The Lynx Program (Lynx), the longest running active hedge fund in Sweden, has launched a pure machine learning strategy, Lynx Constellation.

The main problem is financial market data, according to Bryan Kelly, head of machine learning at $194 billion AQR Capital Management LLC.

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To appear at NeurIPS 2019. Modular and programmable architecture search framework that allows you to implement your own search spaces and search algorithms through a consistent API. Reading the Twitter thread will give you a pretty good idea of the main ideas. Looking to get a few initial users and feedback.

Trint, the London-based transcription startup founded by Emmy-winning journalist Jeff Kofman, has raised $4.5 million in Series A funding. The round includes follow-on investment from Horizons Lab, the Hong Kong-based seed fund operated by the managers of Horizons Ventures, with participation from TechNexus, and The Associated Press. It brings total funding for Trint to $7.8 million since the company's founding in December 2014. Original backers include Google Digital News Innovation Fund and the Knight Enterprise Fund. Counting some of the world's largest media organizations as customers -- including The Associated Press, Vice News, The Washington Post and Der Spiegel -- Trint uses machine learning and speech-to-text technology to automate transcribing, which is a significant pain-point for journalists and other content producers, such as video makers.

MINE: Mutual Information Neural Estimation: The idea is quite simple - take a function that serves as a lower (or upper) bound for a quantity you want to estimate and use gradient ascent (or descent) to maximize (or minimize) the function. In this case, the function is a lower bound on KL divergence: Donsker-Varadhan representation. I have been thinking that this could become a more generalized approach to estimate values that are hard to evaluate from data. Basically take any inequality that involves the term we want to estimate, parameterize it and apply gradient descent/ascent.