Structure Learning for Deep Networks
Learning hierarchical category structure in deep neural networks
... the networks exhibit these ...error-correcting learning in linear three layer neural ...of learning in the network, and characterize formally how learning leads to the emergence of structured ...
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The resurgence of structure in deep neural networks
... I was exceptionally lucky to perform a research visit to Montréal’s Mila two times during my PhD. Therein, I have met extraordinary machine learning researchers, but primar‐ ily wonderful and highly welcoming ...
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Protein secondary structure prediction using neural networks and deep learning: a review
... automatic structure prediction servers continuously and objec- tively ( Koh et ...Protein Structure Prediction (CASP), also a benchmark determining commu- nity-wide project, tested protein structure ...
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Incremental Learning in Deep Neural Networks
... Computer vision attempts to emulate the human visual system in general, and extracts useful information from input, such as images or video sequences. This has proved a surprisingly challenging task; it has occupied ...
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A Deep Hybrid Graph Kernel through Deep Learning Networks
... associated deep basic kernel for the DHGK kernel is computed through a kernel-based embedding vector, and this embedding vector of each graph is computed based on measuring the kernel value between the graph and ...
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Exploiting Structure for Scalable and Robust Deep Learning
... Introduction Deep neural networks learn feature embeddings of the input data that enable state- of-the-art performance in a wide range of computer vision tasks, such as visual recognition Krizhevsky, ...
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Learning Deep Models with Linguistically-Inspired Structure
... neural networks compose words according to the syntactic relationships between them, typically using off-the-shelf ...latent structure encounter a trade-off: make factorization assumptions that limit ...
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Learning Scalable Deep Kernels with Recurrent Structure
... this structure cannot be easily captured by standard kernel ...such structure, we propose expressive closed-form kernel functions for Gaussian ...recurrent networks, while retaining the ...
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Design and Analysis of Deep Learning in Neural Networks
... Profound learning (DL) is a rising and wonderful point of view that licenses expansive scale undertaking driven part grabbing from tremendous ...fuzzy learning into deciliter to trounce the deficiencies of ...
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Distributed deep learning inference in fog networks
... basic structure of ...phase. Learning features in an unsuper- vised fashion is considered as the pre-train ...low learning rate to find the best value for model ...
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Semi-supervised learning of deep neural networks
... trained networks on the data from all of the following ...the structure of malware changes over time, the prediction accuracy of the newer data tends to get ...semi-supervised learning could overcome ...
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Superintelligent Deep Learning Artificial Neural Networks
... Network structure is a system that has no rule of thumb for the choice of its Activation ...ANN structure comprises of the arbitrary choice of combinations of both Probabilistic and Deterministic Activation ...
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On the Expressivity of Neural Networks for Deep Reinforcement Learning
... It is challenging and perhaps requires deep math to character- ize the fractal structure of Q-functions for random dynamics, which is beyond the scope of this paper. Instead, we take an empirical approach ...
Deep depth-based representations of graphs through deep learning networks
... Experimental Analysis: Table.3 indicates that the proposed method has better perfor- mance on classification problems. The reasons for the effectiveness of the proposed 415 DDBR method are threefold. First, the proposed ...
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Learning to Control Random Boolean Networks: A Deep Reinforcement Learning Approach
... novel learning approach to the problem of RBN control that uses Double DQN and PER to directly interact with an RBN and achieve control with no knowledge of the underlying connectivity, structure or Boolean ...
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Combustion Condition Monitoring Through Deep Learning Networks
... In this study, the GAN is applied to further improve the expressive capacity of the DAE. The structure of the designed DAE-GAN is shown in Fig. 2, which includes a generator and a discriminator. The input sample 𝑥 ...
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Manuscripts Classification Using Convolutional Deep Learning Networks
... ...................................... 4.2. CLaMM16 Preprocessing test data is available in a file structure on CD 2 . For the validation of our work, we use the test dataset. We let the training run for 25 ...
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Dissecting deep learning networks—Visualizing mutual information
... in learning specific features of the input images, as the MI order of the kernels changes more dramatically over ...better structure to reach DL solutions, which explains the better overall performance of ...
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Dissecting Deep Learning Networks—Visualizing Mutual Information
... in learning specific features of the input images, as the MI order of the kernels changes more dramatically over ...better structure to reach DL solutions, which explains the better overall performance of ...
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Advanced Music Audio Feature Learning with Deep Networks
... The model ‘Full Freq’ uses similar kernel windows to the original SDNet that spans the entire frequency domain, immediately reducing the dimensions of the output data to a single frequency bin, or an image height of one. ...
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