24,985 research outputs found
Prediction of survival probabilities with Bayesian Decision Trees
Practitioners use Trauma and Injury Severity Score (TRISS) models for predicting the survival probability of an injured patient. The accuracy of TRISS predictions is acceptable for patients with up to three typical injuries, but unacceptable for patients with a larger number of injuries or with atypical injuries. Based on a regression model, the TRISS methodology does not provide the predictive density required for accurate assessment of risk. Moreover, the regression model is difficult to interpret. We therefore consider Bayesian inference for estimating the predictive distribution of survival. The inference is based on decision tree models which recursively split data along explanatory variables, and so practitioners can understand these models. We propose the Bayesian method for estimating the predictive density and show that it outperforms the TRISS method in terms of both goodness-of-fit and classification accuracy. The developed method has been made available for evaluation purposes as a stand-alone application
On the Equivalence Between Deep NADE and Generative Stochastic Networks
Neural Autoregressive Distribution Estimators (NADEs) have recently been
shown as successful alternatives for modeling high dimensional multimodal
distributions. One issue associated with NADEs is that they rely on a
particular order of factorization for . This issue has been
recently addressed by a variant of NADE called Orderless NADEs and its deeper
version, Deep Orderless NADE. Orderless NADEs are trained based on a criterion
that stochastically maximizes with all possible orders of
factorizations. Unfortunately, ancestral sampling from deep NADE is very
expensive, corresponding to running through a neural net separately predicting
each of the visible variables given some others. This work makes a connection
between this criterion and the training criterion for Generative Stochastic
Networks (GSNs). It shows that training NADEs in this way also trains a GSN,
which defines a Markov chain associated with the NADE model. Based on this
connection, we show an alternative way to sample from a trained Orderless NADE
that allows to trade-off computing time and quality of the samples: a 3 to
10-fold speedup (taking into account the waste due to correlations between
consecutive samples of the chain) can be obtained without noticeably reducing
the quality of the samples. This is achieved using a novel sampling procedure
for GSNs called annealed GSN sampling, similar to tempering methods that
combines fast mixing (obtained thanks to steps at high noise levels) with
accurate samples (obtained thanks to steps at low noise levels).Comment: ECML/PKDD 201
Capturing human category representations by sampling in deep feature spaces
Understanding how people represent categories is a core problem in cognitive
science. Decades of research have yielded a variety of formal theories of
categories, but validating them with naturalistic stimuli is difficult. The
challenge is that human category representations cannot be directly observed
and running informative experiments with naturalistic stimuli such as images
requires a workable representation of these stimuli. Deep neural networks have
recently been successful in solving a range of computer vision tasks and
provide a way to compactly represent image features. Here, we introduce a
method to estimate the structure of human categories that combines ideas from
cognitive science and machine learning, blending human-based algorithms with
state-of-the-art deep image generators. We provide qualitative and quantitative
results as a proof-of-concept for the method's feasibility. Samples drawn from
human distributions rival those from state-of-the-art generative models in
quality and outperform alternative methods for estimating the structure of
human categories.Comment: 6 pages, 5 figures, 1 table. Accepted as a paper to the 40th Annual
Meeting of the Cognitive Science Society (CogSci 2018
Generative Models For Deep Learning with Very Scarce Data
The goal of this paper is to deal with a data scarcity scenario where deep
learning techniques use to fail. We compare the use of two well established
techniques, Restricted Boltzmann Machines and Variational Auto-encoders, as
generative models in order to increase the training set in a classification
framework. Essentially, we rely on Markov Chain Monte Carlo (MCMC) algorithms
for generating new samples. We show that generalization can be improved
comparing this methodology to other state-of-the-art techniques, e.g.
semi-supervised learning with ladder networks. Furthermore, we show that RBM is
better than VAE generating new samples for training a classifier with good
generalization capabilities
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