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anh-tong

Config files for my GitHub profile.

anh-tong.github.io

A beautiful, simple, clean, and responsive Jekyll theme for academics

awesome-neural-ode

A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.

bqtorch

An implementation of Bayesian Quadrature based on GPytorch

csdi

Codes for "CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation"

diffrax

Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable.

edward

A library for probabilistic modeling, inference, and criticism. Deep generative models, variational inference. Runs on TensorFlow.

equinox

Callable PyTrees and filtered transforms => neural networks in JAX. https://docs.kidger.site/equinox/

eqxvision

A Python package of computer vision models for the Equinox ecosystem.

fbm

flam

Fast linear algebra in MATLAB

fractional_neural_sde

Fractional White Noises for Neural Stochastic Differential Equations (NeurIPS 2022)

george

Solver for IBP latent Gaussian Process in Python

gpflow

Gaussian processes in TensorFlow

gpss-research

Relational Automatic Statistician for discovering shared kernels and disctintive kernels

horseshoe_gp

hyper-opt

Bilevel Hyperparameter Optimization with Implicit Differentiation

levanter

Legibile, Scalable, Reproducible Foundation Models with Named Tensors and Jax

nanogpt-equinox

nanoGPT using Equinox

nn-gp-models

A collection of models having connections between Gaussian processes and neural networks

papers

Collection of papers

scalable-ibp-gp

scale-limit-hawkes

The difference of two point processes converges to Brownian motion

second

Some notes of second-order optimization, Hessian, Fisher matrix

sigformer

SigFormer: Signature Transformer for Deep Hedging (ICAIF 2023)

sigkax

Implementation of Signature Kernel in JAX

signax

Signax: Signature computation in JAX

soft-dtw-jax

Soft-DTW implementation in JAX with custom gradient

stan_playground

Play with Stan MC. Including some implementations of recent GP models

tensorflow

Computation using data flow graphs for scalable machine learning