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.
See also my more aspirational paper reading list.
Hydrology
home internet stuff
Foundations
GP research
- Regression-based covariance functions for nonstationary spatial modeling
- kalman-jax/sde_gp.py at master · AaltoML/kalman-jax
- Probability Theory (For Scientists and Engineers)
- Scaling multi-output Gaussian process models with exact inference · Invenia Blog
- wesselb/stheno: Gaussian process modelling in Python
- GPyTorch
SDEs in optimisation and learning
To explore: Gradient flows gradient flow.
- ModelingToolkit, Modelica, and Modia: The Composable Modeling Future in Julia - Stochastic Lifestyle
Statistical Inference via Convex Optimization.
Conjugate functions illustrated.
Francis Bach on the use of geometric sums and a different take by Julyan Arbel.
Tutorial to approximating differentiable control problems. An extension of this is universal differential equations.
- mitmath/18303: 18.303 - Linear PDEs course
- JuliaSim - Julia Computing
- dnncode/Spatio-Temporal-Model-for-SPDE: Statistical Modeling for Spatio-Temporal Data from Physical Convection-Diffusion Processes
- Generalizing Automatic Differentiation to Automatic Sparsity, Uncertainty, Stability, and Parallelism - Stochastic Lifestyle
- Score-Based Generative Modeling through Stochastic Differential Equations
- DiffEqFlux.jl: Generalized Physics-Informed and Scientific Machine Learning (SciML) · DiffEqFlux.jl
- [2001.04385] Universal Differential Equations for Scientific Machine Learning
- Seminars & Workshops | DATA DRIVEN SCIENCE & ENGINEERING
- 2007.09660.pdf
- JuliaSim - Julia Computing
- SciML: Open Source Software for Scientific Machine Learning* random_fields.pdf
- How can the general Green’s function of a linear homogeneous differential equation be derived? - Mathematics Stack Exchange
- GitHub - mitmath/18303 at spring19
- 978-0-387-21540-2_8.pdf
- Empirical Arithmetic Averaging Over the Compact Stiefel Manifold - IEEE Journals & Magazine
- Structured Random Matrices
- kar12Supple.pdf
- 2010.00746.pdf
- JB_grothendieck_proof.pdf
Ai for music fun pack
- neuralfunkv2 - Google Drive
- neuralfunkv2.ipynb - Colaboratory
- GitHub - chrisdonahue/wavegan: WaveGAN: Learn to synthesize raw audio with generative adversarial networks
- AI generates endless break beats (NeuralFunk vol.2) - YouTube
https://djtechtools.com/2020/07/14/best-ai-platforms-to-help-you-make-music/
https://www.patreon.com/loudlystudio
Open AI Jukebox is the latest hot generative music thing that I should be across. I would personally take a rather different approach to them to solve this problem, but they are the current benchmark.
Misc
Jaan on translating between variational terminology in physics and ML
Francis Bach, the many faces of integration by parts.
Efficiently sampling functions from Gaussian process posteriors
https://jaan.io/what-is-variational-autoencoder-vae-tutorial/
Whittle likelihood
“Sethuraman” rep of Dirichlet proc = stick breaking
Bubeck on hot results in learning theory takes him far from the world of mirror descent. Also lectures well, IMO.
SDEs
- ICERM SciML conf
- [1912.05509] The Wasserstein-Fourier Distance for Stationary Time Series
- Verdinelli , Wasserman : Hybrid Wasserstein distance and fast distribution clustering
- Hilbert space methods for reduced-rank Gaussian process regression
- willtebbutt/TemporalGPs.jl: Fast inference for Gaussian processes in problems involving time
- 18.337J/6.338J: Parallel Computing and Scientific Machine Learning
- 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
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