization and parameterization. We show successful application to unsupervised indoor localization in ad-hoc sensor networks. Specifically, we show that our proposed method facilitates accurate localization of a moving agent from imaging data it collects. Importantly, our method is applied in the same way to two different imaging modalities, thereby demonstrating its intrinsic capabilities.

Hello, this is my second article about how to use modern C for solving machine learning problems. This time I will show how to make a model for polynomial regression problem described in previous article, but now with another library which allows you to use your GPU easily. For this tutorial I chose MShadow library, you can find documentation for it here. This library was chosen because it is actively developed now, and used as a basis for one of a wide used deep learning framework MXNet. Also it is a header only library with minimal dependencies, so it's integration is not hard at all.

To help you stay up to date on online training opportunities, we're releasing a monthly list of the latest free Data and Artificial Intelligence (AI) sessions in one convenient post. Azure SQL Database is the intelligent, fully managed relational cloud database service that provides the broadest SQL Server engine compatibility, so you can migrate your SQL Server databases without changing your apps. Accelerate app development and make maintenance easy and productive using the SQL tools you love to use. Here's a rundown of recent and upcoming training sessions to help you learn more. Getting ahead means embracing digital transformation to leverage the cloud.

A lab excercise is show cased to calculate ROC and AUC for a sample data set of logistic regression model. Learn and apply the practical code to test the data. Data Scientists take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics, and programming to clean, massage and organize. But worry not we are here to the rescue and teach you how to be a data scientist, more importantly, upgrade your analytic skills to tackle any problem in the field of data science. Join us on "statinfer.com" for becoming a "scientist in data science" Our "Machine Learning" course is now available on Udemy https://www.udemy.com/machine-learnin... Part 1 – Introduction to R Programming.

What if a car is operated by hand gestures or an automated rover that cleans the floor? Well, around 90 engineering students from Andhra Pradesh and Telangana are working on such innovative concepts to make them a reality. The students, all final year engineering students from reputed educational institutions in the two Telugu States, are here in the city for a two-month internship programme as part of which they work on several artificial intelligence-based projects and developing apps to understand the nitty-gritty of the Internet of Things (IoT). Students from National Institute of Technology (NIT) Warangal, GITAM College of Engineering in Visakhapatnam and GMR Institute of Technology in Srikakulam among others have formed a talent pool that is working simultaneously on various software projects. They have made two floors of a three-storied building at Atchampeta on the city outskirts their workplace while they are putting up in paying guest accommodations and rented houses near their office premises.

When asked to describe their artificial intelligence efforts to date, many IT leaders attending the MIT Sloan CIO Symposium used two words: "slow" and "hype." As that exercise illustrates, many CIOs are still struggling to cut through the noise and better understand how AI and machine learning will impact their businesses moving forward. Are robots coming to take our jobs? Are we going to fall exponentially behind our competitors if we can't hire the best data scientists to deal with AI? The speakers on stage had some answers to these and other pressing questions around AI.

Ireland is witnessing a new age of technological and creative innovation with Cork, Dublin and Galway ranking highest in terms of new ways of sharing, creating and innovating respectively. This is according to the New Renaissance Hotspots Report, commissioned by Huawei and authored independently by the Institute of Arts and Ideas and Kjaer Global. The in-depth analysis scored cities based on key indicators, such as the number of creative and knowledge-based jobs, levels of engagement with digital creativity and cultural activities, as well as the diversity of integration with new technologies. It also considered how well cities were prepared for the future, analysing the number of graduates in key creative and technology-based fields and looking at applications for patents in ICT. The study predicts that the New Renaissance will be responsible for the creation of more than 1.1 million jobs across Europe by 2025 and 1.47 million jobs by 2030.

Multilingual Natural Language Processing Applications is the first comprehensive single-source guide to building robust and accurate multilingual NLP systems. Edited by two leading experts, it integrates cutting-edge advances with practical solutions drawn from extensive field experience. Part I introduces the core concepts and theoretical foundations of modern multilingual natural language processing, presenting today's best practices for understanding word and document structure, analyzing syntax, modeling language, recognizing entailment, and detecting redundancy. Part II thoroughly addresses the practical considerations associated with building real-world applications, including information extraction, machine translation, information retrieval/search, summarization, question answering, distillation, processing pipelines, and more. This book contains important new contributions from leading researchers at IBM, Google, Microsoft, Thomson Reuters, BBN, CMU, University of Edinburgh, University of Washington, University of North Texas, and others.

This past semester I added research to my already full schedule of math and engineering classes, as any masochistic student eagerly would. Packed schedule aside, how do you pass up the chance to work on implementing 360 virtual teleportation to anywhere in the world, in real-time. Yes, it is indeed the same concept as the cult worshipped Star Trek transporter, minus the ability to physically be at the location. Perhaps we can add a, "beam me up, Scotty" command when shutting down. The research lab I was working with is the Laboratory for Immersive CommunicatiON (LION).