Unsupervised learning of visual stimuli from fMRI


Unsupervised learning of visual stimuli from fMRI – The success of deep learning algorithms in image recognition and natural language processing has not been well captured. It is therefore important to study the application of deep learning to image retrieval without any human supervision or human-level input. Previous studies have shown the use of CNNs for image retrieval problems, where a large number of images are needed to be retrieved. This paper investigates a different approach that makes use of image semantic clues by using deep CNNs. Our approach is based on a network approach that allows for CNNs to encode visual cues for the image and only store the hidden representations, without requiring human supervision. In order to learn from unlabeled images, we learn a model which is capable of generating high-level semantic descriptions. Specifically, our model is an ImageNet and uses deep convolutional neural networks to encode visual cue representations. We trained the model on a dataset of a wide range of images, based on an image labelled with image annotations, and evaluated the performance. The model demonstrated superior performance compared to the other approaches.

In this paper, we present a neural method by means of a novel method to synthesize the high dimensional data from several tasks simultaneously. The new method uses a novel deep architecture which allows the data to be represented as a single dimensional vector, which makes our method much more flexible than previous methods. The method is able to synthesize high dimensional data with high accuracy. The method was made available as a research tool in the project Learning Computation Graphs. It’s designed and implemented as a supervised learning system, which allows to simulate the dynamic process of data synthesis.

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Unsupervised learning of visual stimuli from fMRI

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  • Semi-Supervised Learning of Semantic Representations with the Grouping Attention Kernel

    Learning a Dynamic Algorithm by Learning Dynamic Computation GraphsIn this paper, we present a neural method by means of a novel method to synthesize the high dimensional data from several tasks simultaneously. The new method uses a novel deep architecture which allows the data to be represented as a single dimensional vector, which makes our method much more flexible than previous methods. The method is able to synthesize high dimensional data with high accuracy. The method was made available as a research tool in the project Learning Computation Graphs. It’s designed and implemented as a supervised learning system, which allows to simulate the dynamic process of data synthesis.


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