marooncabbage

marooncabbage's Stars

• vega/altair

  Declarative statistical visualization library for Python

  Language:Python9.4k1402k795

• cure-lab/LTSF-Linear

  [AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"

  Language:Python2k17112447

• qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM

  A professional list on Large (Language) Models and Foundation Models (LLM, LM, FM) for Time Series, Spatiotemporal, and Event Data.

  92129369

• WallKiller-glitch/V2raySSSSRShare

  科学上网，vpn机场推荐，支持shadowrocket，ss, ssr, v2ray, trojan, clash,clashr,需要自取（每日更新）

  7746040

• DAMO-DI-ML/NeurIPS2023-One-Fits-All

  The official code for "One Fits All: Power General Time Series Analysis by Pretrained LM (NeurIPS 2023 Spotlight)"

  Language:Python47655169

• microsoft/causica

  Language:Python441144155

• stefanradev93/BayesFlow

  A Python library for amortized Bayesian workflows using generative neural networks.

  Language:Python347206950

• RManLuo/reasoning-on-graphs

  Official Implementation of ICLR 2024 paper: "Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning"

  Language:Python32191641

• biomedia-mira/deepscm

  Repository for Deep Structural Causal Models for Tractable Counterfactual Inference

  Language:Jupyter Notebook2709953

• GFNOrg/gfn-lm-tuning

  Language:Jupyter Notebook1273919

• Valentyn1997/CausalTransformer

  Code for the paper "Causal Transformer for Estimating Counterfactual Outcomes"

  Language:Python11761027

• scikit-mobility/DeepGravity

  a PyTorch implementation of the paper "Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information"

  Language:Jupyter Notebook843937

• vios-s/DiffAN

  Diffusion Models for Causal Discovery

  Language:Python815713

• JonathanCrabbe/Dynamask

  This repository contains the implementation of Dynamask, a method to identify the features that are salient for a model to issue its prediction when the data is represented in terms of time series. For more details on the theoretical side, please read our ICML 2021 paper: 'Explaining Time Series Predictions with Dynamic Masks'.

  Language:Python7521018

• chritoth/active-bayesian-causal-inference

  Active Bayesian Causal Inference (Neurips'22)

  Language:Python51318

• lazaratan/dyn-gfn

  DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks

  Language:Python502213

• CausalSim/Unbiased-Trace-Driven-Simulation

  Language:Python34016

• google-research/fooling-feature-visualizations

  Code for "Don't trust your eyes: on the (un)reliability of feature visualizations"

  Language:Jupyter Notebook31203

• tsinghua-fib-lab/OD_benckmark

  The benchmark related to the survey: An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques

  28402

• patrickrchao/DiffusionBasedCausalModels

  Language:Python23110

• psanch21/VACA

  Language:Python22115

• liangzhehan/CMOD

  Language:Python21132

• tsinghua-fib-lab/KSTDiff-Urban-flow-generation

  Official implementation of "Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising Diffusion"(SIGSPATIAL'23)

  Language:Python20313

• vanderschaarlab/DECAF

  DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks

  Language:Python16108

• Logan-Lin/DOT

  Implementation code for the model Diffusion-based Origin-destination Travel Time Estimation (DOT), proposed in the SIGMOD 2024 paper "Origin-Destination Travel Time Oracle for Map-based Services".

  Language:Python15213

• loooffeeeey/OD_forecasting_benchmark

  Language:Python8111

• DMIRLAB-Group/GCA

  Language:Python7215

• Valentyn1997/INFs

  Code for the paper "Normalizing Flows for Interventional Density Estimation"

  Language:Python7100

• tsinghua-fib-lab/ODForecasting

  Language:Python6101

• loooffeeeey/KDD2023

  Language:Python3101