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

This project aim to create multi-label classification annotation tool to boost annotation speed and make it more easier.

A simple Rock-Paper-Scissors game using CV in python

Implementation of UNET architecture for Image Segmentation.

Code for the paper: Sketch Your Own GAN

An updated version of virtual model making

Implementation for Learning to Track with Object Permanence

scikit-learn: machine learning in Python

Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks

A high-performance distributed deep learning system targeting large-scale and automated distributed training.

DiffQ performs differentiable quantization using pseudo quantization noise. It can automatically tune the number of bits used per weight or group of weights, in order to achieve a given trade-off between model size and accuracy.

DiSECt: Differentiable Simulator for Robotic Cutting

Released code for Objects are Different: Flexible Monocular 3D Object Detection, CVPR21

The code for the NeurIPS 2021 paper "A Unified View of cGANs with and without Classifiers".

Official pytorch code for SSC-GAN: Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation(ICCV 2021)

Causal-Adversarial-Instruments - PyTorch Implementation for Developing Library of Investigating Adversarial Examples on A Causal View by Instruments

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Highly comparative time-series analysis

Code for the AAAI 2022 paper "Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph".