jodie VS PaddleHelix

Compare jodie vs PaddleHelix and see what are their differences.

jodie

A PyTorch implementation of ACM SIGKDD 2019 paper "Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks" (by claws-lab)
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jodie PaddleHelix
2 1
340 792
2.9% 1.5%
0.0 5.4
21 days ago 2 months ago
Python Python
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

jodie

Posts with mentions or reviews of jodie. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-26.

PaddleHelix

Posts with mentions or reviews of PaddleHelix. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing jodie and PaddleHelix you can also consider the following projects:

YassQueenDB - Graph database library that allows you to store, analyze, and search through your data in a graph format. By using the Universal Sentence Encoder, it provides an efficient and semantic approach to handle text data. 📚🧠🚀

transferlearning - Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

dipy - DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.

typedb-ml - TypeDB-ML is the Machine Learning integrations library for TypeDB

DeBERTa - The implementation of DeBERTa

eirli - An Empirical Investigation of Representation Learning for Imitation (EIRLI), NeurIPS'21

Unsupervised-Classification - SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]