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

Adjusting for Autocorrelated Errors in Neural Networks for Time Series

Self-Supervised Document-to-Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference

Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation (ICCV 2021)

PyToch implementation of A Novel Self-supervised Learning Task Designed for Anomaly Segmentation

Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

DeepLab resnet v2 model in pytorch

Styleformer - Official Pytorch Implementation

A variational Bayesian method for similarity learning in non-rigid image registration (CVPR 2022)

MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution (CVPR2021)

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.

Python implementation of the multistate Bennett acceptance ratio (MBAR)

Simple PyTorch implementations of Badnets on MNIST and CIFAR10.

Re-implememtation of MAE (Masked Autoencoders Are Scalable Vision Learners) using PyTorch.

Official PyTorch implementation of paper: Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation (ICCV 2021 Oral Presentation)

Replication Package for "An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets"

Deep Networks with Recurrent Layer Aggregation

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Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation

PyTorch code for the NAACL 2021 paper "Improving Generation and Evaluation of Visual Stories via Semantic Consistency"

PyTorch implementation of ICLR 2022 paper PiCO: Contrastive Label Disambiguation for Partial Label Learning