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Profit Driven Feature Selection for High Dimensional Regression via Determinantal Point Process Kernels – We propose a novel and efficient Bayesian inference scheme based on the variational autoencoder model, where the posterior distribution is learned linearly over the data. The model is built out of a general convex optimization problem and the Bayesian optimizer is […]

Semantic Segmentation Using a Semantic Cue – Kernel learning and Kernel learning are two core concepts in artificial neural networks (ANNs) and kernel learning is one of them. Kernel learning has shown great success in achieving high accuracy and consistency of the input kernels but its applicability is limited to image classification. In this paper, […]

Learning to Predict Oriented Images from Contextual Hazards – Visual captioning can be seen as a social problem and the goal is to provide the captioning user with a knowledge about the captioning process. The main challenge here lies in obtaining the knowledge of the captioning process and how to apply it to the problem […]

Convolutional Spatial Transformer Networks (CST) – In this paper, we show how to generate highly structured shapes and their visualizations in a framework based on the convolutional neural networks (CNNs). We perform a comprehensive evaluation on both synthetic and real-world datasets on several tasks including image categorization, face verification and person re-identification. We show that […]

Sparse Nonparametric MAP Inference – In this work, we present a sparse nonparametric MAP inference algorithm to improve the precision of model predictions. In our method, the objective is to estimate the optimal distribution given the model parameters in terms of a non-convex function with an appropriate dimension. For each parameter, we propose an algorithm […]

Probabilistic Belief Propagation by Differential Evolution – It has recently been established that Bayesian networks can be used for approximate decision making. In this paper, we propose a new algorithm for posterior inference in probability density approximations, which is simple and efficient. This algorithm is based on the assumption that an inference procedure is an […]

Theano: a powerful compressive n-gram generator – Theano: a powerful compressive n-gram generator is a natural extension of Theano to the domain of language. In this paper, We present and evaluate a novel approach for learning a new language by exploiting a variety of techniques of the Theano, including the use of the N-gram. This […]

Theory and Practice of Interpretable Machine Learning Models – The purpose of this paper is to propose an effective method of analyzing a user generated content using multiple models that can be used to model multiple models of the same user as well as a unified model that can be used to model multiple models […]

A graph Laplacian: Feature-based partition, bounded orders and triple products – Bayesian inference is one of the most successful nonparametric learning algorithms for large-scale data. The performance of inference systems is closely related to the performance of human intelligence, yet the performance of human intelligence has not been very well studied. In this paper we […]

Adversarially Learned Online Learning – Many computer vision tasks require data-dependent labeling of labeled objects in images. This paper studies object labels in the wild, i.e., using a multi-modal network (MNN). Our approach leverages a novel model architecture and a novel model search technique to learn the labels of a MNN by learning to solve […]