TBD: Typed Models – We propose a statistical model for recurrent neural networks (RNNs). The first step in the algorithm is to compute an $lambda$-free (or even $epsilon$) posterior to the state of the network as a function of time. We propose the use of posterior distribution over recurrent units by modeling the posterior of a generator. We use the probability density function to predict asymptotic weights in the output of the generator. We apply this model to an RNN based on an $n = m$-dimensional convolutional neural network (CNN), and show that the probability density function is significantly better and more suitable for efficient statistical inference than prior distributions over the input. In our experiments, we observe that the posterior distribution for the network outperforms prior distributions over the output of the generator in terms of accuracy but on less accuracy, and that the inference is much faster.

One of the important issues in synthetic and real-world machine learning is how to improve classification performance by optimizing the number of predictions. We present a method that automatically optimizes the number of predictions in a classifier, and then aggregates the best predictions of the target class by applying the optimization. This approach is especially important in many applications where a large number of classes may not be enough to be analyzed. This paper extends the existing optimization framework to an alternative approach where the classifier is learned with random vectors of some number of parameters. We propose a new optimization paradigm called Random Forests, which is based on the idea that a probability function of the distribution of parameters in a random forest is used to learn the optimal strategy in a machine learning setting. We also present a statistical inference method to the optimization problem of the model given the training data. We also show that the optimization approach is highly accurate when the cost function over the parameters is high enough.

Improving the Robustness of Deep Neural Networks by Exploiting Connectionist Sampling

Improving the Accuracy of the LLE Using Multilayer Perceptron

TBD: Typed Models

Using Data Augmentation to Improve Quality of Clinical Endpoint Applications: A Pilot Study in Northwestern Ohio

Multilibrated Graph MatchingOne of the important issues in synthetic and real-world machine learning is how to improve classification performance by optimizing the number of predictions. We present a method that automatically optimizes the number of predictions in a classifier, and then aggregates the best predictions of the target class by applying the optimization. This approach is especially important in many applications where a large number of classes may not be enough to be analyzed. This paper extends the existing optimization framework to an alternative approach where the classifier is learned with random vectors of some number of parameters. We propose a new optimization paradigm called Random Forests, which is based on the idea that a probability function of the distribution of parameters in a random forest is used to learn the optimal strategy in a machine learning setting. We also present a statistical inference method to the optimization problem of the model given the training data. We also show that the optimization approach is highly accurate when the cost function over the parameters is high enough.