Pinned Repositories
acm-cheat-sheet
Acm Cheat Sheet
EvidentialNN
evidential neural network and different regularizations
HyperEvidentialNN
NestedMAML
code of NestedMAML
PLATINUM
self defined
ups
"In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning" by Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, Mubarak Shah (ICLR 2021)
Hugo101's Repositories
Hugo101/HyperEvidentialNN
Hugo101/PLATINUM
self defined
Hugo101/EvidentialNN
evidential neural network and different regularizations
Hugo101/NestedMAML
code of NestedMAML
Hugo101/ups
"In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning" by Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, Mubarak Shah (ICLR 2021)
Hugo101/acm-cheat-sheet
Acm Cheat Sheet
Hugo101/awesome-graph-embedding
A collection of important graph embedding, classification and representation learning papers with implementations.
Hugo101/Brynhildr
A repository for team project of CS6360
Hugo101/CIS521
Intro to Artificial Intelligence (Don't steal my code bro!)
Hugo101/d2l-en
Dive into Deep Learning, Berkeley STAT 157 (Spring 2019) textbook. With code, math, and discussions.
Hugo101/d2l-zh
《动手学深度学习》，英文版即伯克利深度学习（STAT 157，2019春）教材。面向中文读者、能运行、可讨论。
Hugo101/deeplearningbook-chinese
Deep Learning Book Chinese Translation
Hugo101/generative-models
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
Hugo101/Hugo101
Hugo101/hugo101.github.io
Hugo101/lihang-code
《统计学习方法》的代码实现
Hugo101/machine-learning-notes
My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (1000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(1000+页)和视频链接
Hugo101/mit-deep-learning-book-pdf
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
Hugo101/myGenerativeAI
Hugo101/Python
My Python Examples
Hugo101/python-small-examples
告别枯燥，致力于打造 Python 富有体系且实用的小例子、小案例。
Hugo101/ReHession
Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach