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- purposes of conducting machine learning and deep neural networks research, but the system is general...
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- Keras: Deep Learning library for Theano and TensorFlow. Keras is a minimalist, highly modular neural ... Keras if you need a deep learning library that: allows for easy and fast prototyping...
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- deep learning algorithm for solving partial differential equations. High-dimensional PDEs have been a longstanding ... PDEs by approximating the solution with a deep neural network which is trained to satisfy ... Bellman PDE and Burgers’ equation. The deep learning algorithm approximates the general solution ... dimensional space). We call the algorithm a “Deep Galerkin method (DGM)” since it is similar...
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- publications ”A fast learning algorithm for deep belief nets” (G. E. Hinton, S. Osindero...
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- short, is a library of deep learning models and datasets designed to make deep learning...
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- DeepXDE: A deep learning library for solving differential equations. Deep learning has achieved remarkable success...
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- Caffe is a deep learning framework made with expression, speed, and modularity in mind...
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- modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk...
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- paper, we present an initial attempt to learn evolution PDEs from data. Inspired ... development of neural network designs in deep learning, we propose a new feed-forward deep ... proposed PDE-Net is to learn differential operators by learning convolution kernels (filters), and apply...
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- usage of ODE solvers in deep learning applications, see . As the solvers are implemented...
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- MXNet is a deep learning framework designed for both efficiency and flexibility. It allows ... MXNet is also more than a deep learning project. It is also a collection ... blue prints and guidelines for building deep learning systems, and interesting insights of DL systems...
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- Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. Discriminative model learning ... embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising...
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- convolutional neural network that is 8 layers deep. You can load a pretrained version ... animals. As a result, the network has learned rich feature representations for a wide range ... more pretrained networks in MATLAB®, see Pretrained Deep Neural Networks...
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- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Point cloud...
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- task of semantic image segmentation with Deep Learning and make three main contributions that ... which feature responses are computed within Deep Convolutional Neural Networks. It also allows...
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- recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms...
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- present a system, called FaceNet, that directly learns a mapping from face images ... feature vectors. Our method uses a deep convolutional network trained to directly optimize the embedding ... intermediate bottleneck layer as in previous deep learning approaches. To train, we use triplets...
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- cuDNN: Efficient Primitives for Deep Learning. We present a library of efficient implementations of deep ... learning primitives. Deep learning workloads are computationally intensive, and optimizing their kernels is difficult ... there is no analogous library for deep learning. Without such a library, researchers implementing deep ... BLAS, with optimized routines for deep learning workloads. Our implementation contains routines for GPUs, although...
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- VAMPnets: Deep learning of molecular kinetics. There is an increasing demand for computing the relevant ... Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks...