Stars
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphic…
ISSRE 2019: Generic and Robust Localization of Multi-Dimensional Root Cause
[VLDB 2023] Model Selection for Anomaly Detection in Time Series
A simple program to calculate and visualize the FLOPs and Parameters of Pytorch models, with handy CLI and easy-to-use Python API.
Code for Neural Information Processing Systems (NeurIPS) 2019 paper: Pareto Multi-Task Learning
Automatic architecture search and hyperparameter optimization for PyTorch
GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.
An LLM-powered advanced RAG pipeline built from scratch
Scalable and user friendly neural 🧠 forecasting algorithms.
NeuralProphet: A simple forecasting package
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Fast and Accurate ML in 3 Lines of Code
Merlion: A Machine Learning Framework for Time Series Intelligence
An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730
TCube generates rich and fluent narratives that describes the characteristics, trends, and anomalies of any time-series data (domain-agnostic) using the transfer learning capabilities of PLMs.
A large scale latent variable compartmental model for simulating abundance/population time series of interacting phages and microbes that undergoes lysis in presence of the phages.
This repository contains the implementations related to the experiments of a set of publicly available datasets that are used in the time series forecasting research space.
Neural relational inference for interacting systems - pytorch
ClaSPy: A Python package for time series segmentation.
This repository contains the time series segmentation benchmark (TSSB).
🛠️ Corrected Test Sets for ImageNet, MNIST, CIFAR, Caltech-256, QuickDraw, IMDB, Amazon Reviews, 20News, and AudioSet