Probably actually reading

Music

Nestup / cutelabnyc/nested-tuplets: Fancy javascript for manipulating nested tuplets.

Hydrology

• GeoStat-Framework/welltestpy: A python-package for handling well based field campaigns.
• welltestpy Tutorial — welltestpy 1.0.3 documentation

home internet stuff

• UPnP - Official Kodi Wiki
• Settings/Services/UPnP DLNA - Official Kodi Wiki
• How to Flash Your NETGEAR Router with DD-WRT: 5 Easy Steps
• DD-WRT
• DD-WRT » Community
• Installation - DD-WRT Wiki
• Firmware FAQ - DD-WRT Wiki

Foundations

• Product Placement
• (Not So) Free Samples | Michael Betancourt on Patreon
• Sampling Case Study Live Stream Wed 4/14 at 2 PM EDT | Michael Betancourt on Patreon
• Updated Geometric Optimization Paper | Michael Betancourt on Patreon
• We Built Sparse City | Michael Betancourt on Patreon
• Modeling Sparsity Case Study Live Stream Tue 6/8 at 2 PM EDT | Michael Betancourt on Patreon

GP research

• Bayesian inference with INLA
• R-INLA Project
• Linear Models from a Gaussian Process Point of View with Stheno and JAX · Invenia Blog
• 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

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.deepjams.com/

https://evokemusic.ai/

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

• How to write a great research paper - Microsoft Research

• AaltoML/kalman-jax: Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX

• Cheng Soon Ong, Marc Peter Deisenroth | There and Back Again: A Tale of Slopes and Expectations · SlidesLive

• David Duvenaud, J. Zico Kolter, Matt Johnson | Deep Implicit Layers: Neural ODEs, Equilibrium Models and Beyond · SlidesLive

• Overview · ADCME

• Encoder Autonomy | Machine Thoughts

• The Notion of “Double Descent” | Mad (Data) Scientist

• 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

• Bubeck on hot results in learning theory takes him far from the world of mirror descent. Also lectures well, IMO.

• Causality for Machine Learning

learning SDEs

• [1912.05509] The Wasserstein-Fourier Distance for Stationary Time Series
• [2001.04385] Universal Differential Equations for Scientific Machine Learning
• 18.337J/6.338J: Parallel Computing and Scientific Machine Learning
• 2007.09660.pdf
• 2010.00746.pdf
• 978-0-387-21540-2_8.pdf
• AaltoML/SDE: Example codes for the book Applied Stochastic Differential Equations
• An Implicit/Explicit CUDA-Accelerated Solver for the 2D Beeler-Reuter Model
• DiffEqFlux.jl – A Julia Library for Neural Differential Equations
• DiffEqFlux.jl: Generalized Physics-Informed and Scientific Machine Learning (SciML) · DiffEqFlux.jl
• DifferentialEquations.jl: Scientific Machine Learning (SciML) Enabled Simulation and Estimation · DifferentialEquations.jl
• dnncode/Spatio-Temporal-Model-for-SPDE: Statistical Modeling for Spatio-Temporal Data from Physical Convection-Diffusion Processes
• Empirical Arithmetic Averaging Over the Compact Stiefel Manifold - IEEE Journals & Magazine
• Generalizing Automatic Differentiation to Automatic Sparsity, Uncertainty, Stability, and Parallelism - Stochastic Lifestyle
• GitHub - mitmath/18303 at spring19
• Global Sensitivity Analysis · DifferentialEquations.jl
• Hilbert space methods for reduced-rank Gaussian process regression
• How can the general Green’s function of a linear homogeneous differential equation be derived? - Mathematics Stack Exchange
• ICERM SciML conf
• Introduction to Scientific Machine Learning 2: Physics-Informed Neural Networks - YouTube
• Introduction to Scientific Machine Learning through Physics-Informed Neural Networks
• JB_grothendieck_proof.pdf
• JuliaSim - Julia Computing
• kar12Supple.pdf
• Kolmogorov Backward Equations
• mitmath/18303: 18.303 - Linear PDEs course
• ModelingToolkit, Modelica, and Modia: The Composable Modeling Future in Julia - Stochastic Lifestyle
• Neural SDEs: Deep Generative Models in the Diffusion Limit - Maxim Raginsky - YouTube
• NeuralNetDiffEq.jl: A Neural Network solver for ODEs
• Noise Processes · DifferentialEquations.jl
• Parameter Estimation and Bayesian Analysis · DifferentialEquations.jl
• Physics-Informed Neural Networks solver · NeuralPDE.jl
• SciML: Open Source Software for Scientific Machine Learning* random_fields.pdf
• 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
• SciML/NeuralPDE.jl: Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
• SciML/SciMLTutorials.jl: Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
• Score-Based Generative Modeling through Stochastic Differential Equations
• Seminars & Workshops | DATA DRIVEN SCIENCE & ENGINEERING
• Solving Systems of Stochastic PDEs and using GPUs in Julia - Stochastic Lifestyle
• Structured Random Matrices
• Verdinelli , Wasserman : Hybrid Wasserstein distance and fast distribution clustering
• willtebbutt/TemporalGPs.jl: Fast inference for Gaussian processes in problems involving time

General emulation

Sensitivity Analysis

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

SDEs in optimisation

To explore: Gradient flows gradient flow.

Nguyen and Malinsky (2020)

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.

PDEs

• Bringing PDEs to JAX with forward and reverse modes automatic differentiation

References