Stuff that I am currently actively reading. If you are looking at this, and you aren’t me you should really be re-evaluating your hobbies.
Misc
- pyro SVI
- Jaan on translating between variational terminology in physics and ML
- Jaan on VAE
- pamamakouros on normalizing flows
- Eric Jang on normalizing flows
- Sander on typicality
- Sander on waveform audio
- yuge shi’s ELBO gradient post is excellent
- Francis Bach, the many faces of integration by parts.
- Efficiently sampling functions from Gaussian process posteriors
- Temenin on Riemannian GPs
- https://jaan.io/what-is-variational-autoencoder-vae-tutorial/
- Whittle likelihood
- “Sethuraman” rep of Dirichlet proc = stick breaking
SDEs
- [1912.05509] The Wasserstein-Fourier Distance for Stationary Time Series
- 2011.04026.pdf
- Verdinelli , Wasserman : Hybrid Wasserstein distance and fast distribution clustering
- Hilbert space methods for reduced-rank Gaussian process regression | SpringerLink
- [1907.04502] DeepXDE: A deep learning library for solving differential equations
- Aalto: Log in to the site
- willtebbutt/TemporalGPs.jl: Fast inference for Gaussian processes in problems involving time
- 18.337J/6.338J: Parallel Computing and Scientific Machine Learning | 18337
- AaltoML/SDE: Example codes for the book Applied Stochastic Differential Equations
- Introduction to Scientific Machine Learning through Physics-Informed Neural Networks
- Neural SDEs: Deep Generative Models in the Diffusion Limit - Maxim Raginsky - YouTube
- Introduction to Scientific Machine Learning 2: Physics-Informed Neural Networks - YouTube
- DiffEqFlux.jl – A Julia Library for Neural Differential Equations
- SciML: Open Source Software for Scientific Machine Learning
- SciML/DiffEqFlux.jl: Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
- DifferentialEquations.jl: Scientific Machine Learning (SciML) Enabled Simulation and Estimation · DifferentialEquations.jl
- Parameter Estimation and Bayesian Analysis · DifferentialEquations.jl
- SciML/SciMLTutorials.jl: Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
- Kolmogorov Backward Equations
- An Implicit/Explicit CUDA-Accelerated Solver for the 2D Beeler-Reuter Model
- Solving Systems of Stochastic PDEs and using GPUs in Julia - Stochastic Lifestyle
- Noise Processes · DifferentialEquations.jl
- Global Sensitivity Analysis · DifferentialEquations.jl
- SciML/DiffEqFlux.jl: Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
- NeuralNetDiffEq.jl: A Neural Network solver for ODEs
- SciML/NeuralPDE.jl: Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
- Physics-Informed Neural Networks solver · NeuralPDE.jl
General emulation
Spectral bizness
- QuantEcon/lecture-source-jl: Source files for "Lectures in Quantitative Economics" -- Julia version
- mschauer/Kalman.jl: Flexible filtering and smoothing in Julia
- QuantEcon.jl/kalman.jl at master · QuantEcon/QuantEcon.jl
- Whittle.pdf
- time_series - spectral_estimation.pdf
- mschauer/Kalman.jl: Flexible filtering and smoothing in Julia
- A First Look at the Kalman Filter – Quantitative Economics with Julia
- How a Kalman filter works, in pictures | Bzarg
Deep GP
PDEs
parallelism
- Keynote: Anyscale Product Demo - Edward Oakes, Software Engineer, Anyscale - YouTube
- What is Ray? — Ray v1.0.0
- 10x Faster Parallel Python Without Python Multiprocessing | by Robert Nishihara | Towards Data Science
pytorch
- https://pytorch.org/tutorials/beginner/pytorch_with_examples.html
- http://pyro.ai/examples/gp.html
- http://pyro.ai/examples/forecasting_ii.html
- http://pyro.ai/examples/forecasting_iii.html
- http://pyro.ai/examples/dkl.html
- http://pyro.ai/examples/svi_part_i.html
- http://pyro.ai/examples/svi_part_ii.html
- http://pyro.ai/examples/svi_part_iii.html
- http://docs.pyro.ai/en/latest/contrib.timeseries.html (Note the Matern GP)
- http://pyro.ai/examples/timeseries.html
- http://pyro.ai/examples/tensor_shapes.html
- http://docs.pyro.ai/en/dev/contrib.gp.html#combination