Embed from the Web: Online Image Inpainting Using WebGL


Embed from the Web: Online Image Inpainting Using WebGL – In this work we propose a new method for online face modeling: a self-paced 3D online classification task, based on a novel combination of multiple supervised learning models, and online matching using different online matching strategies. Our method is based on a combination of an online pose-model fusion technique and a nonlinear supervised clustering algorithm. The proposed algorithm is also able to cope with non-smooth face models: it has been successfully tested on two real-world cases, in which we can use the face model as a benchmark, and the 3D face model as a comparison unit. In addition, we show the effectiveness of the proposed method by considering whether face models with local appearance differences between faces can be used to model the online pose-model fusion task. We performed experimental results comparing the face model fusion and the 3D face model fusion methods for two publicly available datasets.

The use of neural networks has made the task of speech recognition more challenging due to its inability to classify both the semantic information and the utterances of the words. In this paper, we focus on two important problems from the literature: 1. Recognition of utterances in a single speech sentence; 2. Classification of the utterances in an ensemble of speech sentences. The performance of the machine learning is evaluated using a multi-task learning (MTM) and Multi-channel (MC) learning paradigm for speech recognition. The best performance was achieved using a test set consisting of four different languages, with the goal of predicting the sentence sequence with a higher accuracy than using any other language pair.

A Fuzzy-Based Semantics: Learning Word Concepts and Labels with Attentional Networks

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Embed from the Web: Online Image Inpainting Using WebGL

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  • Nonlinear Learning with Feature-Weight Matrices: Theory and Practical Algorithms

    Improved LSTM based Speech Recognition by Adaptive Deep LearningThe use of neural networks has made the task of speech recognition more challenging due to its inability to classify both the semantic information and the utterances of the words. In this paper, we focus on two important problems from the literature: 1. Recognition of utterances in a single speech sentence; 2. Classification of the utterances in an ensemble of speech sentences. The performance of the machine learning is evaluated using a multi-task learning (MTM) and Multi-channel (MC) learning paradigm for speech recognition. The best performance was achieved using a test set consisting of four different languages, with the goal of predicting the sentence sequence with a higher accuracy than using any other language pair.


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