CaloGAN speeds up particle shower simulations in calorimeters.
problem Accurate simulation of particle showers in calorimeters for high-energy physics experiments.
method Generative adversarial networks (GANs) for fast simulation.
result Achieved speedup factors of up to 100,000x compared to full simulation techniques.
Generative model speeds up particle shower simulations in calorimeters.
problem Expensive and slow particle collision simulations for LHC experiments.
method Deep neural network for high-fidelity, fast electromagnetic calorimeter simulation.
result Achieves speed-up factors of up to 100,000x while maintaining accuracy.
DVAEs speed up calorimeter simulation for LHC data.
problem Slow calorimeter simulation in LHC experiments.
method Discrete Variational Autoencoders (DVAEs).
result Significantly faster calorimeter shower simulation.
New methods use machine learning to tighten constraints on particle physics theories.
problem Improving precision of LHC legacy constraints on particle physics theories.
method Machine learning techniques applied to Monte-Carlo simulations of particle physics processes.
result Significantly stronger bounds on dimension-six operators achieved.
New methods use machine learning to constrain particle physics parameters.
problem Constraining effective field theory parameters in collider experiments.
method Inference techniques using latent-space structure and neural networks trained on augmented data.
result Significantly stronger bounds on effective dimension-six operators.
This research improves calorimeter simulations by creating a faster model.
problem Efficiently simulating detailed calorimeter data for high-energy physics.
method Developed a conditional normalizing flow model for superresolution.
result The model successfully reproduces reference distributions.
New method identifies quark and gluon jets from collider data.
problem Determine quark and gluon jet distributions from collider data.
method Apply topic modeling to jet distributions, using parton shower and theoretical predictions.
result Determined separate quark and gluon jet distributions and spectra.
Unified access package for fundamental physics datasets simplifies machine learning.
problem Lack of unified access to datasets from multiple fundamental physics disciplines.
method Unified Python package with common interface and reference models.
result Graph-based neural networks perform similarly to dedicated methods on various datasets.
New method uses neural networks to estimate parameters without needing detector simulations.
problem Estimating parameters in high-energy physics with detector effects.
method Two-level fitting approach: SRGN (Simulation-level fit based on Reweighting Generator-level events with Neural networks).
result Demonstrated using simulated datasets, SRGN can estimate parameters without detector effects.
A computer vision approach improves neutral particle detection in particle flow algorithms.
problem Optimal reconstruction of particle content and kinematics in calorimeter images.
method Computer vision techniques applied to calorimeter images, using deep learning and super-resolution.
result Significantly improved reconstruction of neutral particle calorimeter energy deposits.
Study examines the excluded area between two-dimensional hard particles, identifying key factors affecting its magnitude.
problem Determining the excluded area between two-dimensional hard particles with various orientations and shapes.
method Used principal component analysis and Monte Carlo simulations to analyze randomly generated non-self-intersecting polygons and star lines.
result The minimum excluded area is achieved when particles are antiparallel, and elongation of the particle shape significantly affects the excluded area.
Jointly estimates flow fields and particle properties from Lagrangian data.
problem Estimating flow fields and particle properties from sparse, noisy Lagrangian data.
method Data assimilation framework coupling Eulerian and Lagrangian models.
result Joint estimation of flow fields and particle properties in various flow regimes.
Estimates log-likelihood of interacting particle systems using virtual particles.
problem Inconsistent estimation of finite-particle log-likelihood in large particle systems.
method Stochastic gradient estimate using continuous trajectory and virtual particle systems.
result Convergence to stationary points of limiting mean-field system's log-likelihood.
This paper optimizes functions of probability measures using particle gradient descent for displacement convex functions.
problem Optimizing functions of probability measures with displacement convex properties.
method Particle gradient descent applied to displacement convex functions with theoretical guarantees.
result Finite number of particles and computations are sufficient to find optimal solutions for displacement convex functions.
Differentiable resampling improves particle filter performance.
problem Non-differentiability of traditional resampling in particle filters.
method Introduced a neural network resampler (particle transformer) trained with a likelihood-based loss function.
result Learned resampling outperforms traditional methods on synthetic and real-world tasks.
New sampling method improves on particle-optimization techniques.
problem Particles tend to collapse in SVGD, leading to poor sampling.
method Introduce stochastic noise to update particles, developing non-asymptotic convergence theory.
result More particles do not always improve approximation due to computational constraints.
A method for optimal Bayesian filtering using progressive particle flow and optimal transport maps.
problem Optimizing Bayesian filtering with deterministic particles to avoid degeneration.
method Progressive flow of particles through a sequence of sub-steps, each using an optimal transport map to replace non-equally weighted particles with equally weighted ones.
result The method avoids particle degeneration and simplifies the filtering process by not requiring inversions or monotonicity constraints.
Optimal weights improve particle-based approximations of discrete distributions.
problem Improving particle-based approximations of discrete distributions.
method Proving optimality of weights and showing how to compute them efficiently.
result Optimal weights can be computed from existing particle-based methods without extra costs.
Particle MCMC involves using a particle filter within an MCMC algorithm. For inference of a model which involves an unobserved stochastic process, the standard implementation uses the particle filter to propose new values for the stochastic process, and MCMC moves to propose new values for the parameters. We show how p…
Topaz uses neural networks to pick particles from cryoEM images efficiently.
problem Manual particle picking is time-consuming and requires post-processing.
method Positive-Unlabeled (PU) learning with minimal labeled data.
result Topaz improves cryoEM reconstruction resolution by up to 0.15 Å.
Optimizes particle filtering for non-stationary environments.
problem Tracking and adapting to non-stationary environments in online prediction.
method Formulated an efficient particle filtering method using online mirror descent algorithm.
result Achieves optimal particle efficiency in non-stationary environments.
Improved particle approximation for mean-field neural networks.
problem Particle approximation error for mean-field neural networks.
method Improved particle approximation error by leveraging the problem structure in risk minimization.
result Established an LSI-constant-free particle approximation error concerning the objective gap.
A new particle filter avoids resampling to improve state estimation in high dimensions.
problem Particle deprivation in high-dimensional state spaces.
method A resampling-free particle filter designed to mitigate particle deprivation.
result The filter offers a near-accurate representation of the posterior distribution in high-dimensional contexts.
We introduce a new sequential Monte Carlo algorithm we call the particle cascade. The particle cascade is an asynchronous, anytime alternative to traditional particle filtering algorithms. It uses no barrier synchronizations which leads to improved particle throughput and memory efficiency. It is an anytime algorithm i…
Paper uses averaging from many particle filters to approximate posterior predictive distributions.
problem Approximating posterior predictive distributions efficiently and accurately.
method Particle swarm filter algorithm that averages many particle filter approximations.
result Law of large numbers and central limit theorem support the method's effectiveness.
This paper studies when particle filtering is efficient for planning in partially observed systems.
problem The efficiency of particle filtering for planning in partially observed linear dynamical systems.
method Coupling of ideal and approximate sequences to bound particle complexity.
result Polynomially many particles suffice for stable systems to approximate optimal planning.
Interacting particle methods are increasingly used to sample from complex and high-dimensional distributions. These stochastic particle integration techniques can be interpreted as an universal acceptance-rejection sequential particle sampler equipped with adaptive and interacting recycling mechanisms. Practically, the…
Cryo-electron microscopy (cryo-EM) is an emerging experimental method to characterize the structure of large biomolecular assemblies. Single particle cryo-EM records 2D images (so-called micrographs) of projections of the three-dimensional particle, which need to be processed to obtain the three-dimensional reconstruct…
Improved particle filters enhance vehicle tracking accuracy.
problem Particle filters struggle with frequent, informative observations.
method Proposes particle filters that sample around recent observations.
result Significant improvement in accuracy and efficiency.
New algorithm trains latent diffusion models using interacting particles.
problem Training latent diffusion models efficiently and accurately.
method Reformulate training as minimizing a free energy functional, then approximate with interacting particles.
result The new algorithm outperforms previous methods in experiments.
Paper learns particle dynamics for versatile object manipulation.
problem Challenges in traditional rigid-body physics engines for complex scenes.
method Combines learning with particle-based systems for versatile object manipulation.
result Robots achieve complex manipulation tasks using the learned simulator.
Framework expands particle filtering to estimate states beyond prior boundaries.
problem Limitations of traditional particle filtering in estimating states outside prior support.
method Diffusion-Enhanced Particle Filtering Framework with adaptive diffusion, entropy-driven regularisation, and kernel-based perturbations.
result Framework significantly improves state estimation accuracy and success rates for out-of-boundary targets.
Bayesian inference using particle flow and neural operators.
problem Efficiently updating beliefs with new data.
method ODE-based neural operator for particle flow, meta-learning for generalization.
result Generalization across different priors, observations, and sequential inference.
Enhances particle filters with neural augmentation for multi-sub-state tracking.
problem Particle filters struggle with complex or approximated models and low latency requirements.
method Learning Flock (LF) uses a neural network to correct particle weights based on sub-particle relationships.
result LF improves performance, robustness, and latency in radar multi-target tracking.
New string singularities enable quantum teleportation of massive particles.
problem Quantum teleportation of massive particles at large distances.
method Introduction of new world-sheet string singularities (cusps) that are stable and allow for the emission of captured massive quantum particles.
result Existence of a new mechanism for quantum teleportation of massive particles.
New particle filter estimates model evidence without bias.
problem Unbiased estimation of marginal likelihood for model comparison.
method Particle filter with rejection control.
result Unbiased estimation of marginal likelihood.
CMS uses machine learning to improve particle flow reconstruction.
problem Improving particle flow reconstruction in CMS.
method Machine learning, graph neural network, heterogeneous computing.
result Machine-learned PF model outperforms standard algorithm.
A new particle algorithm improves mean-field variational inference.
problem Efficiently approximating nonparametric posterior distributions in machine learning.
method Introduces PArticle VI (PAVI), a novel particle-based algorithm for nonparametric mean-field approximation.
result Obtains non-asymptotic error bounds for PArticle VI, providing the first end-to-end guarantee for particle-based MFVI.
A method makes particle filters differentiable without altering their forward pass.
problem Compatibility issues between particle filters and automatic differentiation.
method Introduces a correction to particle weights using the stop-gradient operator.
result Automatic differentiation produces good estimators for gradients and second-order derivatives.
Cryo-electron microscopy (cryo-EM) studies using single particle reconstruction are extensively used to reveal structural information on macromolecular complexes. Aiming at the highest achievable resolution, state of the art electron microscopes automatically acquire thousands of high-quality micrographs. Particles are…
We introduce a novel description of the dynamics of the order book of financial markets as that of an effective colloidal Brownian particle embedded in fluid particles. The analysis of a comprehensive market data enables us to identify all motions of the fluid particles. Correlations between the motions of the Brownian…
Deep Sets improve jet discrimination in particle physics.
problem Representing and learning from collider events with variable-length particle sets.
method Energy Flow Networks and Particle Flow Networks, based on Deep Sets framework.
result Improved or similar performance in discriminating quark jets from gluon jets compared to existing methods.
dSMC improves parallel processing of state-space models.
problem Processing multiple observations efficiently in state-space models.
method A parallel-in-time particle smoother that reduces complexity to log(T).
result dSMC achieves O(log(T)) time complexity on parallel architectures.
Achieved all-orders worldline action for Kerr black hole.
problem Calculating effective action for Kerr black hole.
method Twistor particle theory.
result All-orders worldline effective action for Kerr black hole.
Unified approach unites GANs and diffusion models using particle methods.
problem Combining GANs and diffusion models for generative tasks.
method Proposes a unified framework where generator training is seen as a generalization of particle models.
result Demonstrates that GANs and diffusion models can be integrated within a unified framework.
A new variational inference method using optimal transport.
problem Approximating complex posterior distributions with flexible particle-based methods.
method Introducing a new particle-based variational inference method based on semi-discrete optimal transport.
result The method provides a particle approximation and optimal transportation densities.
Two SVGD variants achieve fast convergence with provable guarantees.
problem Understanding and improving SVGD's performance with finite particles.
method Introducing virtual particles and novel stochastic approximations.
result Provable fast convergence rates for finite-particle SVGD variants.
PF-net combines neural network and particle filter for robot localization.
problem Applying particle filtering to complex systems with rich sensory inputs.
method PF-net integrates system model and particle filter in a neural network.
result PF-net outperforms alternative methods in visual localization tasks.