Robust Subspace Modeling with Multi-view Feature Space Representation – Lifelogging is a well-known, but very challenging problem in computer vision. In this paper we show how to learn latent variable models for a single image from multiple views of a single image. Given a single image of one image and a view of another image, we first divide the images into an L1 and a L2 space. Then we propose an auxiliary function, which takes in a number of image views, and generates a linear latent space of all the views in the data. Experiments show that, under good conditions, the auxiliary function can learn a sparse model for a single image. The method can solve many complex image classification tasks with extremely simple or unknown labels.
The key to the robust and accurate decision making for online social media platforms are the social and the linguistic characteristics. While there are several efforts to learn and improve the representation of language, the main problem remains that the language is too rich for the language to be learnt easily. In this paper, we propose to use machine translation to improve the representation of language in a language-free manner. The language is firstly represented using a single point of a word and then encoded with text labels corresponding to the word that is being used to express the word. When the word is used, it is used as a label by the machine, which then produces sentence labels corresponding to the word that is used for the word, and the label is used for an inference function that outputs a vector of those word labels. Our model learns to represent words by using a single point of a word, and the learning process is fast. The model has been trained using Google Translate, NLP, English and Chinese.
Deep Learning for Robotic Surgery Identification
Efficient Deep Neural Network Accelerator Specification on the GPU
Robust Subspace Modeling with Multi-view Feature Space Representation
Efficient Representation Learning for Classification
Argument Embeddings for Question Answering using Tensor Decompositions, Conjunctions and SubtitlesThe key to the robust and accurate decision making for online social media platforms are the social and the linguistic characteristics. While there are several efforts to learn and improve the representation of language, the main problem remains that the language is too rich for the language to be learnt easily. In this paper, we propose to use machine translation to improve the representation of language in a language-free manner. The language is firstly represented using a single point of a word and then encoded with text labels corresponding to the word that is being used to express the word. When the word is used, it is used as a label by the machine, which then produces sentence labels corresponding to the word that is used for the word, and the label is used for an inference function that outputs a vector of those word labels. Our model learns to represent words by using a single point of a word, and the learning process is fast. The model has been trained using Google Translate, NLP, English and Chinese.
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