Spatiotemporal Machine Learning

dyffusion

[NeurIPS 2023] A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting

torchTS

Time series forecasting with PyTorch

LIMO

generative model for drug discovery

Turbulent-Flow-Net

Turbulent flow network source code

Equivariant-Net

LieGAN

Teleportation-Optimization

[ICLR 2024] Improving Convergence and Generalization Using Parameter Symmetries

Dynamic-Adaptation-Network

AutoSTPP

Automatic Integration for Neural Spatio-Temporal Point Process models (AI-STPP) is a new paradigm for exact, efficient, non-parametric inference of point process. It is capable of learning complicated underlying intensity functions, like a damped sine wave.

CopulaCPTS

Code for Copula conformal prediction paper (ICLR 2024)

DeepSTPP

Spatiotemporal_UQ

Uncertainty Quantification for Deep Spatiotemporal Forecasting

Approximately-Equivariant-Nets

Multi-Fidelity-Deep-Active-Learning

MCD

Implementation of the ICML 2024 paper "Discovering Mixtures of Structural Causal Models from Time Series Data"

Hierarchical-Neural-Processes

LaLiGAN

Latent Space Symmetry Discovery.

PECCO

Probabilistic Equivariant Continuous COnvolution

FS-CAP

few-shot compound activity regression

Interactive-Neural-Process

Zihao-s-Toolbox

A toolbox of shared utilities across different projects.

AutoNPP

Efficient computation of temporal point process intensity using automatic integration

Gradient-Flow-Symmetry

[ICLR 2023] Symmetries, flat minima, and the conserved quantities of gradient flow

ir2rgb

IR to RGB video translation

MFRNP

[ICML 2024] Multi Fidelity Residual Neural Process

GPU-Benchmark

group-net

rose-stl-lab.github.io

UCSD RoseLab Server Documentation

DANP

Domain Adversarial Neural Process

LIMO-Plus

Follow-up study of LIMO by Vineet Thumuluri