Individual-level human mobility prediction has emerged as a significant topic of research with applications in infectious disease monitoring, child, and elderly care. Existing studies predominantly focus on the microscopic aspects of human trajectories: such as predicting short-term trajectories or the next location visited, while offering limited attention to macro-level mobility patterns and the corresponding life routines. In this paper, we focus on an underexplored problem in human mobility prediction: determining the best practices to train a machine learning model using historical data to forecast an individuals complete trajectory over the next days and weeks. In this experiment paper, we undertake a comprehensive experimental analysis of diverse models, parameter configurations, and training strategies, accompanied by an in-depth examination of the statistical distribution inherent in human mobility patterns. Our empirical evaluations encompass both Long Short-Term Memory and Transformer-based architectures, and further investigate how incorporating individual life patterns can enhance the effectiveness of the prediction. We show that explicitly including semantic information such as day-of-the-week and user-specific historical information can help the model better understand individual patterns of life and improve predictions. Moreover, since the absence of explicit user information is often missing due to user privacy, we show that the sampling of users may exacerbate data skewness and result in a substantial loss in predictive accuracy. To mitigate data imbalance and preserve diversity, we apply user semantic clustering with stratified sampling to ensure that the sampled dataset remains representative. Our results further show that small-batch stochastic gradient optimization improves model performance, especially when human mobility training data is limited.

We are in a new era of space exploration, thanks to advancements in ground-and space-based telescopes, such as the James Webb Space Telescope [JWST2023PASP] and CRIRES. These remarkable instruments collect high-resolution, high-signal-to-noise spectra from extrasolar planets [Alderson2023Nature], and brown dwarfs [Miles2023ApJ] atmospheres. Without accurate interpretation of this data, the main objectives of space missions will not be fully accomplished. Different analytical and statistical methods, such as the chi-squared-test, Bayesian statistics, and radiative-transfer atmospheric modeling packages have been developed [batalha2019picaso, MacDonald2023] to interpret the spectra. They utilize either forwardand/or retrieval-radiative transfer modeling to analyze the spectra and extract physical information, such as atmospheric temperature, metallicity, carbon-to-oxygen ratio, and surface gravity [line2014systematic, Iyer2023Sphinx, Marley2015]. These atmospheric models rely on generating the physics and chemistry of these atmospheres for a wide range of thermal structures and compositions. In addition to Bayesian-based techniques, machine learning and deep learning methods have been developed in recent years for various astronomical problems, including confirming the classification of light curves for exoplanet validation [Valizadegan2022], recognizing molecular features [Zingales2018ExoGAN] as well as interpreting brown dwarfs spectra using Random Forest technique [Lueber2023RandomForesr_BDs].

Disadvantaged communities (DAC), as defined by the Justice40 initiative of the Department of Energy (DOE), USA, identifies census tracts across the USA to determine where benefits of climate and energy investments are or are not currently accruing. The DAC status not only helps in determining the eligibility for future Justice40-related investments but is also critical for exploring ways to achieve equitable distribution of resources. However, designing inclusive and equitable strategies not just requires a good understanding of current demographics, but also a deeper analysis of the transformations that happened in those demographics over the years. In this paper, machine learning (ML) models are trained on publicly available census data from recent years to classify the DAC status at the census tracts level and then the trained model is used to classify DAC status for historical years. A detailed analysis of the feature and model selection along with the evolution of disadvantaged communities between 2013 and 2018 is presented in this study.

Let's imagine the following scenario: you get a new project to work on. For this project, you need to develop a machine learning model, which will require running and training several experiments. Each experiment might take several hours or even days and needs to be tracked. You have your own laptop for the development phase, but it's not realistic to use your own laptop for training and running all of the experiments. First, your computer might not have the required hardware, for example, a GPU. Why train on one computer sequentially if you can run and train all the experiments in parallel?

AI and machine learning are very hot topics these days. These are only some of the applications that cannot exist without machine learning. But how can machines learn? I will show you how the magic works in this article, but I won't talk about neural networks! I will show you what is in the deepest deep of machine learning. One of the best presentations about machine learning is Fei Fei Li's TED talk.

Posted by Timothy Nguyen 1 , Research Engineer and Jaehoon Lee, Senior Research Scientist, Google Research For a machine learning (ML) a...

In this post, you will learn about the hold out method used during the process of training machine learning model. When evaluating machine learning (ML) models, the question that arises is whether the model is the best model available from the algorithm hypothesis space in terms of generalization error on the unseen / future data set. Whether the model is trained and tested using the most appropriate method. Out of available models, which model to select? These questions are taken care using what is called as hold out method.

I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Language is one of the hallmarks of human intelligence and one that plays a key role in our learning processes. By using language, we constantly formulate our understanding of a situation of a specific context.

You've spent hours fine-tuning your script, and you're racing to get it onto the server before your deadline tomorrow. You're building Naive Bayes, Logistic Regression, XGBoost, KNN, and any model under the sun in your massive for-loop.You've finally ironed out the kinks on your local machine, and you're ready to scale your precious script, but when it starts to run you see … what exactly? How do you know it's working? What would you do if it broke? How do you even know your models are doing what you want them to?

ML.NET allows .NET developers to easily build and also consume machine learning models in their NET applications. In this episode, Bri Achtman joins Rich to show off some really interesting scenarios that ML.NET and its family of tools enables. They talk about training models, AutoML, the ML.NET CLI, and even a Visual Studio Extension for training models!