mlsanityData to 14 Oct 2026

TorchSurv: A Lightweight Package for Deep Survival Analysis

arXiv 2404.10761 ↗First posted 16 Apr 2024cs.LGcited by 5

Not on the map. Why?

Abstract (arXiv)

TorchSurv is a Python package that serves as a companion tool to perform deep survival modeling within the PyTorch environment. Unlike existing libraries that impose specific parametric forms, TorchSurv enables the use of custom PyTorch-based deep survival models. With its lightweight design, minimal input requirements, full PyTorch backend, and freedom from restrictive survival model parameterizations, TorchSurv facilitates efficient deep survival model implementation and is particularly beneficial for high-dimensional and complex input data scenarios.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 5 of its 27 references that are in the map. First posted on arXiv 16 Apr 2024.

Builds on

5 of 27 references.

References · 5

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  3. 2016DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Networkcited by 358
  4. 2019Time-to-Event Prediction with Neural Networks and Cox Regressioncited by 175
  5. 2022auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Datacited by 17

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025Curia: A Multi-Modal Foundation Model for Radiologycited by 15
  2. 2025NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantificationcited by 5
  3. 2025Let the Experts Speak: Improving Survival Prediction & Calibration via Mixture-of-Experts Headscited by 1
  4. 2025KANFormer for Predicting Fill Probabilities via Survival Analysis in Limit Order Bookscited by 0
  5. 2026Fusion is not one-size-fits-all: Cross-Modal Representation Alignment for Time-to-Event Modelingcited by 0