Academic Research Paper Generator

We used AI to automatically write research papers like those on arXiv.org and in academic journals. To be clear, the titles and abstracts for these academic papers are not real, they are 100% computer generated:

Multi-Instance Dictionary Learning in the Matrix Space with Applications to Video Classification
We present a method for training and testing feature representations of neural networks consisting of two discrete states, and using each state for learning the object class, and the representations to provide a representation of the object class, and its attributes. This approach, called model-free feature learning (MAF), involves training a neural network with a fixed set of models and training a new model with a number of models. We extend the MAF approach to train an end-to-end deep recurrent neural network using the feature representation learned by the model's output and a novel embedding method. The embedding is based on a recurrent neural network that learns sparse representations of the target object class. The embeddings are learned and evaluated by a human expert, in a supervised fashion. Experimental results show that MAF improves the performance of a deep neural network trained with a given embedding and test data. Finally, we also show that MAF improves performance of a deep neural network trained with a pre-trained model, and the learned embeddings.



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