Name: Tony
Type: User
Company: Institute of automation, Chinese academy of science
Bio: A master student in Univercity of Chinese Academy of Science
Location: Haidian district ,Beijing, China
Blog: https://Casia_Dominic
Tony's Projects
清华大学计算机系考研攻略 Guidance for postgraduate entrance examination in Department of Computer Science and Technology, Tsinghua University
[CVPR'24] Aerial Lifting: Neural Urban Semantic and Building Instance Lifting from Aerial Imagery
MLNLP社区用来更好进行论文搜索的工具。Fully-automated scripts for collecting AI-related papers
Audio generation using diffusion models, in PyTorch.
Apply diffusion models using the new Hugging Face diffusers package to synthesize music instead of images.
List of AI Residency Programs
A comprehensive list of Implicit Representations and NeRF papers relating to Robotics/RL domain, including papers, codes, and related websites
笃志前行——一个四非一本院校学子的保研之路
Prepare for 保研机试 for THU, PKU. Welcome to PKU.
基于 ChatGPT API 的文本翻译、文本润色、语法纠错 Bob 插件,让我们一起迎接不需要巴别塔的新时代!
Brain EEG signal data augmentation using synthetic signals generated by a single step diffusion probabilistic model.
自动化所硕博论文模板
中科院科研工作专用ChatGPT,特别优化学术Paper润色体验,支持自定义快捷按钮,支持markdown表格显示,Tex公式双显示,代码显示功能完善,新增本地Python工程剖析功能/自我剖析功能
EEG generation by VAE
计算机自学指南
Codes for "CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation"
:bar_chart: 数据挖掘常用算法:关联分析Apriori算法,数据分类决策树算法,数据聚类K-means算法
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为15个章节,近20万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系[email protected] 版权所有,违权必究 Tan 2018.06
Deep learning model for EEG artifact removal
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.
A time singal diffusion model implement shared by wzy
Pytorch implementation of Diffusion Models (https://arxiv.org/pdf/2006.11239.pdf
DiffWave is a fast, high-quality neural vocoder and waveform synthesizer.
GAN and VAE implementations to generate artificial EEG data to improve motor imagery classification. Data based on BCI Competition IV, datasets 2a. Final project for UCLA's EE C247: Neural Networks and Deep Learning course.
LSTM-GAN for generate plausible ECG signals
EEG Artifact Removal Using Deep Learning (source code, IEEE Journal of Biomedical and Health Informatics)
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.