Search Results for "stochastic"

An open-source stack for generative modeling and probabilistic inference. Gen’s inference library gives users building blocks for writing efficient probabilistic inference algorithms that are tailored to their models, while automating the tricky math and the low-level implementation details. Gen helps users write hybrid algorithms that combine neural networks, variational inference, sequential Monte Carlo samplers, and Markov chain Monte Carlo. Gen features an easy-to-use modeling language...

...Solving forward/inverse integro-differential equations (IDEs) [SIAM Rev.] fPINN: solving forward/inverse fractional PDEs (fPDEs) [SIAM J. Sci. Comput.] NN-arbitrary polynomial chaos (NN-aPC): solving forward/inverse stochastic PDEs (sPDEs) [J. Comput. Phys.] PINN with hard constraints (hPINN): solving inverse design/topology optimization [SIAM J. Sci. Comput.] Residual-based adaptive sampling [SIAM Rev., arXiv] Gradient-enhanced PINN (gPINN) [Comput. Methods Appl. Mech. Eng.] PINN with multi-scale Fourier features [Comput. Methods Appl. Mech. Eng.]

darts is a Python library for easy manipulation and forecasting of time series. It contains a variety of models, from classics such as ARIMA to deep neural networks. The models can all be used in the same way, using fit() and predict() functions, similar to scikit-learn. The library also makes it easy to backtest models, combine the predictions of several models, and take external data into account. Darts supports both univariate and multivariate time series and models. The ML-based models...

...Augmentor is a real-time image augmentation library designed to render the process of artificial dataset enlargement more convenient, less error prone, and easier to reproduce. It offers the user the ability to build a stochastic image-processing pipeline (or simply augmentation pipeline) using image operations as building blocks. In other words, an augmentation pipeline is little more but a sequence of operations for which the parameters can (but need not) be random variables, as the following code snippet demonstrates.

Neural Processes (NPs) is a collection of interactive Jupyter/Colab notebook implementations developed by Google DeepMind, showcasing three foundational probabilistic machine learning models: Conditional Neural Processes (CNPs), Neural Processes (NPs), and Attentive Neural Processes (ANPs). These models combine the strengths of neural networks and stochastic processes, allowing for flexible function approximation with uncertainty estimation. They can learn distributions over functions from data and efficiently make predictions at new inputs with calibrated uncertainty — making them useful for few-shot learning, Bayesian regression, and meta-learning. Each notebook includes theoretical explanations, key building blocks, and executable code that runs directly in Google Colab, requiring no local setup. ...

TF Quant Finance is a high-performance library of quantitative finance components built on TensorFlow, aimed at research and production workloads. It implements pricing engines, risk measures, stochastic models, optimizers, and random number generators that are differentiable and vectorized for accelerators. Users can value options and fixed-income instruments, simulate paths, fit curves, and calibrate models while leveraging TensorFlow’s jit compilation and automatic differentiation. The codebase is organized as modular math and finance primitives so you can combine building blocks or target end-to-end examples. ...

Code of this library is partialy based on myCNN MATLAB class written by Nikolay Chemurin.

RNNLIB is a recurrent neural network library for sequence learning problems. Applicable to most types of spatiotemporal data, it has proven particularly effective for speech and handwriting recognition. full installation and usage instructions given at http://sourceforge.net/p/rnnl/wiki/Home/