Learning meaningful latent space representations for patient risk ...

Deep Patient: An Unsupervised Representation to Predict the Future ...

Here we show that unsupervised deep feature learning applied to pre-process patient-level aggregated EHR data results in representations that ...

[D] Is VAE still worth it? : r/MachineLearning - Reddit

VAE are still broadly used to learn compact and useful representations, while diffusion models are used for generative purposes. What fell out ...

Learning a confidence score and the latent space of a new ...

This new method has the advantage of providing a confidence score for each prediction thanks to a softmax classifier and a meaningful latent ...

machine learning - What is a latent space? - Cross Validated

The motivation to learn a latent space (set of hidden topics/ internal representations) over the observed data (set of events) is that large ...

[D] How to choose dimensions for latent space ? : r/MachineLearning

I thought of using Autoencoders to get the latent representation of the data and then apply probably k-means and see the results. How can I choose the latent ...

Perioperative Predictions with Interpretable Latent Representation

(1) To overcome performance limitations of traditional VAE, it isprediction-guided with explicit expression of predicted outcome in the latent ...

Representation learning algorithms for inferring machine ...

When separating the device size as an independent latent variable with dedicated regression objectives, similar to separation of domain and ...

A Latent Space Metric for Enhancing Prediction Confidence in Earth ...

We take advantage of a Variational AutoEncoder architecture, to derive a confidence metric by the latent space representations of EO datasets.

Representation Learning With Autoencoders For Electronic Health ...

training to match the distribution of an arbitrary prior on the latent space. Pros. • ... risk factor in cardiovascular disease and has significant cost of ...

Deep representation learning of electronic health records to unlock ...

We introduce a representation learning model based on word embeddings, convolutional neural networks, and autoencoders (i.e., ConvAE) to ...

Interpreting clinical latent representations using autoencoders and ...

... Healthcare Lecture Notes in Computer Science, p. 75-91. Scopus. Crossref citations: 0 · Learning meaningful latent space representations for patient risk ...

Autoencoders and their applications in machine learning: a survey

... meaningful representation of the data in the latent ... learn compressed image representations for grouping similar images in the latent space.

What is a Variational Autoencoder? - IBM

The primary training objective for any autoencoder is for it to learn how to efficiently model the latent space of a particular input. Latent ...

Set Prediction in the Latent Space | Connected Papers Search

We propose a method for learning the distance function by performing the matching in the latent space learned from encoding networks. This ...

Dynamically Meaningful Latent Representations of ... - MDPI

In this study, we present a data-driven hybrid modeling approach to help tackle this problem by combining numerically derived representations and latent ...

Generative models and their latent space - The Academic

Learning Representations: The generative model learns to extract meaningful features and representations from the data as it maps them to the ...

Using Autoencoders for Feature Selection - Hex

These latent space representations can be used to select a subset of features for training an AI model. This can improve the performance of the ...

Time series in healthcare: challenges and solutions

To the best of our knowledge, ASSM is the first deep probabilistic model that provides clinically meaningful latent representations, with non- ...

Representation Learning Using Information Maximizing Diffusion ...

InfoDiffusion produces semantically meaningful latent space for a diffusion model. (Top) Smooth latent space. (Bottom) Disentangled, human-interpretable factors ...

Variational Autoencoders and Investment Forecasting: How to Learn ...

... learn a low-dimensional latent space representation of input data ... - Example: Imagine a 2D latent space where one axis represents risk ...