SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

Last update: Nov 28, 2022

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Deep Learning survITE

Overview

SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

Authors: Alicia Curth*, Changhee Lee*, and Mihaela van der Schaar (* co-first authors)
Paper will be publsihed at NeurIPS 2021.

Owner

Changhee Lee, Ph.D. Candidate at UCLA

GitHub Repository

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SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

Related tags

Overview

SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

Owner

Edge-aware Guidance Fusion Network for RGB-Thermal Scene Parsing

Motion and Shape Capture from Sparse Markers

Jarvis Project is a basic virtual assistant that uses TensorFlow for learning.

Convex optimization for fun and profit.

Official Pytorch implementation of C3-GAN

Semiconductor Machine learning project

PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility

Deep Watershed Transform for Instance Segmentation

Large-Scale Unsupervised Object Discovery

Consecutive-Subsequence - Simple software to calculate susequence with highest sum

Computer-Vision-Paper-Reviews - Computer Vision Paper Reviews with Key Summary along Papers & Codes

Some code of the implements of Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network

A list of multi-task learning papers and projects.

Teaching end to end workflow of deep learning

Code for "Typilus: Neural Type Hints" PLDI 2020

The code for our NeurIPS 2021 paper "Kernelized Heterogeneous Risk Minimization".

Codes for realizing theories learned from Data Mining, Machine Learning, Deep Learning without using the present Python packages.

Pytorch implementation of

Collection of common code that's shared among different research projects in FAIR computer vision team.

Self-supervised Deep LiDAR Odometry for Robotic Applications