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

Last update: Nov 28, 2022

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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

Controlling a game using mediapipe hand tracking

Pytorch Implementation of paper "Noisy Natural Gradient as Variational Inference"

Trajectory Prediction with Graph-based Dual-scale Context Fusion

git《Commonsense Knowledge Base Completion with Structural and Semantic Context》(AAAI 2020) GitHub: [fig1]

FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset (CVPR2022)

This repository contains code and data for "On the Multimodal Person Verification Using Audio-Visual-Thermal Data"

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This is the paddle code for SeBoW(Self-Born wiring for neural trees), a kind of neural tree born form a large search space

bio_inspired_min_nets_improve_the_performance_and_robustness_of_deep_networks

Codes for our IJCAI21 paper: Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization

Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes

Implementation of Memory-Compressed Attention, from the paper "Generating Wikipedia By Summarizing Long Sequences"

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