However, typical constructions of a similarity graph have high time complexity, and a quadratic space dependency with respect to |X|. We address this limitation and present a new algorithmic framework that constructs a sparse approximation of the fully connected similarity graph while preserving its cluster structure. Our presented algorithm is based on the kernel density estimation problem, and is applicable for arbitrary kernel functions. We compare our designed algorithm with the well-known implementations from the scikit-learn library and the FAISS library, and find that our method significantly outperforms the implementation from both libraries on a variety of datasets.

However, typical constructions of a similarity graph have high time complexity, and a quadratic space dependency with respect to |X|. We address this limitation and present a new algorithmic framework that constructs a sparse approximation of the fully connected similarity graph while preserving its cluster structure. Our presented algorithm is based on the kernel density estimation problem, and is applicable for arbitrary kernel functions. We compare our designed algorithm with the well-known implementations from the scikit-learn library and the FAISS library, and find that our method significantly outperforms the implementation from both libraries on a variety of datasets.

We pledge "to prove IT can make a real difference to our customer's businesses". We work hard to ensure we understand what our customers need from their technology solutions and then we deliver. We are an award-winning company who provide world class customer service; we think big and we hire great people. Version 1 are more than just another IT services company - we are leaders in implementing and supporting Oracle, Microsoft and AWS technologies. Version 1 is looking for an experienced data engineer join its Managed Services team.

Excellent opportunity for an experienced Data Engineer to join our awesome Engineering team! We are building a brand new software product - Corvidae. Powered by machine learning and capable of handling massive amounts of marketing behaviour data, the Corvidae product allows its users to understand the value of their marketing activity and use this information to automatically optimise that activity. You will work in a small team of multi discipline engineers to develop, maintain, and deliver data pipeline solutions to high standards and appropriate scale AND to develop, maintain, and deliver data storage and management solutions to acceptable standards. We understand that our team are our greatest asset, so we invest heavily in training, benefits and incentives to ensure our team are happy and productive.

Sword is a leader in data insights, digital transformation, and technology services with a substantial reputation in software development, complex business IT projects and mission critical operations. With over 2,500 Technology, Digital & Software specialists working globally, we unlock solutions to the most critical business technology challenges. Working within our Professional Service Business Unit as a Data Engineer, this position will suit someone who can provide architectural and data engineering subject matter expertise in our client projects. You will join a highly experienced team in our Data & AI practice who are supporting Sword's clients on their journey to become data driven organisations. Sword offers career paths in rapidly evolving technology spaces including Data & AI, Modern Managed Services, Information Management, Digital Services, Content Services, and Modern Workplace Transformation.

We help the world see new possibilities and inspire change for better tomorrows. Our analytic solutions bridge content, data, and analytics to help business, people, and society become stronger, more resilient, and sustainable. Wood Mackenzie is looking for a dynamic Data Engineering Manager with demonstrated leadership capability to actively lead the way in modern software development practices and standards. This role is integral to a high functioning and innovative team, providing a unique blend of business and technical savvy to perceive the big-picture vision with the know-how to make that vision a reality. The person that fills this role must be a self-starter with a strong work ethic, energized by a challenge, passionate about bringing great products to market and love the thrill of creating a new standard for what's possible.

As part of the continued strategy and growth of Decision Point AI it has today incorporated it's professional services consulting business Decision Point AI UK as Decision Point AI Limited in Edinburgh, Scotland. We have embarked upon a major strategic engagement in the UK, USA and the Europe, using Scotland as a launch pad. Today we formally launch Decision Point AI UK our UK Professional Services Consulting capability with the'problem solved' attitude to servicing clients issues. As with our UK consulting launches 3 month lead period, we also involved in soft launches in the USA from NYC and Europe from Dublin. The company continues to gain clients and markets based on the prior'BigFour' relationships from its leadership team and new clients from the thriving Scottish technology and innovation scene.

This thesis presents new methods for unsupervised learning of distributed representations of words and entities from text and knowledge bases. The first algorithm presented in the thesis is a multi-view algorithm for learning representations of words called Multiview Latent Semantic Analysis (MVLSA). By incorporating up to 46 different types of co-occurrence statistics for the same vocabulary of english words, I show that MVLSA outperforms other state-of-the-art word embedding models. Next, I focus on learning entity representations for search and recommendation and present the second method of this thesis, Neural Variational Set Expansion (NVSE). NVSE is also an unsupervised learning method, but it is based on the Variational Autoencoder framework. Evaluations with human annotators show that NVSE can facilitate better search and recommendation of information gathered from noisy, automatic annotation of unstructured natural language corpora. Finally, I move from unstructured data and focus on structured knowledge graphs. I present novel approaches for learning embeddings of vertices and edges in a knowledge graph that obey logical constraints.

Amazon worked on building an artificial-intelligence tool to help with hiring, but the plans backfired when the company discovered the system discriminated against women, Reuters reports. Citing five sources, Reuters said Amazon set up an engineering team in Edinburgh, Scotland, in 2014 to find a way to automate its recruitment. The company created 500 computer models to trawl through past candidates' résumés and pick up on about 50,000 key terms. The system would crawl the web to recommend candidates. "They literally wanted it to be an engine where I'm going to give you 100 résumés, it will spit out the top five, and we'll hire those," one source told Reuters.

Imformatics PHD student Sebastian Bitzer performs push up exercises with a programmed Kondo humanoid robot at the newly opened Imformatics Forum building of the University of Edinburgh, Scotland September 3, 2008.