FAIRFAX, Va. – General Dynamics Mission Systems acquired Deep Learning Analytics, LLC, on March 1, 2019. The 5-year-old company provides proven capabilities to harness massive data sets and make advances in computing hardware and algorithms to aid in superior prediction, threat detection, decision-making, and complicated problem solving for customers.
“Deep Learning Analytics brings to General Dynamics an extensive expertise in artificial intelligence and specializes in deploying deep learning algorithms on small, power-efficient appliances and mobile devices,” said Chris Brady, president of General Dynamics Mission Systems. “Through data science, research, machine learning, predictive analytics and software engineering, our government and commercial customers can better exploit their data—a tremendous value to their missions.”
Deep Learning Analytics has pushed the state of the art in machine learning forward and excelled on projects sponsored by the Defense Advanced Research Projects...
In 2018, the iNaturalist organization, with Microsoft and Google as sponsors at CVPR, hosted an international machine learning challenge to benchmark the state of the art in species identification from a photo of a living thing. Deep Learning Analytics finished 2nd, globally, behind only one larger team from China’s Dalian University of Technology, which produced a 1.1% better Top 3 performance (12.9%) than the Deep Learning Analytics entry (14.0%). Data scientists from Baidu (an international powerhouse in machine learning competitive with Google on many AI tasks) ranked 3rd in the competition. In total, 59 teams from all over the world entered.
iNaturalist 2018 competition organizers aimed to benchmark the state of the art performance for the difficult problem of species identification from a photo. The iNaturalist 2018 challenge lasted for three months. Entrants were ranked according to their Top 3 error, i.e. the percentage of an entrant’s ranked top three guesses of 8,142 spec...
Priyanka Oberoi of Deep Learning Analytics has been recognized as one of the best Data Scientists in the Washington, DC region by DCFemTech. The 2018 DCFemTech Awards recognize powerful women programmers, designers, and data scientists based in the Washington, DC region. Nominated by their community, these women are working in the trenches of tech to help their company or organization achieve success, sometimes entirely behind the scenes. The awards reception will be held May 7th at the Washington Post.
In 2017, DARPA's Information Innovation Office (I2O) incorporated Deep Learning Analytics' image manifold work into their "A DARPA Perspective on Artificial Intelligence."The video notes here (approved for public release) outline the process and illustrate how simple tSNE embeddings can capture image manifold properties (like look angle loops) in embeddings. If missing data gaps on data manifolds are easier to identify in lower dimensional embeddings, it may also inform research on deep learning methods to improve inference performance in domains with limited training data.
For the third year in a row in 2017, Deep Learning Analytics was named the fastest growing company in Arlington, Virginia. In 2015, Deep Learning Analytics won for revenue up to $500,000, in 2016, for revenue between $500,000 and $1,500,000, and in 2017, for revenue between $1,500,000 and $5,000,000. Deep Learning Analytics is a twelve person startup (six women and six men).
Arlington Economic Development assessed entrants based on CAGR. "Participating companies were evaluated in four revenue categories after an online application and were required to provide income statements to show proof of growth and revenue. To be eligible, companies had to be privately held and based in Arlington, showing continuous revenue growth over a three-year period."
Hear how deep learning is taking AI to the next level on Jan. 31, 2018.
Join NVTC, In-Q-Tel and your technology business colleagues on January 31, 2018 for a Titans event on the promises and pitfalls of deep learning, featuring Melvin Greer, chief data officer at Intel, and John Kaufhold, CEO of Deep Learning Analytics. Panelists will discuss how deep learning is harnessing massive datasets and advances in computing hardware and algorithms to solve today's complicated challenges in defense, healthcare, infrastructure, the environment, and more.
Some of the cutting-edge innovations being powered by deep learning today include autonomous vehicles, precision medicine, and facial and speech recognition. The discussion will be moderated by Ravi Pappu, Chief Architect of In-Q-Tel.
On May 9th, 2017, at O'Reilly's OSCON, Aaron Schumacher takes a building-block approach to exploring the tools TensorFlow provides so you can build the systems you need and write your own TensorFlow—not just run other people's scripts. Aaron discusses the many aspects of TensorFlow—including data management, machine learning, distribution, and serving—by comparing them with similar functionality in other toolkits.
Jenn Sleeman of Deep Learning Analytics has been recognized as one of the best Data Scientists in the Washington, DC region by DCFemTech. The 2017 DCFemTech Awards recognize powerful women programmers, designers, and data scientists based in the Washington, DC region. Nominated by their community, these women are working in the trenches of tech to help their company or organization achieve success, sometimes entirely behind the scenes. The awards reception will be held May 18th at the Washington Post.
DCFemTech is a coalition of women leaders aimed at amplifying women in tech organizations, sharing resources, and bringing leaders together to close the gender gap.
Meet the team at Deep Learning Analytics, a collaborative tech company doing innovative work in deep learning and artificial intelligence. When they're not at a client site, you can find them working at the Rosslyn campus of Eastern Foundry. We're glad to have them in the neighborhood! Read the full article.
Jennifer Sleeman of Deep Learning Analytics spoke at George Mason University's Founder's Hall on February 1, 2017, introducing Generative Adversarial Networks (GANs), a deep learning generative model. To date, GANs have primarily been used to generate realistic samples of images but other recent uses have included generating images from captions and video. The talk introduced the theory of generative models and GANs in particular and showed how GANs are being used today. The talk provided a basic introduction to generative models and Generative Adversarial Networks with a short example in keras.