Popular GitHub Repositories for Threshold

Still struggling with the high threshold or looking for the appropriate baseline? Come here and new starters can also play with your own multi-agents!

Implementation of MTAD-GAT: Multivariate Time-series Anomaly Detection via Graph Attention Network

Soft Threshold Weight Reparameterization for Learnable Sparsity

Hsin-Rung Chou, Jia-Hong Lee, Yi-Ming Chan, and Chu-Song Chen, "Data-specific Adaptive Threshold for Face Recognition and Authentication," IEEE International Conference on Multimedia Information Processing and Retrieval, MIPR 2019

??? This one stop project is a complete COVID-19 detection package comprising of 3 tasks: ? Task 1 --> COVID-19 Classification ? Task 2 --> COVID-19 Infection Segmentation ? Task 3 --> Lung Segmentation

Privacy-preserving federated learning is distributed machine learning where multiple collaborators train a model through protected gradients. To achieve robustness to users dropping out, existing practical privacy-preserving federated learning schemes are based on (t, N)-threshold secret sharing. Such schemes rely on a strong assumption to guarantee security: the threshold t must be greater than half of the number of users. The assumption is so rigorous that in some scenarios the schemes may not be appropriate. Motivated by the issue, we first introduce membership proof for federated learning, which leverages cryptographic accumulators to generate membership proofs by accumulating users IDs. The proofs are issued in a public blockchain for users to verify. With membership proof, we propose a privacy-preserving federated learning

Statistics, signal processing, finance, econometrics, manufacturing, networking[disambiguation needed] and data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.

A project to train your model from scratch or fine-tune a pretrained model using the losses provided in this library to improve out-of-distribution detection and uncertainty estimation performances. Calibrate your model to produce enhanced uncertainty estimations. Detect out-of-distribution data using the defined score type and threshold.

BIOBOT: A Fall Detection System (FDS) using Artificial Intelligence

Promises and Pitfalls of Threshold-based Auto-labeling (NeurIPS 2023, Spotlight)

Trading system that aims to buy stocks when they are low and sell stock when they have risen. The time horizon for trades is 2 - 45 days. The strategy has been successfully backtested in version 0.2. Next step: forward test strategy to ensure that it works in practice. Test RL to see if we can can improve results (currently only ~ 5 % of max.)

Maat is a framework that mines VirusTotal scan reports to extract various information about the correctness, completeness, and consistency of scanners. It can also be used to train threshold-based and ML-based labeling strategies to label (Android) apps according to their VirusTotal scan reports and much more. Maat also refers to the ancient Egyptian concepts of truth, balance, order, harmony.

Pixelwise classification of wounds in laboratory rat images: A comparison between the threshold methods, Random Forest algorithm and the UNet deep model

This project demonstrates how to build a Convolutional Neural Network (CNN) model for anomaly detection in time series data using Keras. It is implemented in Google Colab and uses a CSV dataset containing time series values. The model detects anomalies based on reconstruction errors by setting a dynamic threshold.

Image and Video Analysis & Processing

This github repository contains the official code for the papers, "Robustness Assessment for Adversarial Machine Learning: Problems, Solutions and a Survey of Current Neural Networks and Defenses" and "One Pixel Attack for Fooling Deep Neural Networks"

Athlete Injury Risk Analyzer predicts injury risks using symmetry metrics (force, impulse, torque). It classifies athletes into risk categories with machine learning models and offers a user-friendly Streamlit app for data upload and predictions.

Implementation of computer vision algorithms and image processing using Numpy & OpenCV

Simulation and implementation of hybrid blockwise and adaptive threshold receiver architectures for MIMO systems with one-bit ADCs, based on the paper: "MIMO Networks With One-Bit ADCs: Receiver Design and Communication Strategies", IEEE Trans. on Communications, vol. 70, no. 3, Mar. 2022.

This repository contains a simple implementation of a threshold pricing agent using the Q-learning algorithm. The agent learns to set optimal threshold prices in a simulated environment to maximize rewards.