Search Results for author:
Found 10 papers, 5 papers with code
Kernel Corrector LSTM
We empirically evaluate the forecasting accuracy and the training time of the new algorithm and compare it with cLSTM and LSTM.
Forecasting Events in Soccer Matches Through Language
This paper introduces an approach to predicting the next event in a soccer match, a challenge bearing remarkable similarities to the problem faced by Large Language Models (LLMs).
Towards a Systematic Approach to Design New Ensemble Learning Algorithms
This approach used 7 simple ensemble methods, we name them strategies, for neural networks that were used to generate 21 new ensemble algorithms.
Estimating Player Performance in Different Contexts Using Fine-tuned Large Events Models
This paper introduces an innovative application of Large Event Models (LEMs), akin to Large Language Models, to the domain of soccer analytics.
A Survey of Advanced Computer Vision Techniques for Sports
We provide a use case for such data: building a model for shot speed estimation with pose data obtained using only Computer Vision models.
Valuing Players Over Time
In soccer (or association football), players quickly go from heroes to zeroes, or vice-versa.
Pastprop-RNN: improved predictions of the future by correcting the past
Forecasting accuracy is reliant on the quality of available past data.
Hierarchical Qualitative Clustering: clustering mixed datasets with critical qualitative information
Clustering can be used to extract insights from data or to verify some of the assumptions held by the domain experts, namely data segmentation.
A Preliminary Study on Hyperparameter Configuration for Human Activity Recognition
In this paper, we present a semi-supervised classifier and a study regarding the influence of hyperparameter configuration in classification accuracy, depending on the user and the activities performed by each user.
autoBagging: Learning to Rank Bagging Workflows with Metalearning
Automated machine learning (autoML) is the field of ML that attempts to answers these needs.
