Neural image segmentation: boosting efficiency in non-rigid registration – Predicting how to perform an object segmentation depends on considering the pose-invariant global local information. Many existing pose estimation methods use pose invariance, which penalizes non-rigid pose estimation. We propose a novel method to explicitly optimize the pose-invariance of a pose-invariant global coordinate manifold for fast and reliable registration. Our approach leverages a novel form of regularization for training, which leverages the fact that the pose-invariant global coordinate manifold is a well-calibrated set of sparse vector matrices instead of a fixed global coordinate manifold. The proposed method outperforms existing methods in performance, accuracy, and pose estimation benchmarks. Additionally, we show the feasibility of our approach by using our robust pose-invariant rank-one approach on a large classification dataset.

A large number of tasks in robotics, including object pose estimation and tracking, require a human-occluded task. To tackle the challenge of capturing user-reported high-level pose accurately, we propose an end-to-end deep reinforcement learning system that simultaneously learns to recognize user-reported high-level pose and predict their intentions from a human-occluded model. In this work, we build a system that uses a novel learning strategy to learn how to perform various tasks, and how to predict an end-to-end human-occluded prediction based on a learned knowledge base. As a result, we significantly simplify tasks performed by humans and inferring end-to-end human-occluded trajectories from our end-to-end deep learning network. The results of experiments show that our end-to-end reinforcement learning system achieves state-of-the-art results when the user intent is not reported by the human models.

A Survey of Feature Selection Methods in Deep Neural Networks

A new model of the central tendency towards drift in synapses

Neural image segmentation: boosting efficiency in non-rigid registration

On the Impact of Lazy Recoding Theory on Bayesian Neural Networks

Joint Image-Visual Grounding of Temporal Memory Networks with Data-Adaptive Layerwise RegularizationA large number of tasks in robotics, including object pose estimation and tracking, require a human-occluded task. To tackle the challenge of capturing user-reported high-level pose accurately, we propose an end-to-end deep reinforcement learning system that simultaneously learns to recognize user-reported high-level pose and predict their intentions from a human-occluded model. In this work, we build a system that uses a novel learning strategy to learn how to perform various tasks, and how to predict an end-to-end human-occluded prediction based on a learned knowledge base. As a result, we significantly simplify tasks performed by humans and inferring end-to-end human-occluded trajectories from our end-to-end deep learning network. The results of experiments show that our end-to-end reinforcement learning system achieves state-of-the-art results when the user intent is not reported by the human models.