Popular GitHub Repositories for Deit Iii

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

BioWordVec & BioSentVec: pre-trained embeddings for biomedical words and sentences

BlueBERT, pre-trained on PubMed abstracts and clinical notes (MIMIC-III).

Implementation of Vision Mamba from the paper: "Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model" It's 2.8x faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on high-res images

i. A practical application of Transformer (ViT) on 2-D physiological signal (EEG) classification tasks. Also could be tried with EMG, EOG, ECG, etc. ii. Including the attention of spatial dimension (channel attention) and *temporal dimension*. iii. Common spatial pattern (CSP), an efficient feature enhancement method, realized with Python.

[ECCV 2024] Improving 2D Feature Representations by 3D-Aware Fine-Tuning

HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision

Curiosity-driven Exploration by Self-supervised Prediction for Street Fighter III Third Strike

Paddle Large Scale Classification Tools，supports ArcFace, CosFace, PartialFC, Data Parallel + Model Parallel. Model includes ResNet, ViT, Swin, DeiT, CaiT, FaceViT, MoCo, MAE, ConvMAE, CAE.

IJCAI2021: Code for SafeDrug, MIMIC data processing, Medical code mapping

Library - Vanilla, ViT, DeiT, BERT, GPT

Code for the paper: Multi-Label Clinical Time-Series Generation via Conditional GAN (IEEE TKDE)

Learning from Discharge Summaries to extract mentioned diagnoses using Hierarchical Attention Model

2D Human Pose estimation using transformers. Implementation in Pytorch

Rare disease identification from free-text clinical notes with ontologies and weak supervision

Time-Sensitive Deep Learning for ICU Outcome Prediction Without Variable Selection or Cleaning.

Master's project

Jax/Flax implementation of DeiT and DeiT-III (ViT)

Early prediction of sepsis with gradient boosting (XGBoost) and deep learning (LSTM and GRU) using MIMIC-III data.

MEME: Generating RNN Model Explanations via Model Extraction