Spectral Clamping by Matrix Factorization


Spectral Clamping by Matrix Factorization – With increasingly complex environments, many methods have been proposed to address the problem of object manipulation. However, existing approaches mainly aim at modeling the object motion along with its interactions, such as pose, orientation, etc. In this paper, we propose an unsupervised and fast online method for object manipulation in the visual space. To this end we learn object-level object pose from images and train a convolutional network (CNN) to model the pose-vector representation. The model is trained with object transformations from the objects and the transformations are extracted from the bounding boxes. Our approach, which achieves state-of-the-art accuracy on 3DOF datasets, is based on the idea of learning rich semantic representations from 3D images. Extensive experiments on both synthetic and real images demonstrate that our method is comparable to the baselines, outperforming most methods.

In this paper, we apply a novel approach of learning a novelty-assisted reinforcement learning agent to the task of recovering a missing value in an action object from a database of actions. We demonstrate the ability of the learning agent to learn the new item, which allows to exploit the properties of the item’s missing value, and the properties of the object’s missing value. We present methods to solve the problem, which are based on reinforcement learning. In our experiments, we show that the learning agent’s learned new value is more accurate than that of a previous candidate, and that it can recover the new value from the database without requiring any knowledge of the current value.

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Spectral Clamping by Matrix Factorization

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  • Fast Multi-scale Deep Learning for Video Classification

    Learning with a Novelty-Assisted Learning AgentIn this paper, we apply a novel approach of learning a novelty-assisted reinforcement learning agent to the task of recovering a missing value in an action object from a database of actions. We demonstrate the ability of the learning agent to learn the new item, which allows to exploit the properties of the item’s missing value, and the properties of the object’s missing value. We present methods to solve the problem, which are based on reinforcement learning. In our experiments, we show that the learning agent’s learned new value is more accurate than that of a previous candidate, and that it can recover the new value from the database without requiring any knowledge of the current value.


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