Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography

No abstract is available for this paper.

• Publication year

  2019

• Venue

  International Conference on Machine Learning

• Publication date

  2019-05-24

• Fields of study

  Medicine, Computer Science

• Identifiers

  No identifiers available.

• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• The Building Blocks of Interpretability

  2018cited by this paper
• Interpretable VAEs for nonlinear group factor analysis

  2018cited by this paper
• How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation

  2018cited by this paper
• Supplement : Semi-Supervised Prediction-Constrained Topic Models

  2018cited by this paper
• The (Un)reliability of saliency methods

  2017cited by this paper
• Generating and designing DNA with deep generative models

  2017cited by this paper
• Latent Space Oddity: on the Curvature of Deep Generative Models

  2017cited by this paper
• Fixing a Broken ELBO

  2017cited by this paper
• Hybrid Deep Discriminative/Generative Models for Semi-Supervised Learning

  2017cited by this paper
• Towards A Rigorous Science of Interpretable Machine Learning

  2017cited by this paper
• Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

  2016cited by this paper
• Variational Inference: A Review for Statisticians

  2016cited by this paper
• Synthesizing the preferred inputs for neurons in neural networks via deep generator networks

  2016influential reference
• Discriminative Regularization for Generative Models

  2016cited by this paper
• “Why Should I Trust You?”: Explaining the Predictions of Any Classifier

  2016cited by this paper
• Programs as Black-Box Explanations

  2016cited by this paper
• Adam: A Method for Stochastic Optimization

  2014cited by this paper
• Stochastic Backpropagation and Approximate Inference in Deep Generative Models

  2014cited by this paper
• Auto-Encoding Variational Bayes

  2013cited by this paper
• Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

  2013influential reference
• Guiding Scientific Discovery with Explanations Using DEMUD

  2013cited by this paper
• Changes in ECG pattern with advancing age

  2011cited by this paper
• How to Explain Individual Classification Decisions

  2009cited by this paper
• A Unified Approach

  2005cited by this paper
• Real time electrocardiogram QRS detection using combined adaptive threshold

  2004cited by this paper
• The Tradeoff Between Generative and Discriminative Classifiers

  2004cited by this paper
• Bayesian Data Analysis

  2004cited by this paper
• On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes

  2001cited by this paper
• An Introduction to Variational Methods for Graphical Models

  1999cited by this paper
• The aging electrocardiogram.

  1990cited by this paper

• Machine Learning as a Tool for Hypothesis Generation

  2023cites this paper
• Learning Consistent Deep Generative Models from Sparsely Labeled Data

  2022cites this paper
• Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders

  2021cites this paper
• Scientific Discovery by Generating Counterfactuals using Image Translation

  2020cites this paper
• Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints

  2020cites this paper