Stat718 Point Process Notes
忘记概念的时候,回来多看几遍
Part 1: Fundamentals of Point Processes
1.1 What is a Point Process?
A point process is a random collection of events—or “points”—occurring in a mathematical space such as:
A 1D timeline (e.g., times of phone calls),
A 2D spatial domain (e.g., locations of trees, crimes, or disease cases),
Or a 3D space-time continuum (...
Spatial Analysis Of Fatal Traffic Accidents Using Poisson Point Process
With Xiuchuan Liu and Xunan Yang, two talented Ph.D. students in statistics at the University of South Carolina.
1. Dataset and data preprocess
(1) Data from SCDOT(South Carolina Department of Transportation)
Road shapefile (including highway, interstates, etc)
Traffic count (Average Daily Traffic)
Link: https://info2.scdot.org/GISMappi...
Clip(remoteclip) Finetung
For Complete PDF(english): remote_clip_finetuning.pdf
For Complete Code(pytorch): github contact: email; much thanks to this great work: remoteclip
What is CLIP?
CLIP is a model that fundamentally changes how machines understand images by training on a combination of image and text data. This method, known as contrast learning, involves...
Intelligent Bear Protection System
5/20/2024 UPDATE: We installed our system on the Tibetan Plateau in Qinghai, up to 4,500 meters high!
Article Link (Chinese): Group Meeting
Editing & Layouts: Ziyue GUO
PPT Link (English): Intelligent Bear Protection System
Editing & Layouts: Pengyu CHEN
Vedio Demo:
The ongoing conflict between residents in the Qinghai-T...
Non Maximum Suppression
Non Maximum Suppression: Theory and Implementation in PyTorch (EN & CN)
Non Maximum Suppression(NMS) Is a technique used in numerous computer vision tasks. It is a class of algorithms to select one entity(e.g., bouding boxes) out of many overlapping entites. We can choose the selection criteria to arrive at the desired results. The criteria ...
Few-shot classification model with PyTorch
In 15 minutes and just a few lines of code, we are going to implement the Prototypical Networks. It’s the favorite method of many few-shot learning researchers (~2000 citations in 3 years), because 1) it works well, and 2) it’s incredibly easy to grasp and to implement.
Discovering Prototypical Networks
First, let’s install the tutorial GitHub ...