list of efficient attention modules
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Updated
Aug 23, 2021 - Python
list of efficient attention modules
Chatbot using Tensorflow (Model is transformer) ko
Implementation of "Attention is All You Need" paper
Flexible Python library providing building blocks (layers) for reproducible Transformers research (Tensorflow β , Pytorch π, and Jax π)
An experimental project for autonomous vehicle driving perception with steering angle prediction and semantic segmentation using a combination of UNet, attention and transformers.
Joint text classification on multiple levels with multiple labels, using a multi-head attention mechanism to wire two prediction tasks together.
A PyTorch Implementation of PGL-SUM from "Combining Global and Local Attention with Positional Encoding for Video Summarization", Proc. IEEE ISM 2021
A Transformer Encoder where the embedding size can be down-sized.
GPT model that can take a text file from anywhere on the internet and imitate the linguistic style of the text
Synthesizer Self-Attention is a very recent alternative to causal self-attention that has potential benefits by removing this dot product.
Deployed locally
PyTorch implementation of the Transformer architecture from the paper Attention is All You Need. Includes implementation of attention mechanism.
Official implementation of the paper "FedLSF: Federated Local Graph Learning via Specformers"
A repository for implementations of attention mechanism by PyTorch.
3D Printing Extrusion Detection using Multi-Head Attention Model
The implementation of transformer as presented in the paper "Attention is all you need" from scratch.
Transformer model based on the research paper: "ππππ²π»ππΆπΌπ» ππ ππΉπΉ π¬πΌπ π‘π²π²π±"
This package is a Tensorflow2/Keras implementation for Graph Attention Network embeddings and also provides a Trainable layer for Multihead Graph Attention.
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