Recurrent Neural Sequence-to-Sequence Models for Prediction of Adjective Outliers – In this paper, we design a novel approach for supervised learning of nouns in natural language from Wikipedia articles. The approach utilizes a large number of semantic units for classification, and we define an efficient strategy for extracting semantic units in the sentence. The approach is evaluated on synthetic datasets of Wikipedia articles and also on real-world English datasets for sentence classification. To evaluate the performance of our approach, we use an online dictionary learning algorithm and a supervised algorithm for noun recognition. The results show that the proposed strategy achieves significant improvement in classification accuracy when compared with other existing approaches.
This paper reports the first full-text representation of sentences in NLP. Our first work in NLP is a word-based neural network (GNRN) model, which has been used in a number of machine translation tasks. The NLRNN achieves very good performance in both word recognition and sentence prediction for sentence embedding tasks. It also outperforms the best of the best by a large margin and shows the advantage of the word-based representation for such tasks.
A supervised learning objective in music classification is described. Music classification is typically carried out using a music-based classification task, where the target music is the sampled music. In the framework of this objective, a supervised learning objective is defined. Based on the objective, a classifier is defined for music classification without the need for any prior knowledge about the target music. This objective is based on the fact that the music features of each sample can be used to rank the classifier. The classification objective is presented to obtain a classifier that is robust against music-sparseness features of samples. The objective is evaluated on three data sets: sample-based data from a toy and a movie. The experimental results show that the proposed objective outperforms other supervised learning objectives.
Mining for Structured Shallow Activation Functions
Recurrent Neural Sequence-to-Sequence Models for Prediction of Adjective Outliers
Action Recognition with 3D CNN: Onsets and Transformations
Dictionary Learning for Feature-Based Music VisualizationA supervised learning objective in music classification is described. Music classification is typically carried out using a music-based classification task, where the target music is the sampled music. In the framework of this objective, a supervised learning objective is defined. Based on the objective, a classifier is defined for music classification without the need for any prior knowledge about the target music. This objective is based on the fact that the music features of each sample can be used to rank the classifier. The classification objective is presented to obtain a classifier that is robust against music-sparseness features of samples. The objective is evaluated on three data sets: sample-based data from a toy and a movie. The experimental results show that the proposed objective outperforms other supervised learning objectives.