Wendy0601

answer-retrieval

Sample application that shows you how to create your own answer retrieval application for StackExchange, using custom features from the Retrieve and Rank service.

Approximation_Algorithm

Bayesian_Approximate_Inference

This project apply the Gibbs sampling and mean field methods to compute the inference and MAP inference

Deep_learning_practice

These are the implement of some deep learning related algorithm based on tensorflow

Event-Identification-Simulation

Datasets Generation

libsvm

LSTM-Human-Activity-Recognition

Human activity recognition using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six categories (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING).

Machine-learning-for-proteins

Listing of papers about machine learning for proteins.

Mobile-Eye-Gaze-Estimation-with-Deep-Learning

A four layers CNN model is designed to estimate the eye gaze or the attention

PCAT-Power-Grid

Wendy0601/Mobile-Eye-Gaze-Estimation-with-Deep-Learning

A four layers CNN model is designed to estimate the eye gaze or the attention

Wendy0601/Bayesian_Approximate_Inference

This project apply the Gibbs sampling and mean field methods to compute the inference and MAP inference

Wendy0601/deeplearning-models

A collection of various deep learning architectures, models, and tips

Wendy0601/libsvm

Wendy0601/LSTM-Human-Activity-Recognition

Human activity recognition using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six categories (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING).

Wendy0601/Machine-learning-for-proteins

Listing of papers about machine learning for proteins.

Wendy0601/pandas_exercises

Practice your pandas skills!

Wendy0601/PCAT-Power-Grid

Wendy0601/Python_PSSE_disturbance

generator disturbance based on PSSE python

Wendy0601/2018

Wendy0601/academic-kickstart

Wendy0601/Benmark2023

We provide a collection of power grid datasets either realistic and simulated and the machine learning algorithms applied to the datasets as benchmarks, aiming at motivating more advanced and robust algorithms for the future realistic power grids.

Wendy0601/deeplearning-biology

A list of deep learning implementations in biology

Wendy0601/graph-based-deep-learning-literature

links to conference publications in graph-based deep learning

Wendy0601/handong1587.github.io

Wendy0601/Identifying-Overlapping-Successive-Events-Using-a-Shallow-Convolutional-Neural-Network

The codes and data of our paper of Wendy0601/Identifying-Overlapping-Successive-Events-Using-a-Shallow-Convolutional-Neural-Network" are included"

Wendy0601/KATE

Code accompanying the paper "KATE: K-Competitive Autoencoder for Text"

Wendy0601/manim

Animation engine for explanatory math videos

Wendy0601/manim-tutorial-CN

manim中文教程,如果想系统地学习一些用法欢迎进入我的疫情期间搭建的博客

Wendy0601/nmt

TensorFlow Neural Machine Translation Tutorial

Wendy0601/PPGN-Physics-Preserved-Graph-Networks

The increasing number of variable renewable energy (solar and wind power) causes power grids to have more abnormal conditions or faults. Faults may further trigger power blackouts or wildfires without timely monitoring and control strategy. Machine learning is a promising technology to accelerate the automation and intelligence of power grid monitoring systems. Unfortunately, the black-box machine learning methods are weak to the realistic challenges in power grids: low observation, insufficient labels, and stochastic environments. To overcome the vulnerability of black-box machine learning, we preserve the physics of power grids through graph networks to efficiently and accurately locate the faults even with limited observability and low label rates. We first calculate the graph embedding of power grid infrastructure by establishing a reduced graph network with the observed nodes, then efficiently locate the fault on the node level using the low-dimensional graph embedding. To augment the location accuracy at low label rates, we build another graph network representing the physical similarity of labeled and unlabeled data samples. Importantly, we provide the physical interpretations of the benefits of the graph design through a random walk equivalence. We conduct comprehensive numerical experiments in the IEEE 123-node. Our proposed method shows superior performance than three baseline classifiers for different fault types, label rates, and robustness to out-of-distribution (OOD) data. Additionally, we extend the proposed method to the IEEE 37-node benchmark system and validate the effectiveness of the proposed training strategy.

Wendy0601/Proximal_optimization

Wendy0601/RNN_manual_model

predict binary number with RNN model

Wendy0601/scikit-learn

scikit-learn: machine learning in Python

Wendy0601/Sequence_to_sequence_with_attention

a encoder-decoder model with attention component is built to predict the sequence according to the input

Wendy0601/STT3851ClassRepo

Web page for STT 3851

Wendy0601/test

Wendy0601/Wendy0601.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

Wendy0601/wrong_web

Wendy0601/xcodeGit

test the push function of xcode