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I posted the same thing on 23/02/2016, with arxiv version of the paper instead.

uhhhh, Jai got scooped on Diigo :D

since double posted, maybe double interesting and we should have a closer look? anybody?

Got $129,000?

How to cram in all buzzword technologies in one title. I will give it all tags we have.

Really nice visualizations for rectifier (ReLU) neural nets, illustrating the effects of skip-connections, depth, width, etc. on the loss function curvature.

Is this the future of blockchain?

Just out - I wish I could understand this :(

ignorance is a bliss :)

Finish him!

There we go: Deep Learning predicts disruptions in plasmas. The paper related to this article is here: https://arxiv.org/abs/1802.02242

Nice easy to read article

Cool stuff! This guy is also interesting: http://cogsci.uci.edu/~ddhoff/HoffmanTime.pdf. Saw him in a conference one, blew my mind :O

"Over the past couple of decades, the research literature has filled up with endless new nature-based metaphors for algorithms. You can find algorithms based on the behaviour of cuckoos, bees, bats, cats, wolves, galaxy formation and black holes. (...) All researchers have been doing is wasting time on developing new approaches that are probably little better than existing ones. And the language of each metaphor then invades the literature, distracting people from using the already sufficiently expressive terminology of mathematics and, above all, working together to find the best way forward." The golden era of fireworks-like algorithm is about to end

Lies, lies, all lies. They will never go away. Papers need to be published.

Scientific use cases show promise, but challenges remain for complex data analytics.

And we get another step closer to neuromorphic computing

Prediction-based method for encouraging reinforcement learning agents to explore their environments through curiosity (reward for unfamiliar states). Learns some games without any extrinsic reward!

Fun failure case: agent gets stuck in front of TV.

Not read this article but on a related note: Curiosity and various metrics for it have been explored for some time in robotics (outside of RL) as a framework for exploring (partially) unfamiliar environments. I came across some papers on this topic applied to UAVs when prep'ing for a PhD app. This one (http://www.cim.mcgill.ca/~yogesh/publications/crv2014.pdf) comes to mind - which used a topic modelling approach.

I tried reading the abstract and my eyes glazed over at the buzzword density. Is this hot doo doo or a meaningful result?

wow, quantum, artificial life, biomimetic, quantum supremacy .... quantum machine learning, and quantum artificial intelligence and, wait for it ...... quantum complexity. All in one abstract is this the new champion?

The first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving

"We construct a novel large-scale, multi-modal dataset by pairing geo-referenced Wikipedia articles with satellite imagery of their corresponding locations."

Researchers have used machine learning to predict the chaotic evolution of a model flame front.

This has attracted a lot of attention in the DL community lately. Article: https://lilianweng.github.io/lil-log/2017/09/28/anatomize-deep-learning-with-information-theory.html Paper: https://arxiv.org/pdf/1703.00810.pdf

Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. For paper see: https://arxiv.org/pdf/1712.06567.pdf

Interesting pointers in this one! I would like to explore neuroevolution as well, although it seems extremely resource-demanding?

Not necessarily, I think it can be made to be much faster hybridizing it with backprop and Taylor maps. Its one ideas in the closet we still have not explored (Differential Intelligence: accelerating neuroevolution).