The GitHub repository for the paper "Informer" accepted by AAAI 2021.

Porting vision models to Keras 3 for easily accessibility. Contains MobileViT v1.

A engine for the Orca AI architechure, written in Python.

This collection of notebooks is based on the Dive into Deep Learning Book. All of the notes are written in Pytorch and the d2l/torch library

An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites

[MIR-2023-Survey] A continuously updated paper list for multi-modal pre-trained big models

Repo for ML Models built from scratch such as Self-Attention, Linear +Logistic Regression, PCA, LDA. CNN, LSTM, Neural Networks using Numpy only

《李宏毅深度学习教程》（李宏毅老师推荐👍），PDF下载地址：https://github.com/datawhalechina/leedl-tutorial/releases

Effinformer: A Deep-Learning-Based Data-Driven Modeling of DC–DC Bidirectional Converters (Published in: IEEE Transactions on Instrumentation and Measurement (*IEEE TIM*))

An unofficial pytorch implementation of 'Efficient Infinite Context Transformers with Infini-attention'

Semantic segmentation is an important job in computer vision, and its applications have grown in popularity over the last decade.We grouped the publications that used various forms of segmentation in this repository. Particularly, every paper is built on a transformer.

Joint detection of Object and its Semantic parts using Attention-based Feature Fusion on PASCAL Parts 2010 dataset

Keras, PyTorch, and NumPy Implementations of Deep Learning Architectures for NLP

[WACV 2024] Separable Self and Mixed Attention Transformers for Efficient Object Tracking

[Biomedical Signal Processing and Control] ECGTransForm: Empowering adaptive ECG arrhythmia classification framework with bidirectional transformer

DSMIL: Dual-stream multiple instance learning networks for tumor detection in Whole Slide Image

[ICLR 2024] Official PyTorch implementation of FasterViT: Fast Vision Transformers with Hierarchical Attention

The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516

Do Transformers Really Perform Bad for Graph Representation? [NIPS-2021]