Mar 22, 2025 · VSL Machine Learning (vsl.ml) VSL aims to provide a robust set of tools for scientific computing with an emphasis on performance and ease of use. In the vsl.ml module, some machine learning models are designed as observers of data, meaning they re-train automatically when data changes, while others do not require this functionality.

Nov 1, 2023 · Results demonstrated that using VSL in classic machine learning, deep learning, and ensemble learning could significantly improve the efficiency of aeration intelligent control in WWTPs. Bayesian regression and ensemble learning achieved the highest accuracy for predicting air demand.

In this article, we introduce you to a simple-to-use training method called Variable Sequence Length (VSL), which can reduce wall-clock times for training large language models with long sequence lengths capabilities without any changes in model architecture and training hyperparameters.

Mar 13, 2021 · VSL reduces the speed of incoming vehicles to a bottleneck area, and RM limits the inflow through on-ramps. In addition, with the increasing development of Autonomous Vehicles (AVs) and Connected AVs (CAVs), new opportunities for traffic control are emerging.

Nov 21, 2021 · vsl.ml has Kmeans, LinearRegression, KNN, Params Regression, NLP, and a Data wrapper with an observer pattern. Thirdparty hamnn is a machine learning library for classification using a nearest neighbor algorithm based on Hamming distances.

Jan 11, 2023 · We want to create a new model on vsl.ml to do classification using the Random Forest algorithm. That model should follow the following interfaces: vsl.util.Observer; Candidate intarface for the struct that we need to create:

V library to develop Artificial Intelligence and High-Performance Scientific Computations - vlang/vsl

Mar 22, 2025 · count_vectorize will give you an array of occurrences of each ngram from ngrams in most_frequent. Assume ng := [['hello'], ['hello'], ['hi']]. nlp.count_vectorize(ng, nlp.most_frequent_ngrams(ng, 0)) should return [2, 1]. See most_frequent_ngrams for more details on how it works.

Having Docker and VS Code installed, you can start developing powerful numerical simulations using VSL in a matter of seconds. Furthermore, the best part of it is that it works on Windows, Linux, and macOS out of the box. Quick, containerized (recommended!) Done. And your system will remain "clean".

Jul 17, 2020 · Machine learning techniques, specifically Reinforcement Learning (RL) methods, are a promising alternative for setting up VSL since they can learn and react to different traffic situations...