Towards a Machine Understanding Neuroscience: A Review


Towards a Machine Understanding Neuroscience: A Review – We present a novel approach to learning deep neural network architectures that generalize well to a large number of tasks. The objective is to identify the relevant features in the input and the relevant features in the output. We construct a neural network that learns a rich set of features to recognize different classes of objects. In addition, the object recognition models are employed to extract features from object images to learn features for other classes of objects, such as the human visual system. To overcome high memory demands imposed by learning a deep representation of the object images, we propose a novel neural network architecture called DeepNet, which is designed to learn a multi-stream model that is able to generalize well to a large number of tasks. We test DeepNet on five datasets and achieve an accuracy of over 80% on the task of object recognition.

Deep neural networks have become a popular approach for machine learning and visual recognition applications. This makes it very difficult to optimize training with these models. The goal of this paper is to study the effect of modeling over training data using different deep models and learning techniques. We used a deep neural network (DNN) model and a stochastic gradient descent classifier to explore which models outperform and learn the best performance. We compared the performance of learning the model and the algorithm using simulated data in which we used a variety of datasets. Experimental results showed that the difference was substantial.

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Towards a Machine Understanding Neuroscience: A Review

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  • Adversarial Input Transfer Learning

    Visual Tracking via Deep Generative ModelsDeep neural networks have become a popular approach for machine learning and visual recognition applications. This makes it very difficult to optimize training with these models. The goal of this paper is to study the effect of modeling over training data using different deep models and learning techniques. We used a deep neural network (DNN) model and a stochastic gradient descent classifier to explore which models outperform and learn the best performance. We compared the performance of learning the model and the algorithm using simulated data in which we used a variety of datasets. Experimental results showed that the difference was substantial.


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