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Bayesian Inference for Gaussian Process Models with Linear Regresses – We propose an approach to learning probabilistic models based on the probabilistic inference task of finding the causal ordering. We show that prior knowledge about the causal ordering is sufficient to model the posterior distributions of the model outputs for this task. The probabilistic inference […]

Neural Sequence-to-Sequence Models with Adversarial Priors – In this paper, we propose a novel recurrent neural network (RNN) as a means of learning to answer unsupervised questions. It is particularly well suited to question answering tasks where the answer space is unknown (e.g. answering in a computer). To this we propose an approach to learn […]

Efficient Bipartite Markov Chain Monte Carlo using Conditional Independence Criterion – In this paper we extend the popular Markov Random Field (MRF) method to the multi-label setting, for the task of multivariate random fields with several labels for each label. Existing MRF methods provide a method for learning the labels within a model, namely a […]

An Experimental Evaluation of the Performance of Conditional Random Field Neurons – This paper presents an experimental evaluation of an algorithm called the Random Field Neurons and a model called a Random Field Neuron. The results are very useful and are validated using data from a large clinical trial. We obtain a numerical evaluation of […]

Improving Recurrent Neural Networks with Graphs – We present an end-to-end model-based algorithm to encode and extract the semantic meanings of sentences. By extracting a semantic meaning from a sequence of sentences, we aim to capture the semantic structure in a graph and propose a method for learning from the set of sentences. Since the […]

Evaluation of Facial Action Units in the Wild Considering Nearly Automated Clearing House – We propose a generic framework for modeling facial action recognition systems, the framework consists of a fully automatic and a fully self-contained, single-model architecture. The goal of this framework is to overcome the limitations in the existing multi-model frameworks, thereby making […]

Nonparametric Nonnegative Matrix Factorization – We present a novel learning algorithm for the sparse vector training problem involving the sparse Markov chain Monte Carlo (MCMC) as a training set for a stochastic objective function. The objective function is a Gaussian function which is independent of any given covariance matrix, and we prove that it is […]

Deep Learning with a Recurrent Graph Laplacian: From Linear Regression to Sparse Tensor Recovery – We demonstrate that the recent convergence of deep reinforcement learning (DRL) with a recurrent neural network (RNN) can be optimized using linear regression. The optimization involves a novel type of recurrent neural network (RINNN) that can be trained in RNNs […]

Multilingual Word Embeddings from Unstructured Speech – This paper discusses the possibility of a global context-aware approach to knowledge-based lexical data retrieval. The aim of this paper is to integrate knowledge from the multilingual nature of the lexical data by translating lexical data into lexical-semantic (semantic-semantic) data. We aim to use the lexical data data […]

Learning Bayesian Networks in a Bayesian Network Architecture via a Greedy Metric – In this paper, we propose a new technique for automatic learning from input data. We consider the problem of machine learning where it is desirable to learn knowledge from a single input, instead of using inputs from multiple sources. We first show […]