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Tomasz Malisiewicz
2,875 followers -
Computer Vision Entrepreneur
Computer Vision Entrepreneur

2,875 followers
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Some important take-home lessons from NIPS 2016. Quite relevant to those applying machine learning in industry and startups, especially those trying to capitalize on new advancements in deep learning.
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Nuts and Bolts of Building Deep Learning Applications: Ng @ NIPS2016
You might go to a cutting-edge machine learning research conference like NIPS hoping to find some mathematical insight that will help you take your deep learning system's performance to the next level. Unfortunately, as Andrew Ng reiterated to a live crowd ...
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Dropout allows us to convert neural network outputs into probabilities, with no cost to performance, and minimal computational overhead. Bayesian Deep Learning, here we come?
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Making Deep Networks Probabilistic via Test-time Dropout
In Quantum Mechanics, Heisenberg's Uncertainty Principle states that there is a fundamental limit to how well one can measure a particle's position and momentum . In the context of machine learning systems, a similar principle has emerged, but relating inte...
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Deep Learning Trends @ ICLR 2016
Started by the youngest members of the Deep Learning Mafia [1], namely  Yann LeCun and Yoshua Bengio , the ICLR conference is quickly becoming a strong contender for the single most important venue in the Deep Learning space . More intimate than NIPS and le...
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I wrote a lengthy blog post about what's new in the world of SLAM algorithms, together with summaries of the December 2015 Future of Real-Time SLAM workshop. I hope you guys find it educational.

http://www.computervisionblog.com/2016/01/why-slam-matters-future-of-real-time.html

#robotics #computervision #iccv #research

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Anybody have the PDF slides from the BMVC Visual SLAM Tutorial that was given back in 2007?

https://www.cs.bris.ac.uk/Research/Vision/Realtime/bmvctutorial/

I'd like to look at the presentation slides for archival purposes (to basically see how much SLAM changed over the past 8 years), and the links to 2/3 presentations seem to be down. I can find PDFs of part II and part II when I do some serious Googling -- does anybody have part I? +Andrew Davison
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ICCV 2015: Twenty one hottest research papers
"Geometry vs Recognition" becomes ConvNet-for-X Computer Vision used to be cleanly separated into two schools: geometry and recognition . Geometric methods like structure from motion and optical flow usually focus on measuring objective real-world quantitie...
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The Deep Learning Gold Rush of 2015
In the last few decades, we have witnessed major technological innovations such as personal computers and the internet finally reach the mainstream. And with mobile devices and social networks on the rise, we're now more connected than ever. So what's next?...
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