The basic problem? Given a collection of points, curves, or surfaces in space, find a well-distributed arrangement that avoids (self-)intersection.
To make things interesting, you also usually have some constraints—like: the geometry is contained inside a bunny!
(3/14)
Why would you want to do this?
Well, *point* repulsion is already used in every major area of computer graphics: from image stippling to mesh generation to density estimation to fluid simulation!
But surprisingly little work has been done on repulsive curves & surfaces… (4/14)
And yet there are *all sorts* of things you can do with higher-dimensional repulsion—from better graph layouts, to generative modeling, to robotic path planning, to intersection-free modeling & illustration, to artificial tissue design, to unraveling mathematical mysteries!(5/14)
Repulsion goes far beyond (literally) traditional collision-aware design & modeling: rather than slamming on the brakes right before the moment of impact, repulsive optimization is all about finding a harmonious *global* balance of forces, long before collisions occur. (6/14)
Imagine, for instance, playing a game of pool with charged particles rather than rigid bodies.
In contrast to collision, where you can aggressively prune away distant barrier functions, the whole point of repulsive optimization is to get long-range forces into equilibrium (7/14)
Beyond just dealing with O(n²) interactions, repulsive energies lead to some rich & interesting challenges. For one thing, repulsive energies for points don't nicely generalize to curves & surfaces. For another, you have to deal w/ optimization of "fractional derivatives." (8/14)
…Rather than regurgitate everything here on Twitter, I'll point again to this talk:
Since I know it's easy to get lost in the math & switch off the video, I gave each section a level of difficulty. Don't feel bad about skipping to the fun stuff! (9/14)
You might also be interested in checking out this longer Twitter thread, which also goes deeper into the details for curves:
But the generalization to repulsive surfaces lets us do some really beautiful things like never before. (10/14)
To end with just one cool example, consider this pair of handcuffs in a "linked" and "unlinked" configuration.
Do you think it's possible to unlink the handcuffs, without breaking them apart or letting them pass through each other? (11/14)
If you said "yes," you were right (hey, you had a 50% chance!)
But can you show me how?
There are lots of beautiful drawings of this counter-intuitive transition—but unless you have a good geometric imagination, they can be pretty hard to follow. (12/14).
However, we can use repulsive optimization to make this remarkable topological phenomenon come to life.
Just minimize tangent-point energy starting in both linked and unlinked configurations, then join the two movies together (the 2nd one playing backwards). Voilà! (13/14)
The paper/videos have lots of other examples of new & different things you can do with repulsive shape optimization.
But the real limitation is *our* own creativity!
I'd love to hear what ideas this sparks for you, and what problems, challenges, & creations it leads to…(14/14)
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“Everyone knows” what an autoencoder is… but there's an important complementary picture missing from most introductory material.
In short: we emphasize how autoencoders are implemented—but not always what they represent (and some of the implications of that representation).🧵
A similar thing happens when (many) people learn linear algebra:
They confuse the representation (matrices) with the objects represented by those matrices (linear maps… or is it a quadratic form?)
With autoencoders, the first (and last) picture we see often looks like this one: a network architecture diagram, where inputs get “compressed”, then decoded.
If we're lucky, someone bothers to draw arrows that illustrate the main point: we want the output to look like the input!
What if instead of two 6-sided dice, you could roll a single "funky-shaped" die that gives the same statistics (e.g, 7 is twice as likely as 4 or 10).
Or make fair dice in any shape—e.g., dragons rather than cubes?
That's exactly what we do! 1/n
Here's the paper, which is an industry-funded collaboration between my PhD student Hossein Baktash at @SCSatCMU, @nmwsharp at @nvidia, and Qingnan Zhou & @_AlecJacobson at @AdobeResearch.
The sum of the squares of several positive values can never be bigger than the square of their sum.
This picture helps make sense of how ℓ₁ and ℓ₂ norms regularize and sparsify solutions (resp.). [1/n]
These pictures are often batting around in my brain when I think about optimization/learning problems, but can take some time to communicate to students, etc. So, I thought I'd make some visualizations. [2/n]
Suppose we minimize the squared length of a vector x, equal to the sum of squares of its components.
To avoid the trivial solution x=0, we'll also require that the components sum to a nonzero value.
Equivalently: minimize the ℓ₂ norm ‖x‖₂, subject to ‖x‖₁=1. [3/n]
We often use discretization to approximate continuous laws of physics, but it also goes the other way:
You can use continuous equations to approximate the behavior of discrete systems!
Here we'll see how electrical circuits can be modeled using the Laplace equation Δφ=0. [1/n]
The Laplacian Δ is central to numerous (continuous) physical equations like the heat equation, the wave equation, and so on.
I have a whole video about it here: [2/n]
The discrete or graph Laplacian L is typically viewed as a numerical approximation of Δ, giving the difference between the value ui at a node of a graph, and a weighted average of uj at all neighbors j:
Entropy is one of those formulas that many of us learn, swallow whole, and even use regularly without really understanding.
(E.g., where does that “log” come from? Are there other possible formulas?)
Yet there's an intuitive & almost inevitable way to arrive at this expression.
When I first heard about entropy, there was a lot of stuff about "free states" and "disorder." Or about the number of bits needed to communicate a message.
These are ultimately important connections—but it's not clear it's the best starting point for the formula itself.
A better starting point is the idea of "surprise."
In particular, suppose an event occurs with probability p. E.g., if the bus shows up on time about 15% of the time, p = 0.15.
How *surprising*, then, is an event with probability p? Let's call this quantity S(p).