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

PyTorch 1.0 inference in C++ on Windows10 platforms

Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences

Contrastive Learning of Structured World Models

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

InsCLR: Improving Instance Retrieval with Self-Supervision

TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction

MRQy is a quality assurance and checking tool for quantitative assessment of magnetic resonance imaging (MRI) data.

EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration

Repository providing a wide range of self-supervised pretrained models for computer vision tasks.

ElasticFace: Elastic Margin Loss for Deep Face Recognition

Heart Arrhythmia Classification

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Ivy is a templated deep learning framework which maximizes the portability of deep learning codebases.

Code accompanying paper: Meta-Learning to Improve Pre-Training

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