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

Tutorial materials for Part of NSU Intro to Deep Learning with PyTorch.

Development Kit for the SoccerNet Challenge

[CVPR 2021] Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion

This repo provides the official code for TransBTS: Multimodal Brain Tumor Segmentation Using Transformer (https://arxiv.org/pdf/2103.04430.pdf).

Semantic Segmentation for Aerial Imagery using Convolutional Neural Network

Code accompanying "Adaptive Methods for Aggregated Domain Generalization"

A Simple Framwork for CV Pre-training Model (SOCO, VirTex, BEiT)

Code and Experiments for ACL-IJCNLP 2021 Paper Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering.

Official code for "Decoupling Zero-Shot Semantic Segmentation"

Mememoji - A facial expression classification system that recognizes 6 basic emotions: happy, sad, surprise, fear, anger and neutral.

Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR

Minimal implementation and experiments of "No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging".

We propose a new method for effective shadow removal by regarding it as an exposure fusion problem.

Breast cancer is been classified into benign tumour and malignant tumour.

🕺Full body detection and tracking

MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks.

Automatic Idiomatic Expression Detection

Code for our ICCV 2021 Paper "OadTR: Online Action Detection with Transformers".

PyTorch implementations of the paper: "Learning Independent Instance Maps for Crowd Localization"

Code for "Learning the Best Pooling Strategy for Visual Semantic Embedding", CVPR 2021