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Found 47 papers, 25 papers with code
GPflow: A Gaussian process library using TensorFlow
GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end.
MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning on/for Quantum Computers in the NISQ era
In fact, also the Machine Learning research related to quantum computers undertakes a dual challenge: enhancing machine learning exploiting the power of quantum computers, while also leveraging state-of-the-art classical machine learning methodologies to help the advancement of quantum computing.
noisy quantum circuit classification (quantum ML, error mitigation)
quantum circuit classification (classical ML)
+1
Variational Quantum Algorithms
Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high computational cost.
Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables
In this paper, we formulate Koopman spectral analysis for NLDSs with structures among observables and propose an estimation algorithm for this problem.
Physically-interpretable classification of biological network dynamics for complex collective motions
A fundamental question addressed here pertains to the classification of collective motion network based on physically-interpretable dynamical properties.
Learning to Predict Navigational Patterns from Partial Observations
We demonstrate how to infer global navigational patterns by fitting a maximum likelihood graph to the DSLP field.
Ranked #1 on
Lane Detection
on nuScenes
Quantum Circuit Learning
Hybridizing a low-depth quantum circuit and a classical computer for machine learning, the proposed framework paves the way toward applications of near-term quantum devices for quantum machine learning.
Quantum Physics
Learning interaction rules from multi-animal trajectories via augmented behavioral models
In this paper, we propose a new framework for learning Granger causality from multi-animal trajectories via augmented theory-based behavioral models with interpretable data-driven models.
Transformer-Based Neural Marked Spatio Temporal Point Process Model for Football Match Events Analysis
However, most sports sequential events modeling methods and performance metrics approaches could be incomprehensive in dealing with such large-scale spatiotemporal data (in particular, temporal process), thereby necessitating a more comprehensive spatiotemporal model and a holistic performance metric.
Location analysis of players in UEFA EURO 2020 and 2022 using generalized valuation of defense by estimating probabilities
Using the open-source location data of all players in broadcast video frames in football games of men's Euro 2020 and women's Euro 2022, we investigated the effect of the number of players on the prediction and validated our approach by analyzing the games.
Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators
The development of a metric for structural data is a long-term problem in pattern recognition and machine learning.
Succinct Trit-array Trie for Scalable Trajectory Similarity Search
To address this problem, we present the trajectory-indexing succinct trit-array trie (tSTAT), which is a scalable method leveraging LSH for trajectory similarity searches.
Predictive World Models from Real-World Partial Observations
By extending HVAEs to cases where complete ground truth states do not exist, we facilitate continual learning of spatial prediction as a step towards realizing explainable and comprehensive predictive world models for real-world mobile robotics applications.
ViCE: Improving Dense Representation Learning by Superpixelization and Contrasting Cluster Assignment
Recent self-supervised models have demonstrated equal or better performance than supervised methods, opening for AI systems to learn visual representations from practically unlimited data.
Estimation of control area in badminton doubles with pose information from top and back view drone videos
In this work, we present the first annotated drone dataset from top and back views in badminton doubles and propose a framework to estimate the control area probability map, which can be used to evaluate teamwork performance.
Automatic detection of faults in race walking from a smartphone camera: a comparison of an Olympic medalist and university athletes
We also revealed that the machine learning model detects faults according to the rules of race walking.
Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: an application to rugby union
Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence.
Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction
However, it has remained difficult to evaluate an attacking player without receiving the ball, and to reveal how movement contributes to the creation of scoring opportunities for teammates.
Harnessing disordered quantum dynamics for machine learning
Quantum computer has an amazing potential of fast information processing.
Robust Regression for Automatic Fusion Plasma Analysis based on Generative Modeling
The first step to realize automatic experimental data analysis for fusion plasma experiments is fitting noisy data of temperature and density spatial profiles, which are obtained routinely.
The International Linear Collider: A Global Project
In this report, we review of all aspects of the ILC program: the physics motivation, the accelerator design, the run plan, the proposed detectors, the experimental measurements on the Higgs boson, the top quark, the couplings of the W and Z bosons, and searches for new particles.
High Energy Physics - Experiment High Energy Physics - Phenomenology Accelerator Physics
Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling
Extracting the rules of real-world multi-agent behaviors is a current challenge in various scientific and engineering fields.
Power-law intensity distribution in $γ$-decay cascades
We show that the distribution of $\gamma$-decay intensities from heavy nuclei follows a power law.
Nuclear Theory
Quantum self-learning Monte Carlo with quantum Fourier transform sampler
The self-learning Metropolis-Hastings algorithm is a powerful Monte Carlo method that, with the help of machine learning, adaptively generates an easy-to-sample probability distribution for approximating a given hard-to-sample distribution.
Quantum Physics
Methodology for replacing indirect measurements with direct measurements
However, in certain cases, the indirect measurement can be reduced to the direct measurement, where a quantum state is destructively measured.
Quantum Physics
High-threshold fault-tolerant quantum computation with analog quantum error correction
To reduce this requirement, we propose a high-threshold fault-tolerant quantum computation with GKP qubits using topologically protected measurement-based quantum computation with the surface code.
Quantum Physics
Data-driven Analysis for Understanding Team Sports Behaviors
This survey focuses on data-driven analysis for quantitative understanding of invasion team sports behaviors such as basketball and football, and introduces two main approaches for understanding such multi-agent behaviors: (1) extracting easily interpretable features or rules from data and (2) generating and controlling behaviors in visually-understandable ways.
Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections
Extracting coherent patterns is one of the standard approaches towards understanding spatio-temporal data.
Quantum error mitigation as a universal error-minimization technique: applications from NISQ to FTQC eras
In the early years of fault-tolerant quantum computing (FTQC), it is expected that the available code distance and the number of magic states will be restricted due to the limited scalability of quantum devices and the insufficient computational power of classical decoding units.
Quantum Physics
Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study
Results show that the proposed classifiers predicted the true events (mean F1 score $>$ 0. 483) better than the existing classifiers which were based on rare events or goals (mean F1 score $<$ 0. 201).
Quantum tangent kernel
In this work, we explore a quantum machine learning model with a deep parameterized quantum circuit and aim to go beyond the conventional quantum kernel method.
How does AI play football? An analysis of RL and real-world football strategies
Recent advances in reinforcement learning (RL) have made it possible to develop sophisticated agents that excel in a wide range of applications.
Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network
In this paper, we propose to use quantum neural networks for parametric t-SNE to reflect the characteristics of high-dimensional quantum data on low-dimensional data.
Estimating counterfactual treatment outcomes over time in complex multiagent scenarios
Evaluation of intervention in a multiagent system, e. g., when humans should intervene in autonomous driving systems and when a player should pass to teammates for a good shot, is challenging in various engineering and scientific fields.
Estimating the Effect of Team Hitting Strategies Using Counterfactual Virtual Simulation in Baseball
The weighted on base average (wOBA) is well known as a measure of an batter's hitting contribution.
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations
In the experiments, using chase-and-escape and football tasks with the different dynamics between the unknown source and target environments, we show that our approach achieved a balance between the reproducibility and the generalization ability compared with the baselines.
Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning
In this paper, we propose a method of valuing possible actions for on- and off-ball soccer players in a single holistic framework based on multi-agent deep reinforcement learning.
A Strategic Framework for Optimal Decisions in Football 1-vs-1 Shot-Taking Situations: An Integrated Approach of Machine Learning, Theory-Based Modeling, and Game Theory
To address this issue, we proposed a novel framework to analyze such scenarios based on game theory, where we estimate the expected payoff with machine learning (ML) models, and additional features for ML models were extracted with a theory-based shot block model.
Evaluating Soccer Match Prediction Models: A Deep Learning Approach and Feature Optimization for Gradient-Boosted Trees
The original training set of matches and features, which was provided for the competition, was augmented with additional matches that were played between 4 April and 13 April 2023, representing the period after which the training set ended, but prior to the first matches that were to be predicted (upon which the performance was evaluated).
Runner re-identification from single-view running video in the open-world setting
However, most of the current studies on player re-identification in multi- or single-view sports videos focus on re-identification in the closed-world setting using labeled image dataset, and player re-identification in the open-world setting for automatic video analysis is not well developed.
DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understanding
The DRUformer is a transformer-based multi-modal important object detection model that takes into account the relationships between all the participants in the driving scenario.
Continuous optimization by quantum adaptive distribution search
In this paper, we introduce the quantum adaptive distribution search (QuADS), a quantum continuous optimization algorithm that integrates Grover adaptive search (GAS) with the covariance matrix adaptation - evolution strategy (CMA-ES), a classical technique for continuous optimization.
Quantum Kernel t-Distributed Stochastic Neighbor Embedding
Since quantum states are higher dimensional objects that can only be seen via observables, our visualization method, which inherits the similarity of quantum data, would be useful in understanding the behavior of quantum circuits and algorithms.
Foul prediction with estimated poses from soccer broadcast video
Recent advances in computer vision have made significant progress in tracking and pose estimation of sports players.
Machine Learning for Soccer Match Result Prediction
The aim of this chapter is to give a broad overview of the current state and potential future developments in machine learning for soccer match results prediction, as a resource for those interested in conducting future studies in the area.
Offensive Lineup Analysis in Basketball with Clustering Players Based on Shooting Style and Offensive Role
This study employs two methods to capture the playing styles of players on offense: shooting style clustering using tracking data, and offensive role clustering based on annotated playtypes and advanced statistics.
