Deep Conditional Transformation Models

Conditional transformation models for survivor function estimation.

In survival analysis, the estimation of patient-specific survivor functions that are conditional on a set of patient characteristics is of special interest. In general, knowledge of the conditional survival probabilities of a patient at all relevant time points allows better assessment of the patient's risk than summary statistics, such as median survival time. Nevertheless, standard methods fo...

Conditional Probability Models for Deep Image Compression

Deep Neural Networks trained as image auto-encoders have recently emerged as a promising direction for advancing the state of the art in image compression. The key challenge in learning such networks is twofold: to deal with quantization, and to control the trade-off between reconstruction error (distortion) and entropy (rate) of the latent image representation. In this paper, we focus on the l...

Learning Structured Output Representation using Deep Conditional Generative Models

Supervised deep learning has been successfully applied to many recognition problems. Although it can approximate a complex many-to-one function well when a large amount of training data is provided, it is still challenging to model complex structured output representations that effectively perform probabilistic inference and make diverse predictions. In this work, we develop a deep conditional ...

XML Document Transformation with Conditional Random Fields

We address the problem of structure mapping that arises in xml data exchange or xml document transformation. Our approach relies on xml annotation with semantic labels that describe local tree editions. We propose xml Conditional Random Fields (xcrfs), a framework for building conditional models for labeling xml documents. We equip xcrfs with efficient algorithms for inference and parameter est...

Conditional Logit Models