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.
revisions
SS
- Source separation and anomaly detection filters - Online LaTeX Editor Overleaf
- Gaussian Processes: from one to many outputs · Invenia Blog
- Implementing a scalable multi-output GP model with exact inference · Invenia Blog
- Scaling multi-output Gaussian process models with exact inference · Invenia Blog
gd
Message passing
Foundations
- Identity Crisis
- Invited Talk - Michael Bronstein · SlidesLive
- 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
D R MacIver
I have been enjoying some of the explanatory style of David R MacIver recently.
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
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.
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.
References
Nguyen, Long, and Andy Malinsky. 2020. “Exploration and Implementation of Neural Ordinary Differential Equations,” 34.
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