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Found 9 papers, 3 papers with code
Laplace-HDC: Understanding the geometry of binary hyperdimensional computing
This paper studies the geometry of binary hyperdimensional computing (HDC), a computational scheme in which data are encoded using high-dimensional binary vectors.
Moment-based metrics for molecules computable from cryo-EM images
Further, we introduce a metric between a stack of projection images and a molecular structure, which is invariant to rotations and reflections and does not require performing 3-D reconstruction.
Randomized Kaczmarz with geometrically smoothed momentum
This paper studies the effect of adding geometrically smoothed momentum to the randomized Kaczmarz algorithm, which is an instance of stochastic gradient descent on a linear least squares loss function.
Fast expansion into harmonics on the disk: a steerable basis with fast radial convolutions
We present a fast and numerically accurate method for expanding digitized $L \times L$ images representing functions on $[-1, 1]^2$ supported on the disk $\{x \in \mathbb{R}^2 : |x|<1\}$ in the harmonics (Dirichlet Laplacian eigenfunctions) on the disk.
An optimal scheduled learning rate for a randomized Kaczmarz algorithm
We study how the learning rate affects the performance of a relaxed randomized Kaczmarz algorithm for solving $A x \approx b + \varepsilon$, where $A x =b$ is a consistent linear system and $\varepsilon$ has independent mean zero random entries.
A common variable minimax theorem for graphs
Let $\mathcal{G} = \{G_1 = (V, E_1), \dots, G_m = (V, E_m)\}$ be a collection of $m$ graphs defined on a common set of vertices $V$ but with different edge sets $E_1, \dots, E_m$.
Multi-target detection with rotations
We demonstrate that, regardless of the level of noise, our technique can be used to recover the target image when the measurement is sufficiently large.
Image recovery from rotational and translational invariants
We introduce a framework for recovering an image from its rotationally and translationally invariant features based on autocorrelation analysis.
Manifold learning with bi-stochastic kernels
In this paper we answer the following question: what is the infinitesimal generator of the diffusion process defined by a kernel that is normalized such that it is bi-stochastic with respect to a specified measure?
