There are 1 repository under bottleneck topic.
Quickly identify what's slow with WordPress
A Simple Traffic Generator for Hyperledger Fabric
Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"
Moore Machine Networks (MMN): Learning Finite-State Representations of Recurrent Policy Networks
SKYProfiler is a performance monitoring tool for Integration Server. SKYProfiler tracks the service invocations and the monitored data can be seen in real time. This helps users track the time each service invocation takes and further drills down to the child service to identify which service contributes to time.
Tensorflow implementation of deep variational information bottleneck
This project consists of C++ implementations of a 3D Rapidly Exploring Random Tree and three other extensions called RRT*, Execution Extended RRT and Synchronised Greedy Biased RRT. It also includes a heuristically guided RRT* with biased sampling towards relevant bottleneck points predicted by a 3D CNN(modified VoxNet in Tensorflow).
simple ABM program to simulate a moving danger (e.g., fire) and people in a confined space trying to escape the danger
A check-in system that uses QR codes and email notifications to track attendance at events. It includes a backend server built with express, a data processing script for generating QR codes and IDs, and a frontend scanner built with react and react-scan-qr. The system sends emails using nodemailer and limits the rate of sending with bottleneck.
99.7% accuracy solution for Dogs vs Cats Redux Kaggle competition
Visualize the Latent Space of an Autoencoder using matplotlib
Python-based code for estimation of highway bottleneck probability using speed transition matrices.
Finding bottlenecks in applications -- an example
A PyTorch toolkit for 2D Human Pose Estimation.
Autoencoders are mostly used for different purposes such as denoising, compression data, anomaly detection, generating new data from the input data entering to the model, and more. This repository introduces a simple autoencoder architecture with some brief explanations of encoder, bottleneck and decoder parts.
Autoencoder dimensionality reduction, EMD-Manhattan metrics comparison and classifier based clustering on MNIST dataset
:wrench: Performance Optimization Project - Simulated real-world scenario where a Desktop VR application must be optimized for release
Zabbix Graphs Bottleneck Classification automates bottleneck analysis in network infrastructure using deep learning and the Zabbix monitoring system. It quickly identifies and classifies bottlenecks, enabling proactive network management and optimization.
⭐⭐⭐ Pytorch implementation of Attentiom, Backbone, ViT, MLP, Re-parameter, Convolution, very flexible module combination.
3 part project: A. bottleneck autoencoder, B. manhattan distance, C. earth mover's distance
Generate labels for (wine) bottle neck
A small experiment with convolutional neural network in keras.
[This project was completed in September 2020] The GML-Net is a convolutional neural network (CNN) that is based on U-Net architecture with an encoder derived from the ResNet family and BottleNeck blocks that provide reading and aggregation of feature maps from a cross-section of various scales. Effective network learning is ensured by loss function defined as a weighted sum of Binary Cross-Entropy Loss, Dice Loss and Lovász hinge Loss.
A binary+library to measure how much time is spent reading vs writing.
Logs times of page creations and intermediate results to spot bottlenecks in Islandora stack.
This is the Algorithm to detect the Handwritten Digits - Autoencoders
Autoencoder dimensionality reduction, EMD-Manhattan metrics comparison and classifier based clustering on MNIST dataset.