GAT VS Perceiver

Compare GAT vs Perceiver and see what are their differences.

GAT

Graph Attention Networks (https://arxiv.org/abs/1710.10903) (by PetarV-)
graph-attention-networks attention-mechanism self-attention Tensorflow neural-networks Python
Source Code
petar-v.com
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Perceiver

Implementation of Perceiver, General Perception with Iterative Attention in TensorFlow (by Rishit-dagli)
Deep Learning Artificial intelligence neural-network Transformers attention-mechanism Python Tensorflow attention Transformer Machine Learning
Source Code
rishit-dagli.github.io
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GAT Perceiver
2 7
3,045 85
- -
0.0 2.6
about 2 years ago about 3 years ago
Python Python
MIT License 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.
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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.

GAT

Posts with mentions or reviews of GAT. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-09.

Perceiver

Posts with mentions or reviews of Perceiver. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-04-15.

What are some alternatives?

When comparing GAT and Perceiver you can also consider the following projects:

pytorch-GAT - My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!

Swin-Transformer-Object-Detection - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.

awesome-graph-classification - A collection of important graph embedding, classification and representation learning papers with implementations.

performer-pytorch - An implementation of Performer, a linear attention-based transformer, in Pytorch

how_attentive_are_gats - Code for the paper "How Attentive are Graph Attention Networks?" (ICLR'2022)

Fast-Transformer - An implementation of Fastformer: Additive Attention Can Be All You Need, a Transformer Variant in TensorFlow

bottleneck - Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"

TimeSformer-pytorch - Implementation of TimeSformer from Facebook AI, a pure attention-based solution for video classification

CrabNet - Predict materials properties using only the composition information!

gato - Unofficial Gato: A Generalist Agent

deepmind-perceiver - My implementation of DeepMind's Perceiver

conformer - Implementation of the convolutional module from the Conformer paper, for use in Transformers

GAT vs pytorch-GAT Perceiver vs Swin-Transformer-Object-Detection GAT vs awesome-graph-classification Perceiver vs performer-pytorch GAT vs how_attentive_are_gats Perceiver vs Fast-Transformer GAT vs bottleneck Perceiver vs TimeSformer-pytorch GAT vs CrabNet Perceiver vs gato Perceiver vs deepmind-perceiver Perceiver vs conformer