:: Library Catalog

Saved in:

Bibliographic Details
Main Author:	Deville, Yves
Format:	Preprint
Published:	2024
Subjects:	Computation
Online Access:	https://arxiv.org/abs/2404.02774
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Multi-stage tomography based on eigenanalysis for high-dimensional dense unitary processes in gate-based quantum computers
by: Deville, Yannick, et al.
Published: (2024)

Solving the Zeh problem about the density operator with higher-order statistics
by: Deville, Alain, et al.
Published: (2024)

Random-coefficient pure states, the density operator formalism and the Zeh problem
by: Deville, Alain, et al.
Published: (2022)

Beyond the density operator and Tr(ρA): Exploiting the higher-order statistics of random-coefficient pure states for quantum information processing
by: Deville, Yannick, et al.
Published: (2022)

Use and misuse of variances for quantum systems in pure or mixed states
by: Deville, Alain, et al.
Published: (2023)

The Landau-Feynman transiently open quantum system: entanglement and density operators
by: Deville, Alain, et al.
Published: (2025)

Penalized Likelihood Parameter Estimation for Differential Equation Models: A Computational Tutorial
by: Simpson, Matthew J, et al.
Published: (2026)

An Introduction to the Mechanics of Incompressible Fluids
by: Deville, Michel O.
Published: (2022)

Fairness Evaluation of Neural Networks Through Computational Profile Likelihood
by: Benjamin Djian, et al.
Published: (2025)

Likelihood Estimation for Stochastic Differential Equations with Mixed Effects
by: Fernando Baltazar‐Larios, et al.
Published: (2026)

Barbie drug identification: Not a child's play
by: Marine Deville, et al.
Published: (2024)

Unitary quantum process tomography with unreliable pure input states
by: Verdeil, François, et al.
Published: (2023)

JAX-BEM: Gradient-Based Acoustic Shape Optimisation via a Differentiable Boundary Element Method
by: Hipperson, James, et al.
Published: (2026)

Estimating Marginal Likelihoods in Likelihood-Free Inference via Neural Density Estimation
by: Bastide, Paul, et al.
Published: (2025)

Differential Equations for Gaussian Statistical Models with Rational Maximum Likelihood Estimator
by: Améndola, Carlos, et al.
Published: (2023)

Differentiable Score-Based Likelihoods: Learning CT Motion Compensation From Clean Images
by: Thies, Mareike, et al.
Published: (2024)

On Nonparanormal Likelihoods
by: Hothorn, Torsten
Published: (2024)

Scaling of Differential Equations
by: Langtangen, Hans Petter, et al.
Published: (2020)

Learning Optical Flow Field via Neural Ordinary Differential Equation
by: Mirvakhabova, Leyla, et al.
Published: (2025)

Bias-Reduced Estimation of Structural Equation Models
by: Jamil, Haziq, et al.
Published: (2025)

Selective Inference in Graphical Models via Maximum Likelihood
by: Guglielmini, Sofia, et al.
Published: (2025)

Coupled gas and bubble dynamics at the solidification front
by: Isabella, Bastien, et al.
Published: (2026)

Neural Differential Appearance Equations
by: Liu, Chen, et al.
Published: (2024)

Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes
by: Lin, Jihao Andreas, et al.
Published: (2024)

Grammar-based Ordinary Differential Equation Discovery
by: Yu, Karin L., et al.
Published: (2025)

Randomized Maximum Likelihood via High-Dimensional Bayesian Optimization
by: Breaz, Valentin, et al.
Published: (2022)

Likelihood Level Adapted Estimation of Marginal Likelihood for Bayesian Model Selection
by: De, Subhayan, et al.
Published: (2025)

Profile Likelihoods on ML-Steroids
by: Heimel, Theo, et al.
Published: (2024)

ChartOptimiser: Task-driven Optimisation of Chart Designs
by: Wang, Yao, et al.
Published: (2025)

Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait Sketching
by: Jiang, Nan, et al.
Published: (2024)

Symbolic Discovery of Stochastic Differential Equations with Genetic Programming
by: de Vries, Sigur, et al.
Published: (2026)

Universal Cross-Tokenizer Distillation via Approximate Likelihood Matching
by: Minixhofer, Benjamin, et al.
Published: (2025)

Efficient Stochastic Optimisation via Sequential Monte Carlo
by: Cuin, James, et al.
Published: (2026)

Complexity Theory meets Ordinary Differential Equations
by: Fono, Adalbert, et al.
Published: (2026)

Solving Differential Equations with Constrained Learning
by: Moro, Viggo, et al.
Published: (2024)

Forecasting N-Body Dynamics: A Comparative Study of Neural Ordinary Differential Equations and Universal Differential Equations
by: S, Suriya R, et al.
Published: (2025)

The Energy of Falsehood: Detecting Hallucinations via Diffusion Model Likelihoods
by: Gautam, Arpit Singh, et al.
Published: (2026)

Offline Preference Optimization via Maximum Marginal Likelihood Estimation
by: Najafi, Saeed, et al.
Published: (2025)

Particle Data Cloning for Complex Ordinary Differential Equations
by: Son, Donghui, et al.
Published: (2025)

Mapping 1,000+ Language Models via the Log-Likelihood Vector
by: Oyama, Momose, et al.
Published: (2025)