Tootfinder

Walking on Spheres and Talking to Neighbors: Variance Reduction for Laplace's Equation
Michael Czekanski, Benjamin Faber, Margaret Fairborn, Adelle Wright, David Bindel
https://arxiv.org/abs/2404.17692

Walking on Spheres and Talking to Neighbors: Variance Reduction for Laplace's Equation
Walk on Spheres algorithms leverage properties of Brownian Motion to create Monte Carlo estimates of solutions to a class of elliptic partial differential equations. Until recently, estimates were constructed pointwise and did not utilize the relationship between solutions at nearby points within a domain. We propose a new caching strategy which leverages the continuity of paths of Brownian Motion. In the case of Laplace's equation with Dirichlet boundary conditions, our novel algorithm has imp…

This https://arxiv.org/abs/2305.01309 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Geometric Prior Based Deep Human Point Cloud Geometry Compression
The emergence of digital avatars has raised an exponential increase in the demand for human point clouds with realistic and intricate details. The compression of such data becomes challenging with overwhelming data amounts comprising millions of points. Herein, we leverage the human geometric prior in geometry redundancy removal of point clouds, greatly promoting the compression performance. More specifically, the prior provides topological constraints as geometry initialization, allowing adapt…

New Douglas-Rashford Splitting Algorithms for Generalized DC Programming with Applications in Machine Learning
Yonghong Yao, Lateef O. Jolaoso, Yekini Shehu, Jen-Chih Yao
https://arxiv.org/abs/2404.14800

New Douglas-Rashford Splitting Algorithms for Generalized DC Programming with Applications in Machine Learning
In this work, we propose some new Douglas-Rashford splitting algorithms for solving a class of generalized DC (difference of convex functions) in real Hilbert spaces. The proposed methods leverage the proximal properties of the nonsmooth component and a fasten control parameter which improves the convergence rate of the algorithms. We prove the convergence of these methods to the critical points of nonconvex optimization under reasonable conditions. We evaluate the performance and effectiveness…

Slax: A Composable JAX Library for Rapid and Flexible Prototyping of Spiking Neural Networks
Thomas M. Summe, Siddharth Joshi
https://arxiv.org/abs/2404.05807

Slax: A Composable JAX Library for Rapid and Flexible Prototyping of Spiking Neural Networks
Recent advances to algorithms for training spiking neural networks (SNNs) often leverage their unique dynamics. While backpropagation through time (BPTT) with surrogate gradients dominate the field, a rich landscape of alternatives can situate algorithms across various points in the performance, bio-plausibility, and complexity landscape. Evaluating and comparing algorithms is currently a cumbersome and error-prone process, requiring them to be repeatedly re-implemented. We introduce Slax, a JA…

This https://arxiv.org/abs/2302.03135 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Monotone Function Intervals: Theory and Applications
A monotone function interval is the set of monotone functions that lie pointwise between two fixed monotone functions. We characterize the set of extreme points of monotone function intervals and apply this to a number of economic settings. First, we leverage the main result to characterize the set of distributions of posterior quantiles that can be induced by a signal, with applications to political economy, Bayesian persuasion, and the psychology of judgment. Second, we combine our characteri…