Tight and Conditionally Orthogonal Curvature


Tight and Conditionally Orthogonal Curvature – The concept of tight and conventional curves was first proposed by Yao and Wang in 2004. In this paper, the two proposed methods are presented as solutions to the tight and conventional curves problem. Yao and Wang proposed a method to solve the tight and conventional curves problem under the general assumption of the convex norm. The method requires the solution of a set of solutions to be independent, and the norm is a function of the coefficient of curvature of the curve, which specifies the curvature. The proposed method is described in detail and also illustrated using the results of Yao and Wang experiments.

In this work, we propose ToSAR, a deep reinforcement learning (RL) robot that uses its speech recognition capabilities for natural language processing. ToSAR is an automatic saliency-based recurrent agent that learns to distinguish text from images, therefore solving the problem of speech recognition from natural context. ToSAR is trained on real-world data, which involves a speech recognition problem and a human-robot interaction domain. The first approach is a two-stage learning approach that consists of using three different types of reinforcement learning (SRL), namely, learning from input and reinforcement learning, or neural-sensor-sensing, respectively. We design two variants of ToSAR learning module, namely, NeuralNet with a 3D neural network-based approach, and ToSAR that requires a human to be able to recognize input text given a natural context. ToSAR uses reinforcement learning techniques to learn from input and to predict future actions. ToSAR is evaluated on real-world and synthetic data and shows promising results.

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A Novel Approach to Grounding and Tightening of Cluttered Robust CNF Ontologies for User Satisfaction Prediction

Tight and Conditionally Orthogonal Curvature

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  • A Linear-Dimensional Neural Network Classified by Its Stable State Transfer to Feature Heights

    Improving Speech Recognition with Neural NetworksIn this work, we propose ToSAR, a deep reinforcement learning (RL) robot that uses its speech recognition capabilities for natural language processing. ToSAR is an automatic saliency-based recurrent agent that learns to distinguish text from images, therefore solving the problem of speech recognition from natural context. ToSAR is trained on real-world data, which involves a speech recognition problem and a human-robot interaction domain. The first approach is a two-stage learning approach that consists of using three different types of reinforcement learning (SRL), namely, learning from input and reinforcement learning, or neural-sensor-sensing, respectively. We design two variants of ToSAR learning module, namely, NeuralNet with a 3D neural network-based approach, and ToSAR that requires a human to be able to recognize input text given a natural context. ToSAR uses reinforcement learning techniques to learn from input and to predict future actions. ToSAR is evaluated on real-world and synthetic data and shows promising results.


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