Self-Supervised Vision Transformers for multiplexed imaging datasets
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Updated
Jun 10, 2024 - Python
Self-Supervised Vision Transformers for multiplexed imaging datasets
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
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.
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)
This project aims to implement the Scaled-Dot-Product Attention layer and the Multi-Head Attention layer using various Positional Encoding methods.
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.
This repository contain various types of attention mechanism like Bahdanau , Soft attention , Additive Attention , Hierarchical Attention etc in Pytorch, Tensorflow, Keras
Code for the runners up entry on the English subtask on the Shared-Task-On-Fighting the COVID-19 Infodemic, NLP4IF workshop, NAACL'21.
"Attention, Learn to Solve Routing Problems!"[Kool+, 2019], Capacitated Vehicle Routing Problem solver
Pytorch Implementation of Transformers
EMNLP 2018: Multi-Head Attention with Disagreement Regularization; NAACL 2019: Information Aggregation for Multi-Head Attention with Routing-by-Agreement
Code and Datasets for the paper "A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing", published on Nature Machine Intelligence in 2021.
Attention-based Induction Networks for Few-Shot Text Classification
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