checkmate
Training neural networks in TensorFlow 2.0 with 5x less memory (by parasj)
spektral
Graph Neural Networks with Keras and Tensorflow 2. (by danielegrattarola)
checkmate | spektral | |
---|---|---|
1 | 3 | |
123 | 2,346 | |
- | - | |
1.8 | 5.7 | |
about 2 years ago | 4 months ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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.
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.
checkmate
Posts with mentions or reviews of checkmate.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-01-05.
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[R] New Paper from OpenAI: DALL·E: Creating Images from Text
So, like... a $45 microSD card? You don't have to load the whole model into memory to perform inference on it. Hell, there's even been some interesting research getting around the GPU memory bottleneck for training as well.
spektral
Posts with mentions or reviews of spektral.
We have used some of these posts to build our list of alternatives
and similar projects.
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New “distilled diffusion models” research can create high quality images 256x faster with step counts as low as 4
But I'm a kamikaze by nature. I'm already learning Keras and Spektral so that I can write GNN's to predict molecular properties.
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[D] GNN Architecture that inputs and outputs both edge and node features?
I'm aware of Spektral: https://graphneural.network/
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tf-based framework for graph neural networks?
Has any library emerged as the clear leader in the TensorFlow Graph Neural Network space? A quick search revealed Spektral.
What are some alternatives?
When comparing checkmate and spektral you can also consider the following projects:
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
graphtransformer - Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.
deepNOID - deepNOID, the binary music genre classifier which determines if what you're listening to really is NOIDED
SuperGluePretrainedNetwork - SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
Spectrum - Spectrum is an AI that uses machine learning to generate Rap song lyrics
DeepAA - make ASCII Art by Deep Learning