Robust 3D Registration via Deep Generative Models – We present a supervised 2D autoencoder for the task of 3D reconstruction. Our model consists of two separate convolutional networks with a recurrent feed-forward network to encode image data sequences, as well as a recurrent network to represent the visual information for the model. We then extract object attributes from these convolutional networks without a recurrent loss. To further facilitate the training process, we perform image-to-image transfer and map learning. The proposed model outperforms the state of the art results on a variety of datasets, including 3D indoor scenes from a hospital.

In this paper, we describe a new approach for multi-task semantic segmentation with recurrent neural network. Our approach combines two tasks: semantic segmentation by recurrent neural networks and object segmentation from a video or audio source. In an end-to-end training framework, we develop an end-to-end policy generation framework, and present the first system for multi-task semantic segmentation with recurrent neural network (RNN) and CNNs. We have developed a multi-task semantic segmentation model with recurrent-CNN network from scratch and train it on different state-of-the-art Residual Reinforcement Learning (ResRRL) models. Our experiments show that our approach is highly accurate and can achieve state-of-the-art performance.

On-line learning of spatiotemporal patterns using an exact node-distance approach

Learning a deep representation of one’s own actions with reinforcement learning

Robust 3D Registration via Deep Generative Models

Robust Sparse Modeling: Stochastic Nearest Neighbor Search for Equivalential Methods of Classification

Recovering language from scratch using probabilistic word embeddings for unconstrained and unsupervised learningIn this paper, we describe a new approach for multi-task semantic segmentation with recurrent neural network. Our approach combines two tasks: semantic segmentation by recurrent neural networks and object segmentation from a video or audio source. In an end-to-end training framework, we develop an end-to-end policy generation framework, and present the first system for multi-task semantic segmentation with recurrent neural network (RNN) and CNNs. We have developed a multi-task semantic segmentation model with recurrent-CNN network from scratch and train it on different state-of-the-art Residual Reinforcement Learning (ResRRL) models. Our experiments show that our approach is highly accurate and can achieve state-of-the-art performance.