With the recent renaissance of deep convolution neural networks, encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient training data and fully annotated training data. However, to scale the recognition to a large number of classes with few or now training samples for each class remains an unsolved problem. One approach to scaling up the recognition is to develop models capable of recognizing unseen categories without any training instances, or zero-shot recognition/ learning. This article provides a comprehensive review of existing zero-shot recognition techniques covering various aspects ranging from representations of models, and from datasets and evaluation settings. We also overview related recognition tasks including one-shot and open set recognition which can be used as natural extensions of zero-shot recognition when limited number of class samples become available or when zero-shot recognition is implemented in a real-world setting. Importantly, we highlight the limitations of existing approaches and point out future research directions in this existing new research area.

HANGZHOU, China--(BUSINESS WIRE)--Alibaba Group Holding Ltd. ("Alibaba Group") announced today the launch of an innovative global research program, "Alibaba DAMO Academy ("Academy")," which is designed to increase technological collaboration worldwide, advance the development of cutting-edge technology and strive to make the world more inclusive by narrowing the technology gap. With the setup of the Academy, the company expects to invest more than US$15billion in research and development over the next three years. The Academy, which stands for the "Academy for Discovery, Adventure, Momentum and Outlook," will oversee the opening of research and development labs worldwide and seek to recruit talented scientists and researchers to join the program. Alibaba Group's Chief Technology Officer, Jeff ZHANG will be the head of the Academy. In the beginning, the Academy will focus on the opening of seven research labs in China (Beijing and Hangzhou), the United States (San Mateo and Bellevue), Russia (Moscow), Israel (Tel Aviv) and Singapore.

Google's TensorFlow team released TensorFlow Lattice today to help developers ensure that their machine learning models adhere to global trends even when training data is noisy. Lattice draws from the concept of lookup tables to simplify the process of defining macro rules to restrict models. A lookup table is a representation of data that includes inputs (keys) and outputs (values). It's easiest to conceptualize with a single key linking to a single output, but there can be multiple keys in the case of more complex multi-dimensional functions. Roughly speaking, the TensorFlow team's approach is to train the lookup table values using training data to maximize accuracy given constraints.

Google's TensorFlow team released TensorFlow Lattice today to help developers ensure that their machine learning models adhere to global trends even when training data is noisy. Lattice draws from the concept of lookup tables to simplify the process of defining macro rules to restrict models. A lookup table is a representation of data that includes inputs (keys) and outputs (values).

Reports on 10 August 2015 state China's internet giant Alibaba will pay Suning some 4,2 billion euro or 4,6 billion US dollar for a 20 per cent stake in the company. At the same time, Suning will purchase a bit more than one per cent in Alibaba. The overall value of the action is some 7 billion USD. Alibaba announced Wednesday it will invest $15 billion over the next three years into "cutting-edge technology" including quantum computing and artificial intelligence. The DAMO Academy will oversea the launch of research labs worldwide and recruit scientists and researchers to join. The Chinese e-commerce giant will open seven labs including ones in San Mateo, Calif., and Bellevue, Wash., as well as Beijing, Moscow and Tel Aviv.

Topological Data Analysis (tda) is a recent and fast growing eld providing a set of new topological and geometric tools to infer relevant features for possibly complex data. This paper is a brief introduction, through a few selected topics, to basic fundamental and practical aspects of tda for non experts. 1 Introduction and motivation Topological Data Analysis (tda) is a recent eld that emerged from various works in applied (algebraic) topology and computational geometry during the rst decade of the century. Although one can trace back geometric approaches for data analysis quite far in the past, tda really started as a eld with the pioneering works of Edelsbrunner et al. (2002) and Zomorodian and Carlsson (2005) in persistent homology and was popularized in a landmark paper in 2009 Carlsson (2009). tda is mainly motivated by the idea that topology and geometry provide a powerful approach to infer robust qualitative, and sometimes quantitative, information about the structure of data-see, e.g. Chazal (2017). tda aims at providing well-founded mathematical, statistical and algorithmic methods to infer, analyze and exploit the complex topological and geometric structures underlying data that are often represented as point clouds in Euclidean or more general metric spaces. During the last few years, a considerable eort has been made to provide robust and ecient data structures and algorithms for tda that are now implemented and available and easy to use through standard libraries such as the Gudhi library (C++ and Python) Maria et al. (2014) and its R software interface Fasy et al. (2014a). Although it is still rapidly evolving, tda now provides a set of mature and ecient tools that can be used in combination or complementary to other data sciences tools. The tdapipeline. tda has recently known developments in various directions and application elds. There now exist a large variety of methods inspired by topological and geometric approaches. Providing a complete overview of all these existing approaches is beyond the scope of this introductory survey. However, most of them rely on the following basic and standard pipeline that will serve as the backbone of this paper: 1. The input is assumed to be a nite set of points coming with a notion of distance-or similarity between them. This distance can be induced by the metric in the ambient space (e.g. the Euclidean metric when the data are embedded in R d) or come as an intrinsic metric dened by a pairwise distance matrix. The denition of the metric on the data is usually given as an input or guided by the application. It is however important to notice that the choice of the metric may be critical to reveal interesting topological and geometric features of the data.

If you are out to describe the truth, leave elegance to the tailor. Machine learning is everywhere now, from self-driving cars to Siri and Google Translate, to news recommendation systems and, of course, trading. In the investing world, machine learning is at an inflection point. What was bleeding edge is rapidly going mainstream. It's being incorporated into mainstream tools, news recommendation engines, sentiment analysis, stock screeners. And the software frameworks are increasingly commoditized, so you don't need to be a machine learning specialist to make your own models and predictions. If you're an old-school quant investor, you may have been trained in traditional statistics paradigms and want to see if machine learning can improve your models and predictions. If so, then this primer is for you! Even if you're not planning to build your own models, AI tools are proliferating, and investors who use them will want to know the concepts behind them. And machine learning is transforming society with huge investing implications, so investors should know basically how it works. In school, when we studied modeling and forecasting, we were probably studying statistical methods. Those methods were created by geniuses like Pascal, Gauss, and Bernoulli.

The overview of how regulatory reforms across key regional markets are affecting the overall development of the global artificial intelligence market is also included in the report. Trends across developed, developing, and less-developed regional markets are presented in a comparative manner, providing the reader an overview of the overall scenario. Get PDF brochure of the report: https://www.qyresearchreports.com/sample/sample.php?rep_id 1325102&type E A detailed analysis of the key vendors, recent developments in technologies, distribution of solution providers and consumers, and other technical data pertaining to the most popular artificial intelligence solutions in the market are also included in the report. The report provides an analysis of the key end use industries utilizing artificial intelligence principles and solutions across a number of application segments. A detailed overview of the market has been presented by segmenting the global market on the basis of criteria such as region, application, and technology.

This article analyzes the stochastic runtime of a Cross-Entropy Algorithm on two classes of traveling salesman problems. The algorithm shares main features of the famous Max-Min Ant System with iteration-best reinforcement. For simple instances that have a $\{1,n\}$-valued distance function and a unique optimal solution, we prove a stochastic runtime of $O(n^{6+\epsilon})$ with the vertex-based random solution generation, and a stochastic runtime of $O(n^{3+\epsilon}\ln n)$ with the edge-based random solution generation for an arbitrary $\epsilon\in (0,1)$. These runtimes are very close to the known expected runtime for variants of Max-Min Ant System with best-so-far reinforcement. They are obtained for the stronger notion of stochastic runtime, which means that an optimal solution is obtained in that time with an overwhelming probability, i.e., a probability tending exponentially fast to one with growing problem size. We also inspect more complex instances with $n$ vertices positioned on an $m\times m$ grid. When the $n$ vertices span a convex polygon, we obtain a stochastic runtime of $O(n^{3}m^{5+\epsilon})$ with the vertex-based random solution generation, and a stochastic runtime of $O(n^{2}m^{5+\epsilon})$ for the edge-based random solution generation. When there are $k = O(1)$ many vertices inside a convex polygon spanned by the other $n-k$ vertices, we obtain a stochastic runtime of $O(n^{4}m^{5+\epsilon}+n^{6k-1}m^{\epsilon})$ with the vertex-based random solution generation, and a stochastic runtime of $O(n^{3}m^{5+\epsilon}+n^{3k}m^{\epsilon})$ with the edge-based random solution generation. These runtimes are better than the expected runtime for the so-called $(\mu\!+\!\lambda)$ EA reported in a recent article, and again obtained for the stronger notion of stochastic runtime.

ManageEngine conducted a survey to understand what IT pros need from an advanced analytics tool. The survey looked at the critical areas of analytics and reporting for service desk management around incident management, asset management and change management. Here's what they had to say: Who needs reporting the most? Service desk managers and CIOs are key decision makers who need insight from their data to formulate better strategies. For instance, by tracking including technician performance, service desk responsiveness, IT user preferences, customer satisfaction, SLA violation rate, and other critical metrics, they can better analyze costs, trends, manage resources, and improve overall service quality.