Tutorial: Generative Modeling Explained
This tutorial on generative modeling is in part of Statistical Machine Learning Tutorial by Ying Nian Wu at UCLA Statistics. The tutorial goes over the key equations and algorithms for learning recent generative models, including energy-based models, diffusion/score-based models, autoregressive/flow-based models, VAEs, and GANs, and explains the connections between these models.
A Introductory Reading List for Artificial General Intelligence
Composed by BIGAI Rearch & Center for VCLA, originally for the students at the Elite Program of General AI (Tong Class), THU & PKU. This reading list is appropriate for those who want to learn about the foundations of AGI.
Awesome AGI & CoCoSci
An awesome & curated list for Artificial General Intelligence, an emerging inter-discipline field that combines artificial intelligence and computational cognitive sciences as majority, alone with probability and statistics, formal logic, cognitive and developmental psychology, computational philosophy, cognitive neuroscience, and computational sociology. Awesome AGI & CoCoSci is an all-in-one collection, consisting of recources from basic courses and tutorials, to papers and books around multiple topics. Both junior and senior researchers, whether learning, working on, or working around AGI, meet their interest here.
Beethoven 250th
An article on the music of Beethoven for his 250th birthday. 世事无常，音乐永恒——贝多芬诞辰250周年纪念日（简体中文撰写）。
Program Synthesis Tutorial
My slides for presentation at LAMDA Group, giving a coarse introduction to Program Synthesis, a sub-field of AI.
Machine Learning Note
Refined notes for Machine Learning, form both the probabilistic perspective and the logical perspective.
Linear Classifiers
Commons and distinctions between linear classifiers.本文比较了几种线性分类器的异同（中文撰写）。
Seminar: Few-Shot Bayesian Imitation Learning with Logical Program Policies
My slides for presentation at LAMDA Group, introducing Few-Shot Bayesian Imitation Learning with Logical Program Policies (AAAI'20)
Seminar: A Probabilistic Programming Language for Scene Perception
My slides for presentation at LAMDA Group, introducing Picture: A Probabilistic Programming Language for Scene Perception (CVPR'15)
Book Review: Deep Work
My notes on Deep Work by Cal Newport.
Seminar: G2SAT
My notes and slides for presentation at LAMDA Group, introducing G2SAT (NIPS'19)
A Paper List for Visual Object Tracking
A complete paper list of Single-Object Visual Tracking Algorithms, Surveys and Benchmarks of recent years. Different from existing paper list, this project doesn't simply category the papers by publishment, but from a tracking process perspective. Main Contributions and Novelties of each tracker paper is carefully studied, forming our taxonomy criteria. The investigation covers top conferences as AAAI, CVPR, ECCV, ICCV, ICML, IJCAI, NIPS and top journals as IJCV, TIP, PAMI, CSVT. Note that the list is not bijective, namely a single paper may appear in diverse contents.
Notes for Paper-Reading
Notes for papers on computer vision.
Visual Object Tracking Benchmarks
VOT 2019(ICCV 2019)
CVPR 2018
CMU-HUST-15210
This repository contains codes and lab report for CMU-HUST-15210: Parallel and Sequential Algorithms and Data Structure. The course is imported from Carnegie Mellon University for students selected in the ACM project. If you need to use the results or perspectives in my lab report, please cite the report..
Abductive Learning
Personal perspectives on Abductive Learning.
Probability Theory Note
Refined Note for Probability Theory and Mathematical Statistics.
Learning Computer Vision
Notes and Codes for learning OpenCV-Python..
Learning Plotly-Python
Notes and code for learning plotly-python.
Learning D3.js
My learning notes and source codes of fundamental d3js.
C Programming
My learning notes and source codes of C.