This repository is intended as a companion to the manuscript Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation. In particular, this repository provides all the tools necessary to reproduce all results presented in the above mentioned paper. Furthermore, it is hoped that this repository may serve as a starting-point for extending these tools and techniques. In this readme, we will provide a summary and walkthrough of all the information contained within the included notebooks. However, we recommend starting by reading the included manuscript Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation. To explore the code used for training and evaluating agents, as well as take a more detailed look at the results, please see the example notebooks.

We consider the tensor completion problem of predicting the missing entries of a tensor. The commonly used CP model has a triple product form, but an alternate family of quadratic models which are the sum of pairwise products instead of a triple product have emerged from applications such as recommendation systems. Non-convex methods are the method of choice for learning quadratic models, and this work examines their sample complexity and error guarantee. Our main result is that with the number of samples being only linear in the dimension, all local minima of the mean squared error objective are global minima and recover the original tensor accurately. The techniques lead to simple proofs showing that convex relaxation can recover quadratic tensors provided with linear number of samples. We substantiate our theoretical results with experiments on synthetic and real-world data, showing that quadratic models have better performance than CP models in scenarios where there are limited amount of observations available.

Unsupervised dimension selection is an important problem that seeks to reduce dimensionality of data, while preserving the most useful characteristics. While dimensionality reduction is commonly utilized to construct low-dimensional embeddings, they produce feature spaces that are hard to interpret. Further, in applications such as sensor design, one needs to perform reduction directly in the input domain, instead of constructing transformed spaces. Consequently, dimension selection (DS) aims to solve the combinatorial problem of identifying the top-$k$ dimensions, which is required for effective experiment design, reducing data while keeping it interpretable, and designing better sensing mechanisms. In this paper, we develop a novel approach for DS based on graph signal analysis to measure feature influence. By analyzing synthetic graph signals with a blue noise spectrum, we show that we can measure the importance of each dimension. Using experiments in supervised learning and image masking, we demonstrate the superiority of the proposed approach over existing techniques in capturing crucial characteristics of high dimensional spaces, using only a small subset of the original features.

Machine learning techniques have deeply rooted in our everyday life. However, since it is knowledge- and labor-intensive to pursuit good learning performance, human experts are heavily engaged in every aspect of machine learning. In order to make machine learning techniques easier to apply and reduce the demand for experienced human experts, automatic machine learning~(AutoML) has emerged as a hot topic of both in industry and academy. In this paper, we provide a survey on existing AutoML works. First, we introduce and define the AutoML problem, with inspiration from both realms of automation and machine learning. Then, we propose a general AutoML framework that not only covers almost all existing approaches but also guides the design for new methods. Afterward, we categorize and review the existing works from two aspects, i.e., the problem setup and the employed techniques. Finally, we provide a detailed analysis of AutoML approaches and explain the reasons underneath their successful applications. We hope this survey can serve as not only an insightful guideline for AutoML beginners but also an inspiration for future researches.

Abstract-- This paper presents a novel approach to the facial expression generation problem. Building upon the assumption of the psychological community that emotion is intrinsically continuous, we first design our own continuous emotion representation with a 3-dimensional latent space issued from a neural network trained on discrete emotion classification. The so-obtained representation can be used to annotate large in the wild datasets and later used to trained a Generative Adversarial Network. We first show that our model is able to map back to discrete emotion classes with a objectively and subjectively better quality of the images than usual discrete approaches. But also that we are able to pave the larger space of possible facial expressions, generating the many moods of emotion. Moreover, two axis in this space may be found to generate similar expression changes as in traditional continuous representations such as arousalvalence. Finally we show from visual interpretation, that the third remaining dimension is highly related to the well-known dominance dimension from psychology. Affective computing is a topic of broad interest, finding applications in many fields such as healthcare, marketing or human-machine interfaces.

Maybe you've used text analytics methods to analyze free-form textual feedback? Here, we break down 5 key text analytics approaches, and share examples of how text analytics is used by businesses today. Plus, you'll also get the bonus Text Analytics Cheat Sheet! Download the e-book now and get the bonus Text Analytics Cheat Sheet, too!

Abstract--This paper investigates the vision-based autonomous driving with deep learning and reinforcement learning methods. Different from the end-to-end learning method, our method breaks the vision-based lateral control system down into a perception module and a control module. The perception module which is based on a multi-task learning neural network first takes a driver-view image as its input and predicts the track features. The control module which is based on reinforcement learning then makes a control decision based on these features. In order to improve the data efficiency, we propose visual TORCS (VTORCS), a deep reinforcement learning environment which is based on the open racing car simulator (TORCS). By means of the provided functions, one can train an agent with the input of an image or various physical sensor measurement, or evaluate the perception algorithm on this simulator. The trained reinforcement learning controller outperforms the linear quadratic regulator (LQR) controller and model predictive control (MPC) controller on different tracks. The experiments demonstrate that the perception module shows promising performance and the controller is capable of controlling the vehicle drive well along the track center with visual input. N recent years, artificial intelligence (AI) has flourished in many fields such as autonomous driving [1] [2], games [3] [4], and engineering applications [5] [6]. As one of the most popular topics, autonomous driving has drawn great attention both from the academic and industrial communities and is thought to be the next revolution in the intelligent transportation system. The autonomous driving system mainly consists of four modules: an environment perception module, a trajectory planning module, a control module, and an actuator mechanism module. The initial perception methods [7] [8] are based on the expensive LIDARs which usually cost tens of thousands of dollars. The high cost limits their large-scale applications to the ordinary vehicles. Recently, more attention is paid to the image-based methods [9] of which the core sensor, i.e. camera is relatively cheap and already equipped on most vehicles. Some of these perception methods have been developed into products [10] [11]. In this paper, we focus on the lateral control problem based on the image captured by the onboard camera.

The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world {via} creating new services and applications in a variety of sectors such as environmental monitoring, mobile-health systems, intelligent transportation systems and so on. The {information and communication technology }(ICT) sector is experiencing a significant growth in { data} traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. {It} is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy {via} providing a broad overview of data collection, storage, access, processing and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS {require} cybersecurity to protect {them} against malicious attacks and unauthorized intrusion, which {become} a challenge with the enormous amount of data that is continuously being generated in the network. {Thus, we also} provide an overview of the different security solutions proposed for CPS big data storage, access and analytics. We also discuss big data meeting green challenges in the contexts of CPS.

Editor's note: The use of AI in LinkedIn products has been the subject of multiple press articles and research papers (some highlighted on this blog). With the release of a new LinkedIn Learning course about AI at LinkedIn, we asked our Head of AI, Deepak Agarwal, for a brief overview of what AI is and how it works, geared towards people who are interested in this growing field. In this post, we discuss AI as a broad topic and look at a few ways that it influences product design at LinkedIn. Back in 2005, I was working in my first job at AT&T Bell Labs. The telecommunications industry was struggling due to price wars and increased competition from wireless carriers.

Women are redefining the boundaries set by age-old prejudices in almost every field. The arena of technology is no exception. The domain of technology is evolving every day owing to various entrepreneurs and inventors leading the way. In this backdrop, female entrepreneurs are making a significant contribution to the field of robotics. Women are altering the way humans will interact with robots in the future. From sophisticated drones to projecting and implementing symbiotic ideas, women are reshaping the future of the industry.