aidanrocke

5 ms·

1. 1.▲
   The emergence of money as a topological phase transition(keplerlounge.com)

   1 points by aidanrocke 6y ago|0 comments
2. 2.▲
   The animal spirits behind the communist principle of anti-semitism(keplerlounge.com)

   1 points by aidanrocke 6y ago|0 comments
3. 3.▲
   Von Neumann's reflections on the American education system(youtube.com)

   1 points by aidanrocke 6y ago|0 comments
4. 4.▲
   The Generalised Uncertainty Principle(keplerlounge.com)

   1 points by aidanrocke 6y ago|0 comments
5. 5.▲
   Fizeau apparatus for measuring the speed of light(keplerlounge.com)

   1 points by aidanrocke 6y ago|0 comments
6. 6.▲
   Visualisation meditation differs from wishful thinking(keplerlounge.com)

   1 points by aidanrocke 6y ago|0 comments
7. 7.▲
   Schmidhuber's Comment on Laughter(keplerlounge.com)

   2 points by aidanrocke 6y ago|0 comments
8. 8.▲
   by aidanrocke 6y ago

   Whenever the discussion of liberty versus equality of outcomes arises there is a tendency to create a false dichotomy where we are forced to choose between extreme positions that are mutually exclusive. However, this is a mistake as the bes
9. 9.▲
   The choice between freedom and equality(keplerlounge.com)

   1 points by aidanrocke 6y ago|1 comments
10. 10.▲
    by aidanrocke 6y ago

    From the abstract: 'The brain carries out tasks that are very demanding from a computational perspective, apparently powered by a mere 20 Watts.This fact has intrigued computer scientists for many decades, and is currently drawing many
11. 11.▲
    Brain Computation: A Computer Science Perspective [pdf](igi-web.tugraz.at)

    1 points by aidanrocke 6y ago|1 comments
12. 12.▲
    by aidanrocke 7y ago

    For more information, check this brilliant thread by Michelle Kendall: https://twitter.com/mishkendall/status/1239946522390867969
13. 13.▲
    Covid-19_instant_tracing(github.com)

    2 points by aidanrocke 7y ago|1 comments
14. 14.▲
    by aidanrocke 7y ago

    This article uses a Julia implementation to explain how the complex-step method allows us to easily compute derivatives of analytic functions up to machine-precision. Possibly the best short blog post on this subject on the internet.
15. 15.▲
    Complex-Step Differentiation(johnlapeyre.com)

    3 points by aidanrocke 7y ago|1 comments
16. 16.▲
    by aidanrocke 7y ago

    tl;dr 1. The objective of this paper is to show how the complex-step derivative approximation is related to algorithmic differentiation. 2. For a tutorial on complex-step differentiation, I can recommend the blog post of John Lapeyre: http
17. 17.▲
    Complex-step differentiation and automatic differentiation (2001) [pdf](pdfs.semanticscholar.org)

    56 points by aidanrocke 7y ago|6 comments
18. 18.▲
    by aidanrocke 7y ago

    tl;dr 1. Within the context of optimisation, differentiable approximations of the min and max operators on R^n are very useful. 2. However, in order for these approximations to be useful they must also be numerically stable.
19. 19.▲
    Differentiable approximations to the min and max operators(github.com)

    3 points by aidanrocke 7y ago|2 comments
20. 20.▲
    by aidanrocke 7y ago

    tl;dr 1. Here I present an elementary proof for a classical result in random matrix theory that applies to any random matrix sampled from a continuous distribution. One of its many important consequences is that almost all linear models wit
21. 21.▲
    Almost all random matrices are nonsingular(keplerlounge.com)

    2 points by aidanrocke 7y ago|1 comments
22. 22.▲
    by aidanrocke 7y ago

    tl;dr 1. The typical deep neural network tutorial introduces deep networks as compositions of nonlinearities and affine transforms. 2. In fact, a deep network with relu activation simplifies to a linear combination of affine transformations
23. 23.▲
    Are partial derivatives the computational primitives of deep neural networks?(keplerlounge.com)

    3 points by aidanrocke 7y ago|1 comments
24. 24.▲
    Automatic Differentiation via Contour Integration(keplerlounge.com)

    94 points by aidanrocke 7y ago|20 comments
25. 25.▲
    Automatic Differentiation via Contour Integration(keplerlounge.com)

    2 points by aidanrocke 7y ago|0 comments
26. 26.▲
    Scaling Laws for Dendritic Computation(keplerlounge.com)

    1 points by aidanrocke 7y ago|0 comments
27. 27.▲
    Neuroscientists can help address climate change(keplerlounge.com)

    2 points by aidanrocke 7y ago|0 comments
28. 28.▲
    A comparison of credit-assignment models in mathematics and biology, Part I(keplerlounge.com)

    1 points by aidanrocke 7y ago|0 comments
29. 29.▲
    by aidanrocke 7y ago

    For the kind of explosiveness that man will be able to contrive by 1980, the globe is dangerously small, its political units dangerously unstable.-John von Neumann
30. 30.▲
    Can we survive technology? (1955) [pdf](geosci.uchicago.edu)

    80 points by aidanrocke 7y ago|48 comments