Name: Xiong Daowen
Type: User
Bio: My name is XiongDaowen, and my research field is brain computer interface. I hope to study with you!
Location: NO.2, Chongwen Road, Nan'an District, Chongqing, China
Blog: http://www.cqupt.edu.cn/
Xiong Daowen's Projects
AISystem 主要是指AI系统,包括AI芯片、AI编译器、AI推理和训练框架等AI全栈底层技术
A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code).
Code and Data for "Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Features"
For beginner, this will be the best start for VAEs, GANs, and CVAE-GAN. This contains AE, DAE, VAE, GAN, CGAN, DCGAN, WGAN, WGAN-GP, VAE-GAN, CVAE-GAN. All use PyTorch.
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
Using DNNs to build encoding models of EEG visual responses.
Using vision-language models to decode natural image perception from non-invasive brain recordings.
EEGraph: Convert EEGs to graphs with frequency and time-frequency domain connectivity measures.
Scripts for the emotion EEG, ECG, sociability ISRSA analysis
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
Notebooks and code relating to "Generative feedback explains distinct brain activity codes for seen and mental images"
IDDM (Industrial, landscape, animate...), support DDPM, DDIM, PLMS, webui and multi-GPU distributed training. Pytorch实现,生成模型,扩散模型,分布式训练
Tips for Writing a Research Paper using LaTeX
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
Multimodal Mixture-of-Experts VAE
[ICLR 2024] EEG-based image decoding. i. Propose a contrastive learning framework to align image and eeg. ii. Resolving brain activity for biological plausibility.
Some example scripts on pytorch
Examples of the use of PYG
Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch