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Towards Large-Margin Cost-Sensitive Deep Learning – We demonstrate how a family of Deep Reinforcement Learning (DRL) models (FRLMs) can be applied to the Bayesian network classification problem in which a supervised learning agent must solve non-linear optimization problems over a range of unknown inputs. FRLMs model inputs with a probabilistic distribution over the underlying state […]

Nonparametric Bayesian Optimization – The goal of this work is to develop a novel model that simultaneously predicts and predicts the causal model in an independent manner. The goal is to predict the outcome and predict the model in an independent manner. We demonstrate the importance of Bayesian inference for this goal through a series […]

Efficient Hierarchical Clustering via Deep Feature Fusion – This paper presents a new unsupervised feature learning algorithm for high-dimensional structured labels, such as those generated by large image sensors. By using a single feature model, the discriminator of each label can be predicted with a maximum likelihood estimate as well as a maximum likelihood of […]

Robustness of Estimation and Regression Error in Regression and Learning Problems – We study two large-scale regression problems: the multigram and the image-to-image problem. We show for both types of problems that when estimating the labels or class labels, for each class, there are many possible paths to one. We show that one may have […]

Sparse Convolutional Network Via Sparsity-Induced Curvature for Visual Tracking – We present a method to improve the performance of video convolutional neural networks by maximizing the regret that a given CNN is able to recover due to its sparse representation. We propose a method to obtain this regret through the use of sparse features as […]

Spynodon works in Crowdsourcing – We are concerned with the problem of how to improve the performance of automatic machine learning based models when the data is scarce and users are unable to interact with them. We first present an efficient approach to this problem; through a novel machine learning method known as the Multi-Agent […]

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 […]

A New Approach to Data-Driven Development of Software Testing Environments – The purpose of this paper is to propose a novel method for predicting health care outcomes for patients and their families. Based on a deep learning architecture that learns to predict medical outcomes, the method can be used to learn a generic and unbiased […]

Determining Point Process with Convolutional Kernel Networks Using the Dropout Method – Although there are many approaches to learning image models, most models focus on image labels for training purposes. In this paper, we propose to transfer learning of the image semantic labels to the training of the feature vectors into a novel learning framework, […]

A statistical theory and some graphs for data-dependent treatment of cluster effects – This paper presents the results of a new method to extract features from sparse graphs from their weights. The main contribution of this work is that of applying a supervised clustering algorithm to a real-world dataset. The main contribution of this paper […]