Tootfinder

MegaParticles: Range-based 6-DoF Monte Carlo Localization with GPU-Accelerated Stein Particle Filter
Kenji Koide, Shuji Oishi, Masashi Yokozuka, Atsuhiko Banno
https://arxiv.org/abs/2404.16370

MegaParticles: Range-based 6-DoF Monte Carlo Localization with GPU-Accelerated Stein Particle Filter
This paper presents a 6-DoF range-based Monte Carlo localization method with a GPU-accelerated Stein particle filter. To update a massive amount of particles, we propose a Gauss-Newton-based Stein variational gradient descent (SVGD) with iterative neighbor particle search. This method uses SVGD to collectively update particle states with gradient and neighborhood information, which provides efficient particle sampling. For an efficient neighbor particle search, it uses locality sensitive hashin…

This https://arxiv.org/abs/2210.13161 has been replaced.
link: https://scholar.google.com/scholar?q=a

Functional and variational aspects of nonlocal operators associated with linear PDEs
We introduce a general difference quotient representation for non-local operators associated with a first-order linear operator. We establish new local to non-local estimates and strong localization principles in various spaces of functions, measures and distributions, which fully generalize those known for gradients. Under suitable assumptions, we also establish the invariance of quasiconvexity within the proposed local-nonlocal setting. Applications to the fine properties of $\mathcal A$-grad…

This https://arxiv.org/abs/2307.14642 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?
We prove that black-box variational inference (BBVI) with control variates, particularly the sticking-the-landing (STL) estimator, converges at a geometric (traditionally called "linear") rate under perfect variational family specification. In particular, we prove a quadratic bound on the gradient variance of the STL estimator, one which encompasses misspecified variational families. Combined with previous works on the quadratic variance condition, this directly implies convergence of BBVI with…

MegaParticles: Range-based 6-DoF Monte Carlo Localization with GPU-Accelerated Stein Particle Filter
Kenji Koide, Shuji Oishi, Masashi Yokozuka, Atsuhiko Banno
https://arxiv.org/abs/2404.16370

MegaParticles: Range-based 6-DoF Monte Carlo Localization with GPU-Accelerated Stein Particle Filter
This paper presents a 6-DoF range-based Monte Carlo localization method with a GPU-accelerated Stein particle filter. To update a massive amount of particles, we propose a Gauss-Newton-based Stein variational gradient descent (SVGD) with iterative neighbor particle search. This method uses SVGD to collectively update particle states with gradient and neighborhood information, which provides efficient particle sampling. For an efficient neighbor particle search, it uses locality sensitive hashin…

German results for the european election are terrible. Here are pics of our cats from today instead!

What an uplifting story—it made my day! #MicroFarming #Gardening #NoMoreLawns

Variational Analysis in the Wasserstein Space
Nicolas Lanzetti, Antonio Terpin, Florian D\"orfler
https://arxiv.org/abs/2406.10676 https://

Variational Analysis in the Wasserstein Space
We study optimization problems whereby the optimization variable is a probability measure. Since the probability space is not a vector space, many classical and powerful methods for optimization (e.g., gradients) are of little help. Thus, one typically resorts to the abstract machinery of infinite-dimensional analysis or other ad-hoc methodologies, not tailored to the probability space, which however involve projections or rely on convexity-type assumptions. We believe instead that these proble…

This https://arxiv.org/abs/2404.19027 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Better Optimization of Variational Quantum Eigensolvers by combining the Unitary Block Optimization Scheme with Classical Post-Processing
Variational Quantum Eigensolvers (VQE) are a promising approach for finding the classically intractable ground state of a Hamiltonian. The Unitary Block Optimization Scheme (UBOS) is a state-of-the-art VQE method which works by sweeping over gates and finding optimal parameters for each gate in the environment of other gates. UBOS improves the convergence time to the ground state by an order of magnitude over Stochastic Gradient Descent (SGD). It nonetheless suffers in both rate of convergence …

This https://arxiv.org/abs/2211.07861 has been replaced.
link: https://scholar.google.com/scholar?q=a

Regularized Stein Variational Gradient Flow
The Stein Variational Gradient Descent (SVGD) algorithm is a deterministic particle method for sampling. However, a mean-field analysis reveals that the gradient flow corresponding to the SVGD algorithm (i.e., the Stein Variational Gradient Flow) only provides a constant-order approximation to the Wasserstein Gradient Flow corresponding to the KL-divergence minimization. In this work, we propose the Regularized Stein Variational Gradient Flow, which interpolates between the Stein Variational Gr…

This https://arxiv.org/abs/2402.14758 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Batch and match: black-box variational inference with a score-based divergence
Most leading implementations of black-box variational inference (BBVI) are based on optimizing a stochastic evidence lower bound (ELBO). But such approaches to BBVI often converge slowly due to the high variance of their gradient estimates and their sensitivity to hyperparameters. In this work, we propose batch and match (BaM), an alternative approach to BBVI based on a score-based divergence. Notably, this score-based divergence can be optimized by a closed-form proximal update for Gaussian va…