Machine learning development toolkit built upon Transformer encoder network architectures and tailored for the realm of high-energy physics and particle-collision event analysis.
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Updated
May 15, 2024 - Python
Machine learning development toolkit built upon Transformer encoder network architectures and tailored for the realm of high-energy physics and particle-collision event analysis.
several types of attention modules written in PyTorch
Self-Supervised Vision Transformers for multiplexed imaging datasets
完整的原版transformer程序,complete origin transformer program
This repository contains code for implementing Vision Transformer (ViT) model for image classification
[VLDB'22] Anomaly Detection using Transformers, self-conditioning and adversarial training.
HydraViT is a PyTorch implementation of the HydraViT model, an adaptive multi-branch transformer for multi-label disease classification from chest X-ray images. The repository provides the necessary code to train and evaluate the HydraViT model on the NIH Chest X-ray dataset.
Exploring attention weights in transformer-based models with linguistic knowledge.
A Basic Multi layered Neural Network, With Attention Masking Features
The Transformer model implemented from scratch using PyTorch. The model uses weight sharing between the embedding layers and the pre-softmax linear layer. Training on the Multi30k machine translation task is shown.
Multi^2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT (Findings of ACL: EMNLP 2020)
Transformer translator website with multithreaded web server in Rust
This project aims to implement the Scaled-Dot-Product Attention layer and the Multi-Head Attention layer using various Positional Encoding methods.
A Faster Pytorch Implementation of Multi-Head Self-Attention
Image Captioning with Encoder as Efficientnet and Decoder as Decoder of Transformer combined with the attention mechanism.
This is the official repository of the original Point Transformer architecture.
PyTorch implementation of some attentions for Deep Learning Researchers.
A Transformer Classifier implemented from Scratch.
Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR.
Attention is all you need: Discovering the Transformer model
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