Learning to Distill Similarity between Humans and Robots – We consider the problem of learning a latent discriminant model over the latent space of data. To achieve this we consider the same problem with two different latent space models: linear and nonlinear nonparametric models. One model is a nonlinear nonlinear autoencoder with linear coefficients and its coefficients are linear in the dimension. For nonlinear autoencoder we show that it is possible to learn the latent variable of interest and that the model can be used to model the nonlinear latent space. We also show that the latent variable of interest is linear in the dimension and also the model can be used to model the nonlinear latent space. We present a new model called Linear autoencoder (LAN) which can learn the latent variables of interest and the latent latent variable of interest simultaneously. We present an algorithm for this learning problem.

In this work we develop a generic approach based on the Bayesian clustering algorithm. Our clustering algorithm combines two related objectives: clustering between pairs of random variables and clustering between clusters of points. The main contribution of our method is the use of the similarity between cluster points in a hierarchical Bayesian model with the same model. The hierarchical Bayesian model is a family of hierarchical graphs with many nodes connected at each node and the nodes and the groups are called cluster groups. The similarity between the point groups is encoded by the point values in pairs of random variables. The graph-to-graph approach is shown to be a better than tree clustering algorithm by using the similarity between the cluster points during the inference process.

We present a new method that combines a deep-learning technique with a model training on high-dimensional data. The learned models are evaluated on a classification task by training different classes of deep models on the high-dimensional data. Our method outperformed other machine learning techniques on both tasks.

Towards a Framework of Deep Neural Networks for Unconstrained Large Scale Dataset Design

Hierarchical Learning for Distributed Multilabel Learning

# Learning to Distill Similarity between Humans and Robots

The Role of Information Fusion and Transfer in Learning and Teaching Evolution

Learning to Generate Chairs with Pointwise Loss FunctionsIn this work we develop a generic approach based on the Bayesian clustering algorithm. Our clustering algorithm combines two related objectives: clustering between pairs of random variables and clustering between clusters of points. The main contribution of our method is the use of the similarity between cluster points in a hierarchical Bayesian model with the same model. The hierarchical Bayesian model is a family of hierarchical graphs with many nodes connected at each node and the nodes and the groups are called cluster groups. The similarity between the point groups is encoded by the point values in pairs of random variables. The graph-to-graph approach is shown to be a better than tree clustering algorithm by using the similarity between the cluster points during the inference process.

We present a new method that combines a deep-learning technique with a model training on high-dimensional data. The learned models are evaluated on a classification task by training different classes of deep models on the high-dimensional data. Our method outperformed other machine learning techniques on both tasks.