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Official implementation for "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting" (ICLR 2024 Spotlight), https://openreview.net/forum?id=JePfAI8fah
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
Riemannian Adaptive Optimization Methods with pytorch optim
An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).
X-KANeRF [KANeRF-benchmarking]: KAN based NeRF with various basis functions like B-Splines, Fourier, Gaussians, Wavelets, Polynomials, etc
End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification (IEEE Transactions on Biomedical Engineering)
This is the PyTorch implementation of the FBMSNet architecture for EEG-MI classification.
EEGdenoiseNet, a benchmark dataset, that is suited for training and testing deep learning-based EEG denoising models, as well as for comparing the performance across different models.
Rethinking CNN Architecture for Enhancing Decoding Performance of Motor Imagery-based EEG Signals
A research repository of deep learning on electroencephalographic (EEG) for Motor imagery(MI), including eeg data processing(visualization & analysis), papers(research and summary), deep learning m…
MONAI Generative Models makes it easy to train, evaluate, and deploy generative models and related applications
Code for the paper published in Deep Generative Models for Health Workshop at the Neurips 2023.
A ViT based transformer applied on multi-channel time-series EEG data for motor imagery classification
This may be the simplest implement of DDPM. You can directly run Main.py to train the UNet on CIFAR-10 dataset and see the amazing process of denoising.
Official PyTorch Implementation of "Scalable Diffusion Models with Transformers"
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python