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Showing 1 to 7 of 7 for “"Deep network architecture"”.
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A spatial deep network architecture for brain decoding
… to incorporate structured smoothness in a deep learning model. FGL achieves this goal by connecting nodes across layers based on spatial similarity. The inductive bias of structured smoothness implemented by FGL is motivated by applications such as brain image decoding, i.e., predicting …
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Decoding brains by paying attention: An attention-based fMRI task state decoding deep network architecture
… task decoding is done in pursuit of creating a deep learning model for task prediction. Typically these models will include either handcrafted features or data driven approaches for downscaling the input features in successive layers. In this thesis, we explore and compare the effectiveness of …
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Automatic Extraction of Joint Characteristics from Rock Mass Surface Point Cloud Using Deep Learning
… on 3D point cloud models of rock masses using deep learning is presented. The process starts with classifying joints on a 3D rock mass surface through training a deep network architecture and validated using manually labelled datasets. Then, individual joint surfaces are identified using the …
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Spatio-temporal human action detection and instance segmentation in videos
… action detection approach based on a frame-level deep feature representation combined with a two-pass dynamic programming approach. The method obtains a frame-level action representation by leveraging recent advances in deep learning based action recognition and object detection methods. To …
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Model-Architecture Co-design of Deep Neural Networks for Embedded Systems
In deep learning, a convolutional neural network (ConvNet or CNN) is a powerful tool for building interesting embedded applications that use data to make predictions. An application running on an embedded system typically has limited access to memory resources, processing power, and storage. …
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Demystifying deep network architectures : from theory to applications
Deep neural networks significantly power the success of machine learning and artificial intelligence. Over the past decade, the community keeps designing architectures of deep layers and complicated connections. Many works in deep learning theory tried to understand deep networks from different …
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Towards robust and domain invariant feature representations in Deep Learning
… question: Do representations learned using deep networks just fit a given data distribution or do they sufficiently model the underlying structure of the problem ? This question could be understood using a simple example: If a learning algorithm is shown a number of images of a simple …