mrymaltin's starred repositories
iTransformer
Official implementation for "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting" (ICLR 2024 Spotlight), https://openreview.net/forum?id=JePfAI8fah
COVID-19-age-groups
Interpreting age groups impact on COVID-19 using deep learning timeseries models
Transfer-Learning-Library
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
neuralforecast
Scalable and user friendly neural :brain: forecasting algorithms.
Time-Series-Library
A Library for Advanced Deep Time Series Models.
multi-output-glucose-forecasting
The code used for the paper Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories published in KDD 2018
annotated_deep_learning_paper_implementations
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
Vision_Transformer
Vision Transformer (ViT) is a type of neural network architecture that has been introduced to address the problem of image classification. Unlike traditional convolutional neural networks (CNNs), which rely on convolutions to extract local features from an image, ViT employs a self-attention mechanism to extract global features for classification.
Vision-Transformer-ViT
Pytorch ViT for Image classification on the CIFAR10 dataset
pytorch-vit-minimum
Vision-Transformer minimum implementation for Classification, Image Captioning, Object Detection, etc.
TransBTS
This repo provides the official code for : 1) TransBTS: Multimodal Brain Tumor Segmentation Using Transformer (https://arxiv.org/abs/2103.04430) , accepted by MICCAI2021. 2) TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images(https://arxiv.org/abs/2201.12785).
2D-Medical-Denoising-Diffusion-Probabilistic-Model-
This is the repository for the paper "2D Medical Image Synthesis Using Transformer-based Denoising Diffusion Probabilistic Model".