CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal Reasoning (视觉-语言因果推理开源框架)

Experimental library integrating LLM capabilities to support causal analyses

✨✨Latest Advances on Multimodal Large Language Models

Codes for Linear and Nonlinear Disambiguation Optimization (LANDO)

Non Stationary Dynamical Mode Decomposition

Python Dynamic Mode Decomposition

neural networks to learn Koopman eigenfunctions

Causal Inference and Discovery in Python by Packt Publishing

A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.

A general-purpose Python package for Koopman theory using deep learning.

Code release for "Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting" (NeurIPS 2022), https://arxiv.org/abs/2205.14415

Official implementation for "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting" (ICLR 2024 Spotlight), https://openreview.net/forum?id=JePfAI8fah

A Library for Advanced Deep Time Series Models.

Code release for "Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors" (NeurIPS 2023), https://arxiv.org/abs/2305.18803

Linear and non-linear spectral forecasting algorithms

A package for the sparse identification of nonlinear dynamical systems from data

Material for Pybrain 2020 MNE-Python workshop

A list of all public EEG-datasets

For running psychology and neuroscience experiments

EEG Experiments in Python

Some tutorials for students of neuroscience

Source code/webpage/demos for the What-If Tool

Time series explainability via self-supervised model behavior consistency

Self-supervised contrastive learning for time series via time-frequency consistency

Inference code for Llama models

Large Language Model Text Generation Inference