📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.

Last update: Jan 17, 2022

Overview

Weird Deep Learning Metrics

📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.

Metric	Description	Notes
Churn	Shows the disagreement between two model's prediction. Higher the churn, more the disagreement. Bounds: [0,1]. Takes two prediction arrays as inputs and retuns churn percentage.	From this paper

Owner

Rahul Vigneswaran

GitHub Repository

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📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.

Related tags

Overview

Weird Deep Learning Metrics

Owner

Rahul Vigneswaran

In this project I played with mlflow, streamlit and fastapi to create a training and prediction app on digits

A Simple Key-Value Data-store written in Python

一套完整的微博舆情分析流程代码，包括微博爬虫、LDA主题分析和情感分析。

BlueFog Tutorials

Code corresponding to The Introspective Agent: Interdependence of Strategy, Physiology, and Sensing for Embodied Agents

Colossal-AI: A Unified Deep Learning System for Large-Scale Parallel Training

Starter Code for VALUE benchmark

Context-Sensitive Misspelling Correction of Clinical Text via Conditional Independence, CHIL 2022

The all new way to turn your boring vector meshes into the new fad in town; Voxels!

A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning", IJCAI-21

Bayesian algorithm execution (BAX)

(CVPR2021) ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

Simulator for FRC 2022 challenge: Rapid React

UT-Sarulab MOS prediction system using SSL models

Implementation of the paper NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting.

Streamlit App For Product Analysis - Streamlit App For Product Analysis

Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image

Predicting Auction Sale Price using the kaggle bulldozer auction sales data: Modeling with Ensembles vs Neural Network

RITA is a family of autoregressive protein models, developed by LightOn in collaboration with the OATML group at Oxford and the Debora Marks Lab at Harvard.

Over9000 optimizer