Group items tagged

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?

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

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).

Some work by DeepMind on applying reinforcement learning to teach a computer to navigate complex environments. Come for the science - stay for the video: https://goo.gl/8rTx2F

nice, did any of you try it already

If it can be done for obesity, I guess noise is also an option right? :)

love it

Fascinating insight into the question of how networks learn.

Related paper at: http://journals.aps.org/prl/pdf/10.1103/PhysRevLett.118.080501

A college course at University of Washington on "Calling Bullshit". We should invite them to give a lunch lecture at ESA... Our aim in this course is to teach you how to think critically about the data and models that constitute evidence in the social and natural sciences. While bullshit may reach its apogee in the political domain, this is not a course on political bullshit. Instead, we will focus on bullshit that comes clad in the trappings of scholarly discourse. Our learning objectives are straightforward. After taking the course, you should be able to: * Remain vigilant for bullshit contaminating your information diet. * Recognize said bullshit whenever and wherever you encounter it. * Figure out for yourself precisely why a particular bit of bullshit is bullshit. * Provide a statistician or fellow scientist with a technical explanation of why a claim is bullshit. * Provide your crystals-and-homeopathy aunt or casually racist uncle with an accessible and persuasive explanation of why a claim is bullshit. We will be astonished if these skills do not turn out to be among the most useful and most broadly applicable of those that you acquire during the course of your college education.

love it: "Politicians are unconstrained by facts. Science is conducted by press release. Higher education rewards bullshit over analytic thought. Startup culture elevates bullshit to high art. Advertisers wink conspiratorially and invite us to join them in seeing through all the bullshit - and take advantage of our lowered guard to bombard us with bullshit of the second order. The majority of administrative activity, whether in private business or the public sphere, seems to be little more than a sophisticated exercise in the combinatorial reassembly of bullshit. We're sick of it. It's time to do something, and as educators, one constructive thing we know how to do is to teach people. So, the aim of this course is to help students navigate the bullshit-rich modern environment by identifying bullshit, seeing through it, and combating it with effective analysis and argument."

"To create Sketch-RNN, Google Brain researchers David Ha and Douglas Eck collected more than five million user-drawn sketches from the Google tool Quick, Draw! Each time a user drew something on the app, it recorded not only the final image, but also the order and direction of every pen stroke used to make it. The resulting data gives a more complete picture (ho, ho, ho) of how we really draw." It's funny because this David Ha used to be a quant banker ha ha

"Hinton's new approach, known as capsule networks, is a twist on neural networks intended to make machines better able to understand the world through images or video. In one of the papers posted last week, Hinton's capsule networks matched the accuracy of the best previous techniques on a standard test of how well software can learn to recognize handwritten digits." Links to papers: https://arxiv.org/abs/1710.09829 https://openreview.net/forum?id=HJWLfGWRb&noteId=HJWLfGWRb

impressive!

seems a very impressive guy :"Hinton formed his intuition that vision systems need such an inbuilt sense of geometry in 1979, when he was trying to figure out how humans use mental imagery. He first laid out a preliminary design for capsule networks in 2011. The fuller picture released last week was long anticipated by researchers in the field. "Everyone has been waiting for it and looking for the next great leap from Geoff," says Kyunghyun Cho, a professor"

This is a social sciences paper trying to make use of ML. Quote from text: "Social scientists studying the life course must find a way to reconcile a widespread belief that understanding has been generated by these data-as demonstrated by more than 750 published journal articles using the Fragile Families data (10)-with the fact that the very same data could not yield accurate predictions of these important outcomes." "(...) In other words, the submissions were much better at predicting each other than at predicting the truth."

an important message to learn from

In the paper authors Daniel Hennes (former RF in AI here at the ACT) ....

A somewhat eclectic paper about events collected with an event camera onboard the ISS, courtesy of Western Sydney University and...the US AirForce Academy.