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AI, Python, Cognitive Neuroscience

AI, Python, Cognitive Neuroscience

@ai_python_en

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Showing posts older than #1557 · Back to latest

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Post #1556 373
Camera localization techniques for AR require persistent storage of digital 3D maps. But deep neural networks can reconstruct detailed images of scenes from such maps. Our solution keeps 3D maps confidential while accurately computing camera pose https://aka.ms/AA5bu2n #CVPR2019
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Post #1553 382
Neurons inside BERT when it gets trained on “Hey Jude” over and over.

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Post #1549 504
Here's a demo of image classification with the webcam by using #tensorflowjs, the entire code is being run in the browser!

#machinelearning in the browser is the next big frontier of AI, as the world's 50% population is now online.

#Google's #tensorflowjs makes it easier to train and deploy machine learning/deep learning models in the browser itself. No major installations required, just a browser and internet!

https://lnkd.in/gxZvTJj
#ai #datascience #machinelearners #deeplearning

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Analytics Vidhya Build a Machine Learning Model in your Browser using TensorFlow.js Building a machine learning model in your browser? It's now possible using tensorflow.js (previously deeplearn.js)! Learn how it works in this article.
Post #1546 486
#TapNGhost: Novel Attack Techniques against #Smartphone Touchscreens - essentially by putting you phone down on a hacked table with #NFC you can hijack what the user taps on the screen. Never using my phone on a table again 😅 #hack #exploit #security youtu.be/kmYCXH4ax-g
YouTube Tap 'n Ghost: A Compilation of Novel Attack Techniques against Smartphone Touchscreens Tap 'n Ghost: A Compilation of Novel Attack Techniques against Smartphone Touchscreens Seita Maruyama (Waseda University), Satohiro Wakabayashi (Waseda University), Tatsuya Mori (Waseda University / RIKEN AIP)
Post #1543 504
There is no machine learning for dummies. It is a fact that we have to accept it !
machine learning is an advanced topic that needs knowledge of math, optimization algorithms and programming constraints.

Poeple love to hear stories about AI and how powerful machine learning is. However, they give up as soon as they see the first math equation.

If you want it, work for it ! do not dream for it !

#AI

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Post #1542 572
According to one urban legend, statistics depends on the normal distribution and, since most data aren't normally distributed, statistics is invalid.

This myth is easily busted. Many data, including natural phenomena, are in fact normally distributed. Secondly, the normal (Gaussian) distribution is but one of more than three dozen used in statistics. These two books concisely review the ones used most often by statisticians:

- Handbook of Statistical Distributions (Krishnamoorthy)
- Statistical Distributions (Forbes et al.)

As Bayesian statistics becomes mainstream, a good understanding of probability may be more important than ever. I've been cracking the books and have found these three quite helpful:

- Introduction to Probability (Bertsekas and Tsitsiklis)
- Introduction to Probability Models (Ross)
- Essentials of Probability Theory for Statisticians (Proschan and Shaw)

These three provide a somewhat philosophical take on probability:

- Probability, Statistics and Truth (von Mises)
- Probability Theory: The Logic of Science (Jaynes)
- Uncertainty: The Soul of Modeling, Probability & Statistics (Briggs)

Lastly, I'd recommend The Improbability Principle by David Hand to anyone.

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Post #1539 492
Interesting NBER paper on the history of industry investment in basic research. "The Changing Structure of American Innovation: Some Cautionary Remarks for Economic Growth", Ashish Arora, Sharon Belenzon, Andrea Patacconi, Jungkyu Suh

https://www.nber.org/papers/w25893

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Post #1538 797
Is it a good idea to train RL policies from raw pixels? Could visual priors about the world help RL? We just released the code of our Mid-Level Vision paper addressing these questions. Spoiler: using raw pixels doesn’t generalize! Play with the results at http://perceptual.actor

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Post #1537 418
Structured prediction requires substantial training data. new paper introduces the first few-shot scene graph model with predicates as functions within a graph convolution framework, resulting in the first semantically & spatially interpretable model.
https://arxiv.org/pdf/1906.04876.pdf

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