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 graphical models and potential outcomes frameworks.
machine-learning
data-science
causal-inference
causality
treatment-effects
bayesian-networks
causal-machine-learning
causal-models
do-calculus
graphical-models
python3
Обновлено 2024-11-21 01:27:34 +03:00
An official implementation for " UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation"
video
localization
segmentation
caption-task
coin
joint
msrvtt
multimodal-sentiment-analysis
multimodality
pretrain
pretraining
retrieval-task
video-language
video-text
video-text-retrieval
youcookii
alignment
caption
Обновлено 2024-07-25 14:07:31 +03:00
the grand unified configuration system
Обновлено 2024-03-29 22:52:17 +03:00
This project provides an official implementation of our recent work on real-time multi-object tracking in videos. The previous works conduct object detection and tracking with two separate models so they are very slow. In contrast, we propose a one-stage solution which does detection and tracking with a single network by elegantly solving the alignment problem. The resulting approach achieves groundbreaking results in terms of both accuracy and speed: (1) it ranks first among all the trackers on the MOT challenges; (2) it is significantly faster than the previous state-of-the-arts. In addition, it scales gracefully to handle a large number of objects.
Обновлено 2023-10-04 00:42:58 +03:00
MonitoFi: Health & Performance Monitor for your Apache NiFi
Обновлено 2023-08-02 02:16:38 +03:00