Questions tagged [machine-learning]
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189
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How good is approximation of distance function on the Cayley graph by "Fourier" basis coming from the irreducible representations?
Consider finite group $G$ , symmetric set of its elements $S$, construct a Cayley graph.
Consider $d(g)$ - word metric or distance on the Cayley graph from identity to $g$.
As any function on a group ...
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From a constraint satisfaction problem (CSP) to a sudoku grid [closed]
one of the existing methods of solvin a sudoku grid is via constraints satisfaction (CSP), but can we do the inverse ie convert a CSP problem into a sudoku grid and then solve it ?
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How to choose N policemen positions to catch a drunk driver in the most effective way (on a Cayley graph of a finite group)?
Consider a Cayley graph of some big finite group. Consider random walk on such a graph - think of it as drunk driver. Fix some number $N$ which is much smaller than group size.
Question 1: How to ...
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Should a neural network architecture change after a pass in gradient descent?
I'm trying to understand neural networks formally a little better and it was always my understanding that the miracle that happens during backpropagation is that "performing a pass in gradient ...
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Interpolation in convex hull
I'm reading a paper, Learning in High Dimension Always Amounts to Extrapolation, that provides a result I don't understand.
It provides this theorem which I do understand:
Theorem 1: (Bárány and ...
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Bayes classifiers with cost of misclassification
A minimum ECM classifier disciminate the features $\underline{x}$ to belong to class $t$ ($\delta(\underline{x}) = t$) if $\forall j \ne t$:
$$\sum_{k\ne t} c(t|k) f_k(\underline{x})p_k \le \sum_{k\ne ...
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How to lower bound the absolute value of the difference of two Kullback-Leibler divergences given the constrains below?
Given that $\min (D[p_1||p_3],D[p_2||p_4])=a$, how to find a lower bound of the difference $\left \vert D[p_1\parallel p_2]-D[p_3\parallel p_4] \right\vert$ with respect to $a$? If the condition is ...
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Short path problem on Cayley graphs as language translation task (from "Permutlandski" to "Cayleylandski"(s) :). Reference/suggestion request
Context: Algorithms to find short paths on Cayley graphs of (finite) groups are of some interest - see below.
There can be several approaches to that task. One of ideas coming to my mind - in some ...
- 24.4k
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Difference between deep neural networks and expectation maximization algorithm
Having had a short encounter with deep neural networks, it seems to boil down to the task of determining the values of a vast amount of parameters.
The expectation maximization algorithm, of which I ...
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What are compact manifolds such that GROWTH (of spheres volumes) is well approximated by the Gaussian normal distribution?
Consider some compact Riemannian manifold $M$. Fix some point $p$.
Consider a "sub-sphere of radius $r$" - i.e. set of points on distance $r$ from $p$.
Consider growth function $g(r)$ to be ...
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Does the permutohedron satisfy any minimal distortion property for graph metric vs Euclidean distance?
We can look on the permutohedron as a kind of "embedding" of the Cayley graph of $S_n$ to the Euclidean space. (That Cayley graph is constructed by the standard generators, i.e. ...
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What Cayley graphs arise as nodes+edges from "nice" polytopes and when are these polytopes convex?
The Permutohedron is a remarkable convex polytope in $R^n$, such that its nodes are indexed by permutations and edges correspond to the Cayley graph of $S_n$ with respect to the standard generators, i....
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Continuity of Kernel Mean Embeddings
Given some kernel $k: X \times X \to \mathbb{R}$ with RKHS $H_k$ we say that $k$ is characteristic on the space of signed Radon measures over $X$, denoted by $\mathcal{M}(X)$, if the kernel mean ...
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A network to transform/predict one probability distribution to another
I have a random variable of a particular density (e.g., normal), and a known probability distribution (e.g., mixture Gaussian). I used a simple KL measure to predict/transform one another. Now I need ...
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Some new questions on Rademacher complexity
For $A\subset R^n$,$A=(a_1,a_2,\dots, a_n)$, $\sigma_i$ are Rademacher random variable.
Is $|\mathbb{E}_\sigma \inf_{a\in A}\sum_{i=1}^n\sigma_ia_i| \le |\mathbb{E}_\sigma \sup_{a\in A}\sum_{i=1}^n\...
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