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Name: Jesse
Type: User
Name: Jesse
Type: User
AHB (Adaptive Hyper-box) R package provides two main algorithms: AHB_fast_match and AHB_MIP_match for Interpretable Individualized Treatment Effect Estimation.
A Python Package providing two algorithms, DAME and FLAME, for fast and interpretable treatment-control matches of categorical data
FLAME_db is a Python package for performing matching for observational causal inference on datasets containing discrete covariates. It implements the Fast, Large-Scale Almost Matching Exactly (FLAME) algorithms using SQL quries, which match treatment and control units on subsets of the covariates. FLAME_db scales to huge datasets with millions of observations where existing state-of-the-art methods fail, and that it achieves significantly better performance than other matching methods. The resulting matched groups are interpretable, because the matches are made on covariates, and high-quality, because machine learning is used to determine which covariates are important to match on.
We decreased the class imbalance ratio of the leaf dataset to 205 from 415 using LeafImageEditor. The copy/paste data augmentation decreased class imbalance ratio to 50. It helps us to get much better performance on Leaf damage semantic segmentation. The code of algorithm will be published later.
The official PyTorch implementation of the paper "Optical Flow Training under Limited Label Budget via Active Learning" (ECCV 2022)
label-smooth, amsoftmax, focal-loss, triplet-loss, lovasz-softmax. Maybe useful
We want to find a method to train neural networks with the smallest number of crossbars. Sparse neural networks trained based on The Lottery Ticket Hypothesis enable us to save even 90% of crossbars for training.
This is an Android application which enables multiple users to play RISK GAME online concurrently.
This is a wireless scoring board to help blind employees to rate the quality of products in Firmenich Flavor Lab
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.