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Maximum Entropy Models from Phase Harmonic Covariances

arXiv.org Machine Learning

Maximum Entropy Models from Phase Harmonic Covariances Sixin Zhang 1, 4, St ephane Mallat 1, 2,3 1 ENS, PSL University, Paris, France 2 Coll ege de France, Paris, France 3 Flatiron Institute, New York, USA 4 Center for Data Science, Peking University, Beijing, China November 25, 2019 Abstract We define maximum entropy models of non-Gaussian stationary random vectors from covariances of nonlinear representations. These representations are calculated by multiplying the phase of Fourier or wavelet coefficients with harmonic integers, which amounts to compute a windowed Fourier transform along their phase. Rectifiers in neural networks compute such phase windowing. The covariance of these harmonic coefficients capture dependencies of Fourier and wavelet coefficients across frequencies, by canceling their random phase. We introduce maximum entropy models conditioned by such covariances over a graph of local interactions. These models are approximated by transporting an initial maximum ...


Machine Learning on 50 Million Smart Meters: Utility Powerhouse Extends C3 Platform Europe-wide

#artificialintelligence

In enterprise AI, C3 (formerly C3 IoT) is amassing an impressive and seemingly unmatched record, one that the company has extended with its latest win, the expansion of a five-year engagement with Enel, Europe's largest power utility, to encompass nearly 50 million smart meters in homes and businesses. This follows C3 contract wins last year with Royal Dutch Shell, the U.S. Air Force and 3M, along with partnerships with AWS, Google Cloud and Microsoft Azure. In the large utilities space, other customers include Con Edison, covering the New York metropolitan area, and Engie, one of the biggest utilities in France. The new contract (dollar amount not disclosed) expands on C3's existing, five-year engagement for Enel in Italy involving 32 million smart meters. C3 will provide the €74.6 billion utility with AI and smart grid analytics applications enabling Enel to deploy the Unified Virtual Data Lake, integrating data across its retail, distribution, trading, renewables and conventional generation businesses.


Synechron launches AI data science accelerators for FS firms

#artificialintelligence

These four new solution accelerators help financial services and insurance firms solve complex business challenges by discovering meaningful relationships between events that impact one another (correlation) and cause a future event to happen (causation). Following the success of Synechron's AI Automation Program – Neo, Synechron's AI Data Science experts have developed a powerful set of accelerators that allow financial firms to address business challenges related to investment research generation, predicting the next best action to take with a wealth management client, high-priority customer complaints, and better predicting credit risk related to mortgage lending. The Accelerators combine Natural Language Processing (NLP), Deep Learning algorithms and Data Science to solve the complex business challenges and rely on a powerful Spark and Hadoop platform to ingest and run correlations across massive amounts of data to test hypotheses and predict future outcomes. The Data Science Accelerators are the fifth Accelerator program Synechron has launched in the last two years through its Financial Innovation Labs (FinLabs), which are operating in 11 key global financial markets across North America, Europe, Middle East and APAC; including: New York, Charlotte, Fort Lauderdale, London, Paris, Amsterdam, Serbia, Dubai, Pune, Bangalore and Hyderabad. With this, Synechron's Global Accelerator programs now includes over 50 Accelerators for: Blockchain, AI Automation, InsurTech, RegTech, and AI Data Science and a dedicated team of over 300 employees globally.


Synechron Launches AI Data Science Accelerators for the BFSI sector

#artificialintelligence

Synechron the global financial services consulting and technology services provider, has announced the launch of its AI Data Science Accelerators for Financial Services, Banking and Insurance (BFSI) firms. These four new solution accelerators help financial services and insurance firms solve complex business challenges by discovering meaningful relationships between events that impact one another (correlation) and cause a future event to happen (causation). Following the success of Synechron's AI Automation Program – Neo, Synechron's AI Data Science experts have developed a powerful set of accelerators that allow financial firms to address business challenges related to investment research generation, predicting the next best action to take with a wealth management client, high-priority customer complaints, and better predicting credit risk related to mortgage lending. The Accelerators combine Natural Language Processing (NLP), Deep Learning algorithms and Data Science to solve the complex business challenges and rely on a powerful Spark and Hadoop platform to ingest and run correlations across massive amounts of data to test hypotheses and predict future outcomes. The Data Science Accelerators are the fifth Accelerator program Synechron has launched in the last two years through its Financial Innovation Labs (FinLabs), which are operating in 11 key global financial markets across North America, Europe, Middle East and APAC; including: New York, Charlotte, Fort Lauderdale, London, Paris, Amsterdam, Serbia, Dubai, Pune, Bangalore and Hyderabad.


Making sense of cancer's 'big data' problem to revolutionise patient care

#artificialintelligence

The Mark Foundation Institute for Integrated Cancer Medicine will be funded by an £8.6 million award to the University of Cambridge from The Mark Foundation for Cancer Research – the first time that the New York-based philanthropic organisation has made an award to a UK institution. The virtual institute aims to exploit recent advances in big data processing and machine learning to capture and integrate clinical, genomic, and image data collated from hundreds of cancer patients in real-time. Laboratory and clinic-based researchers and data experts will work together to determine whether sophisticated computational integration of all these diverse data types into a single platform can inform and predict the best treatment decisions for each individual patient. Blood tests, biopsies, medical imaging, and genetic tests are a routine part of current cancer care; however, it is not always clear which of these increasingly large datasets are most important in guiding treatment at specific points in the patient journey. "Doctors have long dreamed of an objective system that can integrate all the results generated from their cancer patients, guiding comprehensive treatment decisions both for current treatment and to predict how a particular disease will behave in the future," explains Professor Richard Gilbertson, Director of the Cancer Research UK Cambridge Centre where the new institute will be based.


Integrating Environmental Data, Citizen Science and Personalized Predictive Modeling to Support Public Health in Cities: The PULSE WebGIS

AAAI Conferences

The percentage of the world’s population living in urban areas is projected to increase significantly in the next decades. This makes the urban environment the perfect bench for research aiming to manage and respond to dramatic demographic and epidemiological transitions. In this context the PULSE project has partnered with five global cities to transform public health from a reactive to a predictive system focused on both risk and resilience. PULSE aims at producing an integrated data ecosystem based on continuous large-scale collection of information available within the smart city environment. The integration of environmental data, citizen science and location-specific predictive modeling of disease onset allows for richer analytics that promote informed, data-driven health policy decisions. In this paper we describe the PULSE ecosystem, with a special focus on its WebGIS component and its prototype version based on New York city data.


Top Data Sources for Journalists in 2018 (350 Sources)

@machinelearnbot

There are many different types of sites that provide a wealth of free, freemium and paid data that can help audience developers and journalists with their reporting and storytelling efforts, The team at State of Digital Publishing would like to acknowledge these, as derived from manual searches and recognition from our existing audience. Kaggle's a site that allows users to discover machine learning while writing and sharing cloud-based code. Relying primarily on the enthusiasm of its sizable community, the site hosts dataset competitions for cash prizes and as a result it has massive amounts of data compiled into it. Whether you're looking for historical data from the New York Stock Exchange, an overview of candy production trends in the US, or cutting edge code, this site is chockful of information. It's impossible to be on the Internet for long without running into a Wikipedia article.


What is hardcore data science – in practice?

@machinelearnbot

To explore emerging topics and new areas of study made possible by vast troves of raw data and cutting-edge architectures, check out the Data Science and Machine Learning sessions at Strata Data Conference, September 25-28, 2017, in New York City. Use code KDNU to get an additional 20% off Best price (ends August 11). Data science has become widely accepted across a broad range of industries in the past few years. Originally more of a research topic, data science has early roots in scientists' efforts to understand human intelligence and create artificial intelligence; it has since proven that it can add real business value. As an example, we can look at the company I work for: Zalando, one of Europe's biggest fashion retailers, where data science is heavily used to provide data-driven recommendations, among other things.


Upcoming Meetings in Analytics, Big Data, Data Science, Machine Learning: June and Beyond

@machinelearnbot

Here are 110 upcoming meetings and conferences, for June 2017 and beyond. You can also find the latest list on KDnuggets Meetings page Color code: Business-Oriented meetings in Blue, Research meetings (with calls for papers and program committee) in green Top countries: India, France, Australia: 3 For the second month in a row, London is the top city: Washington DC, New Orleans, Houston, Chicago, Atlanta: 3 June 2017 Jun 1-2, Deep Learning in Finance Summit. Mention "KDNuggets" and save 18% on tickets. Use code KDNUGGETS to save 15%. Use code TEC6245KD to save.


Fast Track Your Data – Live from Munich - 22 June 2017 - IBM Analytics

#artificialintelligence

If you could have any superpower, which would it be? For Hilary Mason, it's not a question of if, because she already has one: She uses technology to unearth hidden insights, from how to catch people's attention online, to where you can find the best burgers in New York City. She has been at the forefront of machine learning, AI, and analytics. Hilary will discuss how your business can use these tools to move from insight to action to competitive advantage faster than a speeding bullet. She is the co-founder of Fast Forward Labs, Data Scientist in Residence at Accel and is the former Chief Scientist at Bitly.