WayneDW/Jax_implementation_of_CSGLD

Stars
2
Language
Python
License
Apache License 2.0
Created almost 2 years ago
Updated over 1 year ago

WayneDW/Jax_implementation_of_CSGLD

WayneDW

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Test purposes

Sentiment-Analysis-in-Event-Driven-Stock-Price-Movement-Prediction

Use NLP to predict stock price movement associated with news

DeepLight_Deep-Lightweight-Feature-Interactions

Accelerating Inference for Recommendation Systems (WSDM'21)

Contour-Stochastic-Gradient-Langevin-Dynamics

An elegant adaptive importance sampling algorithms for simulations of multi-modal distributions (NeurIPS'20)

Jupyter Notebook

Automated-Equity-Asset-Selection-and-Allocation

Optimal portfolio selection

Bayesian-Sparse-Deep-Learning

Code for An Adaptive Empirical Bayesian Method for Sparse Deep Learning (NeurIPS'19)

Parallel-Solvers-for-Linear-System

Parallel Solver for Large-Scale Sparse Matrix Computations

More-than-Algorithms

Refresh algorithms using C++ and Python

Bayesian-Data-Analysis

An alternative to frequentist inference

two_sigma_financial_modeling

Kaggle Competition

Variance_Reduced_Replica_Exchange_SGMCMC

Variance reduction in energy estimators accelerates the exponential convergence in deep learning (ICLR'21)

Interacting-Contour-Stochastic-Gradient-Langevin-Dynamics

A pleasantly parallel adaptive importance sampling algorithms for simulations of multi-modal distributions (ICLR'22)

Jupyter Notebook

Variational_Schrodinger_Diffusion_Model

A multi-variate transport-optimized diffusion models accelerated by simulation-free properties (ICML'24)

Jupyter Notebook

Non-reversible-Parallel-Tempering-for-Deep-Posterior-Approximation

Code for "Non-reversible Parallel Tempering for Deep Posterior Approximation (AAAI 2023)"

Global-Optimization-and-Monte-Carlo-Simulation-via-An-Adaptively-Weighted-Stochastic-Gradient-MCMC

Code for "An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization (Statistics and Computing 2022)"

Learn_Schrodinger_Bridge

Learn others code for practice

Notes