Order selection with confidence for finite mixture models

Autor: Hien D. Nguyen, Daniel Fryer, Geoffrey J. McLachlan

Publikováno v: Journal of the Korean Statistical Society. 52:154-184

The determination of the number of mixture components (the order) of a finite mixture model has been an enduring problem in statistical inference. We prove that the closed testing principle leads to a sequential testing procedure (STP) that allows fo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d7396cb4d1f4fc74ffb1da989ce7c0b
https://doi.org/10.1007/s42952-022-00195-z

Trading performance for memory in sparse direct solvers using low-rank compression

Autor: Grégoire PICHON, Thibault Marette, Loris Marchal, Frédéric Vivien

Publikováno v: Future Generation Computer Systems
Future Generation Computer Systems, 2022, 130, pp.307-320. ⟨10.1016/j.future.2021.12.018⟩
[Research Report] RR-9368, INRIA. 2020
Future Generation Computer Systems, Elsevier, In press

Sparse direct solvers using Block Low-Rank compression have been proven efficient to solve problems arising in many real-life applications. Improving those solvers is crucial for being able to 1) solve larger problems and 2) speed up computations. A

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f63db4c7540f15de729501627227b184
https://doi.org/10.1016/j.future.2021.12.018

Two-Layer Neural Networks with Values in a Banach Space

Autor: Yury Korolev

Publikováno v: Korolev, Y 2022, ' Two-layer neural networks with values in a Banach space ', SIAM Journal on Mathematical Analysis, vol. 54, no. 6, pp. 6358-6389 . https://doi.org/10.1137/21M1458144

We study two-layer neural networks whose domain and range are Banach spaces with separable preduals. In addition, we assume that the image space is equipped with a partial order, i.e. it is a Riesz space. As the nonlinearity we choose the lattice ope

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::29a1a87b3dea86eeb4339347f289aa4d
https://doi.org/10.1137/21m1458144

A deterministic gradient-based approach to avoid saddle points

Autor: L. M. Kreusser, S. J. Osher, B. Wang

Publikováno v: Kreusser, L M, Osher, S J & Wang, B 2023, ' A Deterministic Gradient-Based Approach to Avoid Saddle Points ', European Journal of Applied Mathematics, vol. 34, no. 4, pp. 738-757 . https://doi.org/10.1017/S0956792522000316

Loss functions with a large number of saddle points are one of the major obstacles for training modern machine learning (ML) models efficiently. First-order methods such as gradient descent (GD) are usually the methods of choice for training ML model

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::138ae8b4831bd0df4615e38acd89b9c9
https://doi.org/10.1017/s0956792522000316

Fast solvers for high-frequency aeroacoustics

Autor: Felšöci, Marek

Publikováno v: Numerical Analysis [cs.NA]. Université de Bordeaux, 2023. English. ⟨NNT : 2023BORD0031⟩

In the aeronautical industry, aeroacoustics is used to model the propagation of acoustic waves in air flows enveloping an aircraft in flight. One can thus simulate the noise produced at ground level by an aircraft during the takeoff and landing phase

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______165::9a836aa683788f5dbee4633582ba7686
https://theses.hal.science/tel-04077474/file/FELSOCI_MAREK_2023.pdf