We describe some simple simulations showing two possible adaptive advantages of the ability to predict the consequences of one’s actions: predicted inputs can replace missing inputs and predicted success vs. failure can help deciding whether to actually executing a planned action or not. The neural networks controlling the organisms’ behaviour include distinct modules whose connection weights a...

We propose a Deep Belief Network architecture for learning a joint representation of multimodal data. The model defines a probability distribution over the space of multimodal inputs and allows sampling from the conditional distributions over each data modality. This makes it possible for the model to create a multimodal representation even when some data modalities are missing. Our experimenta...

The evaluation of productivity of educational units during the last decades has become an important priority for many countries. A current approach considers the schools as production units that use multiple inputs and produce multiple outputs. Data Envelopment Analysis (DEA) is a very effective methodology for the estimation of relative efficiencies in the presence of multiple inputs and outpu...

Missing data is a recurrent and challenging problem, especially when using machine learning algorithms for real-world applications. For this reason, missing imputation has become an active research area, in which recent deep approaches have achieved state-of-the-art results. We propose DAEMA (Denoising Autoencoder with Mask Attention), algorithm based on denoising autoencoder architecture atten...

We consider studies of cohorts of individuals after a critical event, such as an injury, with the following characteristics. First, the studies are designed to measure "input" variables, which describe the period before the critical event, and to characterize the distribution of the input variables in the cohort. Second, the studies are designed to measure "output" variables, primarily mortalit...

This paper discusses the task of learning a classifier from observed data containing missing values amongst the inputs which are missing completely at random. A non-parametric perspective is adopted by defining a modified risk taking into account the uncertainty of the predicted outputs when missing values are involved. It is shown that this approach generalizes the approach of mean imputation ...

The computational principles underlying the processing of sensory-evoked synaptic inputs are understood only rudimentarily. A critical missing factor is knowledge of the activation patterns of the synaptic inputs to the processing neurons. Here we use well-defined, reproducible skin stimulation to describe the specific signal transformations that occur in different parallel mossy fiber pathways...

congenitally missing of maxillary lateral incisors is one of the most common patterns of hypodontia. this paper presents a nine year old boy with congenital missing of lateral incisors. familial history showed that, his mother, aunts, uncle and grandmother have also congenital absence of lateral incisors.

Subutai Ahmad Interval Research Corporation 1801-C Page Mill R<;l. Palo Alto, CA 94304 We present efficient algorithms for dealing with the problem of missing inputs (incomplete feature vectors) during training and recall. Our approach is based on the approximation of the input data distribution using Parzen windows. For recall, we obtain closed form solutions for arbitrary feedforward networks...