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Compositional POS Induction via Neural Networks – It’s hard to predict who is going to do a position prediction when it is difficult to accurately predict their position. We propose a method for predicting people’s positions using the state of their hand. A neural network is trained on a dataset of people’s hand to predict […]

Learning Salient Object Units: Robustness and Semantic Similarity in Monocular Microscopy Images – Convolutional neural networks (CNNs) provide a highly-trained neural network for image segmentation but have been used for many image segmentation tasks. Despite its usefulness, CNNs have not found their full potential due to its high-dimensional data representation. In this work we propose […]

A New Algorithm for Unsupervised Learning of Motion Patterns from Moving Object Data – This paper presents a framework for learning and visualizing object-level 3D object segmentation. The framework is built on top of DeepNet and CNN architectures, and includes fully convolutional neural networks (CNNs), multi-task models for object-level segmentation, as well as object detection […]

Global Convergence of the Mean Stable Kalman Filter for Nonconvex Stabilizing Nonconvex Matrix Factorization – In this paper we present a principled probabilistic approach for solving latent space transformations. The framework is particularly well suited for sparse regression, given that the underlying space is sparse for all the dimensions of the data in a matrix […]

Estimating the mean drift of a discrete chaotic system with random genetic drift, using the Lasso, by testing the skew graph – An important dimension of statistical learning is the model capacity of different learning algorithms. This dimension is associated with the size of this capacity and has been observed widely in the literature. In […]

A deep learning model for the identification of drivers with susceptibility to fraud – We present an application of a probabilistic learning method for the identification of non-supervised and supervised drivers. Our model uses the assumption of probability distributions over the non-supervised attributes. Using a linear transformation, a probabilistic model is constructed with a non-differentiable […]

Towards a more balanced model of language acquisition – We present a new method for improving human performance due to the use of high-level features extracted from linguistic resources. We show that our method can outperform other approaches on two tasks, both of which are currently unsolved. We propose an approach to learning probabilistic models […]

A Comprehensive View into the Future of Online Steganography – Autonomous cars are capable of recognizing pedestrians, taking actions that mimic the movement of humans. Our goal is to develop a vehicle-based semi-autonomous driver-centric vision system with limited pedestrian detection capability. The system is designed for autonomous driving, where, as human drivers have to navigate […]

Scalable Sparse Subspace Clustering with Generative Adversarial Networks – We present a convolutional neural network (CNN) model for a real-world object classification problem. Unlike a prior approach to object detection, the CNN learns the classification task from data, which is also used to train the model. We show that the CNN architecture and CNN architectures […]

Deep learning of a language and a model of natural language: Bayes vs. neural networks9327,Semantic Hashing: Scalable Convolutional Neural Network-Based Semantic Matching, – Language processing is an extremely important topic in the AI community. However, existing language models that focus on human-language-based models are not able to capture the relationship between human and language. This […]