
Using the Multidimensional Bilateral Distribution for Textual Discrimination
Using the Multidimensional Bilateral Distribution for Textual Discrimination – We present a new dataset for a novel kind of semantic discrimination (tense) task aiming at comparing two types of text: semantic and unsemantically. It includes largescale annotated hand annotated datasets that are large in size and are capable of covering an entire language. We propose […]

Segmentation from High Dimensional Data using Gaussian Process Network Lasso
Segmentation from High Dimensional Data using Gaussian Process Network Lasso – The recent release of Convolutional Neural Networks (CNN) with deep architectures can be easily implemented, but is computationally expensive to train. Recent work has shown that the amount of data needed for training CNNs can be increased with the number of parameters used by […]

Efficient Online Convex Sparse Autoencoders for Nonconvex Sparsity
Efficient Online Convex Sparse Autoencoders for Nonconvex Sparsity – In this paper, we propose a family of online computationally optimal (OFA) inference algorithms for nonconvex and polynomial problems. This algorithm was designed to solve the problem where the data are sparse and contain unknown entries, i.e., those data that are never seen. The algorithm is […]

Compositional Argumentation with Interrater Agreement
Compositional Argumentation with Interrater Agreement – We present an online framework that generalizes the Markov Decision Process (MDP) to an online environment where we learn to use the inputs and evaluate their performance. The goal is to predict the response of the agent on each of the two inputs to the agent. Our framework, the […]

Pose Flow Estimation: Interpretable Interpretable Feature Learning
Pose Flow Estimation: Interpretable Interpretable Feature Learning – Recent advances in deep learning have enabled the efficient training of deep neural networks, but the large number of datasets still requires a dedicated optimization. To address this problem, it is important for both the training and optimization steps to be made parallel. In this paper, we […]

The Entire Model Is Approximately Truncated: An Optimal Estimation of Linear Parameters
The Entire Model Is Approximately Truncated: An Optimal Estimation of Linear Parameters – This paper proposes the use of a randomassignment algorithm for a multimodal machine learning problem with a simple but general purpose function. The model must be one of binary data structures, such as a binary tree, or some similar structure, such as […]

Using Generalized CrossDomainUniversal Representations for Topic Modeling
Using Generalized CrossDomainUniversal Representations for Topic Modeling – A new paradigm for multiclass classification from a large range of visual cues is proposed, which utilizes the multiclass feature set to guide the classification process. The proposed framework generalizes to a new set of multiclass classes, i.e., an image with more than 6 classes. The proposed […]

Distributed Stochastic Dictionary Learning
Distributed Stochastic Dictionary Learning – This paper proposes a novel stochastic classification framework for binary recognition problems such as classification, clustering, and ranking. Under such models, in order to model uncertainty, one can choose to model the gradient as a mixture of twovalued parameters (i.e., the distance between the output and the input). Here, the […]

Fast and Robust Proximal Algorithms for GraphStructured Variational Computation
Fast and Robust Proximal Algorithms for GraphStructured Variational Computation – We present the firstever modelfree stochastic algorithm for the purpose of estimating the likelihood of a target variable, using a combination of twodimensional probabilistic models. Unlike existing stochastic optimization algorithms that model stochastic processes, our algorithm can also model uncertainty in the underlying stochastic process. […]

Relevance Estimation Using Sparse Multidimensional Scaling: Application to Classification and Regression
Relevance Estimation Using Sparse Multidimensional Scaling: Application to Classification and Regression – We present a method to use unsupervised feature learning (similar to Sparse MultiClass Classification) over large class images (e.g., MNIST and CIFAR10). Under certain assumptions about the image representations, we establish a new classifier for the task of classification over the MNIST dataset. […]