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Found 10 papers, 5 papers with code
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
We propose a framework for probabilistic forecasting of dynamical systems based on generative modeling.
SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
We present Scalable Interpolant Transformers (SiT), a family of generative models built on the backbone of Diffusion Transformers (DiT).
Stochastic interpolants with data-dependent couplings
In this work, using the framework of stochastic interpolants, we formalize how to \textit{couple} the base and the target densities, whereby samples from the base are computed conditionally given samples from the target in a way that is different from (but does preclude) incorporating information about class labels or continuous embeddings.
A dynamic risk score for early prediction of cardiogenic shock using machine learning
We developed a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict onset of cardiogenic shock.
Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions
For example, extending the inference process with auxiliary variables leads to improved sample quality.
Survival Mixture Density Networks
Survival MDN applies an invertible positive function to the output of Mixture Density Networks (MDNs).
Learning Invariant Representations with Missing Data
Enforcing such independencies requires nuisances to be observed during training.
Inverse-Weighted Survival Games
When the loss is proper, we show that the games always have the true failure and censoring distributions as a stationary point.
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models
Deep generative models (DGMs) seem a natural fit for detecting out-of-distribution (OOD) inputs, but such models have been shown to assign higher probabilities or densities to OOD images than images from the training distribution.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
X-CAL: Explicit Calibration for Survival Analysis
A survival model's calibration can be measured using, for instance, distributional calibration (D-CALIBRATION) [Haider et al., 2020] which computes the squared difference between the observed and predicted number of events within different time intervals.
