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Found 26 papers, 12 papers with code
Wyckoff Transformer: Generation of Symmetric Crystals
Symmetry rules that atoms obey when they bond together to form an ordered crystal play a fundamental role in determining their physical, chemical, and electronic properties such as electrical and thermal conductivity, optical and polarization behavior, and mechanical strength.
Predicting ionic conductivity in solids from the machine-learned potential energy landscape
One can achieve significant computational advantages by leveraging them as the foundation for traditional methods of assessing the ionic conductivity, such as molecular dynamics or nudged elastic band techniques.
AI Competitions and Benchmarks: Competition platforms
The ecosystem of artificial intelligence competitions is a diverse and multifaceted landscape, encompassing a variety of platforms that each host numerous competitions annually, alongside a plethora of specialized websites dedicated to singular contests.
Symbolic expression generation via Variational Auto-Encoder
There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature.
The Tracking Machine Learning challenge : Throughput phase
Both were measured on the Codalab platform where the participants had to upload their software.
Segmentation of EM showers for neutrino experiments with deep graph neural networks
For the clustering of overlapping showers, we use a modified hierarchical density-based clustering algorithm.
Online detection of failures generated by storage simulator
In this work, we challenge two problems: 1) lack of storage data in the methods above by creating a simulator and 2) applying existing online algorithms that can faster detect a failure occurred in one of the components.
A study of Neural networks point source extraction on simulated Fermi/LAT Telescope images
Astrophysical images in the GeV band are challenging to analyze due to the strong contribution of the background and foreground astrophysical diffuse emission and relatively broad point spread function of modern space-based instruments.
Black-Box Optimization with Local Generative Surrogates
To address such cases, we introduce the use of deep generative models to iteratively approximate the simulator in local neighborhoods of the parameter space.
Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation
The goal of the change-point detection is to discover changes of time series distribution.
NFAD: Fixing anomaly detection using normalizing flows
Most of the conventional approaches to anomaly detection, such as one-class SVM and Robust Auto-Encoder, are one-class classification methods, i. e. focus on separating normal data from the rest of the space.
Adaptive Divergence for Rapid Adversarial Optimization
Adversarial Optimization (AO) provides a reliable, practical way to match two implicitly defined distributions, one of which is usually represented by a sample of real data, and the other is defined by a generator.
$(1 + \varepsilon)$-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets
We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples.
Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks
The increasing luminosities of future Large Hadron Collider runs and next generation of collider experiments will require an unprecedented amount of simulated events to be produced.
Cherenkov Detectors Fast Simulation Using Neural Networks
We propose a way to simulate Cherenkov detector response using a generative adversarial neural network to bypass low-level details.
Space Navigator: a Tool for the Optimization of Collision Avoidance Maneuvers
The number of space objects will grow several times in a few years due to the planned launches of constellations of thousands microsatellites.
Generative Models for Fast Calorimeter Simulation.LHCb case
Simulation is one of the key components in high energy physics.
Machine Learning in High Energy Physics Community White Paper
In this document we discuss promising future research and development areas for machine learning in particle physics.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
Numerical optimization for Artificial Retina Algorithm
High-energy physics experiments rely on reconstruction of the trajectories of particles produced at the interaction point.
Towards automation of data quality system for CERN CMS experiment
Daily operation of a large-scale experiment is a challenging task, particularly from perspectives of routine monitoring of quality for data being taken.
Muon Trigger for Mobile Phones
The CRAYFIS experiment proposes to use privately owned mobile phones as a ground detector array for Ultra High Energy Cosmic Rays.
Everware toolkit. Supporting reproducible science and challenge-driven education
Version control systems like git help with the workflow and analysis scripts part.
Computers and Society Distributed, Parallel, and Cluster Computing
Reproducible Experiment Platform
Data analysis in fundamental sciences nowadays is an essential process that pushes frontiers of our knowledge and leads to new discoveries.
Data Analysis, Statistics and Probability
Disk storage management for LHCb based on Data Popularity estimator
We use regression algorithms and time series analysis to find the optimal number of replicas for datasets that are kept on disk.
A genetic algorithm for autonomous navigation in partially observable domain
The problem of autonomous navigation is one of the basic problems for robotics.
New approaches for boosting to uniformity
The use of multivariate classifiers has become commonplace in particle physics.
High Energy Physics - Experiment
