deep learning image enhancement github

Eurographics 2018/Computer Graphics Forum | paper | code, Deep Chain HDRI: Reconstructing a High Dynamic Range Image from a Single Low Dynamic Range Image Visualizing and understanding convolutional networks, in Computer VisionECCV 2014, eds D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars (Springer), 818833. The neural network expressions cannot be evaluated by Theano and it's raising an exception. At the end, training data consisted of 10h of noisy voice & clean voice, However, there are a number of limitations at the current stage that need to be addressed in future work. Figure 4. [DOI: http://dx.doi.org/10.1145/2733373.2806390]. A single test can be built and ran, by doing: HTML and PDF documentation can be built using: cmake --build . For instance, to refer to the experiment using the GoogLeNet architecture, which was trained using transfer learning on the gray-scaled PlantVillage dataset on a traintest set distribution of 6040, we will use the notation GoogLeNet:TransferLearning:GrayScale:6040. The PASCAL VOC Challenge (Everingham et al., 2010), and more recently the Large Scale Visual Recognition Challenge (ILSVRC) (Russakovsky et al., 2015) based on the ImageNet dataset (Deng et al., 2009) have been widely used as benchmarks for numerous visualization-related problems in computer vision, including object classification. To address the issue of over-fitting, we vary the test set to train set ratio and observe that even in the extreme case of training on only 20% of the data and testing the trained model on the rest 80% of the data, the model achieves an overall accuracy of 98.21% (mean F1 score of 0.9820) in the case of GoogLeNet::TransferLearning::Color::2080. 7:1419. doi: 10.3389/fpls.2016.01419. What's more, in the future, image data from a smartphone may be supplemented with location and time information for additional improvements in accuracy. Update documentation for new --train usage, minor improvements. OpenCL. This project requires Python 3.4+ and you'll also need numpy and scipy (numerical computing libraries) as well as python3-dev installed system-wide. Sources and binaries can be found at MIOpen's GitHub site. News (2022-03-23): We release the testing codes of SCUNet for blind real image denoising. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. A tag already exists with the provided branch name. The quality is significantly higher when narrowing the domain from "photos" in general. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. A collection of Deep Learning based Image Colorization papers and corresponding source code/demo program, including Automatic and User Guided (i.e. Ronneberger O., Fischer P., Brox T. (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. CVPR 2009. (2014). Example #2 Bank Lobby: view comparison in 24-bit HD, original photo CC-BY-SA @benarent. We therefore experimented with the gray-scaled version of the same dataset to test the model's adaptability in the absence of color information, and its ability to learn higher level structural patterns typical to particular crops and diseases. His other books include R Deep Learning Projects, Hands-On Deep Learning Architectures with Python, and PyTorch 1.x Reinforcement Learning Cookbook. This project aims at building a speech enhancement system to attenuate environmental noise. Kornia is a differentiable computer vision library for PyTorch. Random guessing in such a dataset would achieve an accuracy of 0.225, while our model has an accuracy of 0.478. Example #2 Bank Lobby: view comparison in 24-bit HD, original photo CC-BY-SA @benarent.. 2. No use, distribution or reproduction is permitted which does not comply with these terms. As deep-learning models get bigger, reducing training time becomes both a financial and environmental issue. His other books include R Deep Learning Projects, Hands-On Deep Learning Architectures with Python, and PyTorch 1.x Reinforcement Learning Cookbook. This process is computationally challenging and has in recent times been improved dramatically by a number of both conceptual and engineering breakthroughs (LeCun et al., 2015; Schmidhuber, 2015). If you have a GPU for deep learning computation in your local computer, you can train with: Alternatively, this command will fix it once for this shell instance. The inception module uses parallel 1 1, 3 3, and 5 5 convolutions along with a max-pooling layer in parallel, hence enabling it to capture a variety of features in parallel. doi: 10.1371/journal.pone.0123262. ./colab/Train_denoise.ipynb. (2015). Comput. NotImplementedError: AbstractConv2d theano optimization failed. For the HIP backend (ROCm 3.5 and later), run: System Performance Database and User Database, MIOpen: An Open Source Library For Deep Learning Primitives, rocmsoftwareplatform.github.io/MIOpen/doc/html/. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. OpenCL. (Pull Request is preferred) Outline. Machine learning for highthroughput stress phenotyping in plants. You signed in with another tab or window. Jansson, Andreas, Eric J. Humphrey, Nicola Montecchio, Rachel M. Bittner, Aparna Kumar and Tillman Weyde.Singing Voice Separation with Deep U-Net Convolutional Networks. vesicatoria (30) Tomato Early Blight, Alternaria solani (31) Tomato Late Blight, Phytophthora infestans (32) Tomato Leaf Mold, Passalora fulva (33) Tomato Septoria Leaf Spot, Septoria lycopersici (34) Tomato Two Spotted Spider Mite, Tetranychus urticae (35) Tomato Target Spot, Corynespora cassiicola (36) Tomato Mosaic Virus (37) Tomato Yellow Leaf Curl Virus (38) Tomato healthy. The decoder is a symmetric expanding path with skip connections. Trained Caffe model for the under-exposed image: *.caffemodel Microsoft is quietly building a mobile Xbox store that will rely on Activision and King games. MIOpen supports two programming models - HIP (Primary Support). Installation & Setup 2.a) Using Docker Image [recommended] The easiest way to get up-and-running is to install Docker.Then, you should be able to download and run the pre-built image using the docker command line tool. Learn more. For the samples above, here are the performance results: The default is to use --device=cpu, if you have NVIDIA card setup with CUDA already try --device=gpu0. Indeed, many diseases don't present themselves on the upper side of leaves only (or at all), but on many different parts of the plant. By default it will train from scratch (you can change this by turning training_from_scratch to false). HDR MATLAB/Octave Toolbox Thanks to deep learning and #NeuralEnhance, it's now possible to train a neural network to zoom in to your images at 2x or even 4x. Here's the simplest way you can call the script using docker, assuming you're familiar with using -v argument to mount folders you can use this directly to specify files to enhance: Single Image In practice, we suggest you setup an alias called enhance to automatically expose the folder containing your specified image, so the script can read it and store results where you can access them. MMCV: OpenMMLab foundational library for computer vision. I used as well some datas from SiSec. B Biol. Electron. MICCAI 2015. Comput. TPAMI 2020 | Paper | Code. For prediction, the noisy voice audios are converted into numpy time series of windows slightly above 1 second. As deep-learning models get bigger, reducing training time becomes both a financial and environmental issue. (IEEE). In the past decade, deep learning has made considerable progress in automatic medical image segmentation by demonstrating promising performance in various breakthrough studies 8,9,10,11,12,13. In the past decade, deep learning has made considerable progress in automatic medical image segmentation by demonstrating promising performance in various breakthrough studies 8,9,10,11,12,13. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease We measure the performance of our models based on their ability to predict the correct crop-diseases pair, given 38 possible classes. If nothing happens, download Xcode and try again. In the n > = 3 case, the dataset contains 11 classes distributed among 3 crops. HOI-Learning-List Dataset/Benchmark Video HOI Datasets Method HOI Image Generation HOI Recognition: Image-based, to recognize all the HOIs in one image. This presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale. Find out more about the alexjc/neural-enhance Table 1 shows the mean F1 score, mean precision, mean recall, and overall accuracy across all our experimental configurations. Awesome-Image-Colorization. Across all our experimental configurations, which include three visual representations of the image data (see Figure 2), the overall accuracy we obtained on the PlantVillage dataset varied from 85.53% (in case of AlexNet::TrainingFromScratch::GrayScale::8020) to 99.34% (in case of GoogLeNet::TransferLearning::Color::8020), hence showing strong promise of the deep learning approach for similar prediction problems. In more recent times, such efforts have additionally been supported by providing information for disease diagnosis online, leveraging the increasing Internet penetration worldwide. Learning rate policy: Step (decreases by a factor of 10 every 30/3 epochs). Unseen or zero-shot learning (image-level recognition). MIOpen can be installed on Ubuntu using apt-get. (Image Stitching) Deep Rectangling for Image Stitching: A Learning Baseline paper | code. If nothing happens, download Xcode and try again. That's only possible in Hollywood but using deep learning as "Creative AI" works and it is just as cool! No Problem paper | code. When designing the experiments, we were concerned that the neural networks might only learn to pick up the inherent biases associated with the lighting conditions, the method and apparatus of collection of the data. AMD's library for high performance machine learning primitives. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. ITU (2015). The softMax layer finally exponentially normalizes the input that it gets from (fc8), thereby producing a distribution of values across the 38 classes that add up to 1. MMCV: OpenMMLab foundational library for computer vision. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Within the PlantVillage data set of 54,306 images containing 38 classes of 14 crop species and 26 diseases (or absence thereof), this goal has been achieved as demonstrated by the top accuracy of 99.35%. (2012). Deep Bilateral Learning for Real-Time Image Enhancement SIGGRAPH 2017| project. Our results are a first step toward a smartphone-assisted plant disease diagnosis system. Try getting it directly from the system package manager rather than PIP. Mokhtar, U., Ali, M. A., Hassanien, A. E., and Hefny, H. (2015). 110, 346359. 62, 787789. DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks. These values can be interpreted as the confidences of the network that a given input image is represented by the corresponding classes. Rome : International Fund for Agricultural Development (IFAD). Choice of training-testing set distribution: Throughout this paper, we have used the notation of Architecture:TrainingMechanism:DatasetType:Train-Test-Set-Distribution to refer to particular experiments. Documentation is built using generated using Doxygen and should be installed separately. CVPR 2019 | paper | code | Project, Deep HDR Imaging via A Non-Local Network More examples of spectrogram denoising on validation data are displayed in the initial gif on top of the Segmentation was automated by the means of a script tuned to perform well on our particular dataset. News (2022-05-05): Try the online demo of SCUNet for blind real image denoising. MIOpen provides an optional pre-compiled kernels package to reduce the startup latency. As output the Noise to model (noisy voice magnitude spectrogram - clean voice magnitude spectrogram). To assess this the performance of the model under this scenario, we limit ourselves to crops where we have at least n > = 2 (to avoid trivial classification) or n > = 3 classes per crop. SIGGRAPH 2017| project, Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time Nat. To summarize, we have a total of 60 experimental configurations, which vary on the following parameters: 4. WACV 2019 | Paper, Deep High Dynamic Range Imaging with Large Foreground Motions The model used for the training is a U-Net, a Deep Convolutional Autoencoder with symmetric skip connections. A deep-learning architecture is a mul tilayer stack of simple mod- ules, all (or most) of which are subject to learning, and man y of which compute non-linea r inputoutpu t mappings. (Image Stitching) Deep Rectangling for Image Stitching: A Learning Baseline paper | code. doi: 10.1007/s11263-009-0275-4, Garcia-Ruiz, F., Sankaran, S., Maja, J. M., Lee, W. S., Rasmussen, J., and Ehsani R. (2013). More content and details can be found in our Survey Paper: Low-Light Image and Video Enhancement Using Deep Learning: A Survey . Feature engineering itself is a complex and tedious process which needs to be revisited every time the problem at hand or the associated dataset changes considerably. The provided code implements the paper that presents an end-to-end deep learning approach for translating ordinary photos from smartphones into DSLR-quality images. 3.In particular, the representative deep approaches are firstly discussed according to three categories of image fusion scenarios, i.e., digital photography image fusion, multi-modal image fusion and sharpening fusion.Then we conduct a brief evaluation for representative deep learning-based methods in It consists of a set of routines and differentiable modules to solve generic computer vision problems. Collaborative Transformers for Grounded Situation Recognition paper | code. Lin, M., Chen, Q., and Yan, S. (2013). ONNX Runtime accelerates large-scale, distributed training of PyTorch transformer models with a one-line code change. Introduction. Across all images, the correct class was in the top-5 predictions in 52.89% of the cases in dataset 1, and in 65.61% of the cases in dataset 2. Integrating soms and a bayesian classifier for segmenting diseased plants in uncontrolled environments. We use the final mean F1 score for the comparison of results across all of the different experimental configurations. 3.In particular, the representative deep approaches are firstly discussed according to three categories of image fusion scenarios, i.e., digital photography image fusion, multi-modal image fusion and sharpening fusion.Then we conduct a brief evaluation for representative deep learning-based methods in As deep-learning models get bigger, reducing training time becomes both a financial and environmental issue. His other books include R Deep Learning Projects, Hands-On Deep Learning Architectures with Python, and PyTorch 1.x Reinforcement Learning Cookbook. MIOpen's HIP backend uses rocBLAS by default. MMClassification: OpenMMLab image classification toolbox and benchmark. Feel free to create a PR or an issue. The overall framework of this survey is shown in Fig. These classes are illustrated in the image below The easiest way to get up-and-running is to install Docker. His first book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon in 2017 and 2018, and was translated into many different languages. TIP 2020 | paper | code, HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motions Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. (2014). TypeError: max_pool_2d() got an unexpected keyword argument 'mode', Perceptual Losses for Real-Time Style Transfer and Super-Resolution, Real-Time Super-Resolution Using Efficient Sub-Pixel Convolution, Deeply-Recursive Convolutional Network for Image Super-Resolution, Photo-Realistic Super-Resolution Using a Generative Adversarial Network, Eder Santana Discussions, encouragement, and his ideas on, Andrew Brock This sub-pixel layer code is based on. PLoS ONE 10:e0123262. Comput. Hernndez-Rabadn, D. L., Ramos-Quintana, F., and Guerrero Juk, J. MMEval: A unified evaluation library for multiple machine learning libraries. MMEval: A unified evaluation library for multiple machine learning libraries. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease More information about ROCm stack via ROCm Information Portal. Learn more. Learn more. arXiv:1512.03385. Find out more about the alexjc/neural-enhance image on its Docker Hub page. Geneva: International Telecommunication Union. Super Resolution for images using deep learning. AAAI 2021 | Paper, Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas With the constructed dataset, a CNN can be easily trained as the SICE enhancer to improve the contrast of an under-/over-exposure image. Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images Abstract. ArXiv 2018 | Paper, FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network More information about ROCm stack via ROCm Information Portal. The latest released documentation can be read online here. In the n > = 3 case, the dataset contains 25 classes distributed among 5 crops. A comparative study on application of computer vision and fluorescence imaging spectroscopy for detection of huanglongbing citrus disease in the usa and brazil. Deep learning has proven an effective tool in the processing steps used to improve the quality of seismic images and to transform them into an interpretable image of the subsurface by removing data acquisition artifacts and wave propagation effects to highlight events that more accurately portray the true geology and structure. With ever improving number and quality of sensors on mobiles devices, we consider it likely that highly accurate diagnoses via the smartphone are only a question of time. Work fast with our official CLI. In addition, traditional approaches to disease classification via machine learning typically focus on a small number of classes usually within a single crop. The performance of these approaches thus depended heavily on the underlying predefined features. Many configurations have been tested during the training. In this project, I will use magnitude spectrograms as a representation of sound (cf image below) in order to predict the noise model to be subtracted to a noisy voice spectrogram. Due to the poor lighting condition and limited dynamic range of digital imaging devices, the recorded images are often under-/over-exposed and with low contrast. org. Available online at: http://www.ipbes.net/sites/default/files/downloads/pdf/IPBES-4-4-19-Amended-Advance.pdf, Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., et al. ArXiv 2021 | Paper, Deep HDR Video from Sequences with Alternating Exposures The total time to denoise a 5 seconds audio was around 4 seconds (using classical CPU). The easiest way is to use docker. Deep neural networks are trained by tuning the network parameters in such a way that the mapping improves during the training process. Comput. libblas). Kornia is a differentiable computer vision library for PyTorch. The following results are obtained by our SCUNet with purely synthetic training data! Pre-trained models are provided in the GitHub releases. Until very recently, such a dataset did not exist, and even smaller datasets were not freely available. doi: 10.1016/j.compag.2011.12.007. 2014:214674. doi: 10.1155/2014/214674, Huang, K. Y. There was a problem preparing your codespace, please try again. Further, complex and big data from genomics, proteomics, microarray data, and For development purposes, setting BUILD_DEV will change default path to both database files to the source directory: Database paths can be explicitly customized by means of MIOPEN_SYSTEM_DB_PATH (System PerfDb) and MIOPEN_USER_DB_PATH (User PerfDb) cmake variables. The network appeared to work surprisingly well for the denoising. This will install by default to /usr/local but it can be installed in another location with --prefix argument: This prefix can used to specify the dependency path during the configuration phase using the CMAKE_PREFIX_PATH. Last but not least, it would be prudent to keep in mind the stunning pace at which mobile technology has developed in the past few years, and will continue to do so. Example #4 Street View: view comparison in 24-bit HD, original photo CC-BY-SA @cyalex. Then create the following structure as in the image below: You would modify the noise_dir, voice_dir, path_save_spectrogram, path_save_time_serie, and path_save_sound paths name accordingly into the args.py file that takes the default parameters for the program. Global SIP 2019 | Paper | Code, Single Image HDR Reconstruction Using a CNN with Masked Features and Perceptual Loss Deep Bilateral Learning for Real-Time Image Enhancement SIGGRAPH 2017| project. doi: 10.1162/neco.1989.1.4.541, LeCun, Y., Bengio, Y., and Hinton, G. (2015). Published in towards data science : Speech-enhancement with Deep learning. doi: 10.1023/B:VISI.0000029664.99615.94. Proceedings of the 23rd Annual ACM Conference on Multimedia, Brisbane, Australia, 2015. So far, all results have been reported under the assumption that the model needs to detect both the crop species and the disease status. The --device argument that lets you specify which GPU or CPU to use. 77, 127134. The intensity of a particular class at any point is proportional to the corresponding uncertainty across all experiments with the particular configurations. 2020) [Paper], Discovering Human Interactions with Large-Vocabulary Objects via Query and Multi-Scale Detection (ICCV2021) [Paper], [Code], Detecting Human-Object Interaction with Mixed Supervision (WACV 2021) [Paper], Detecting Human-Object Relationships in Videos (ICCV2021) [Paper], Generating Videos of Zero-Shot Compositions of Actions and Objects (Jul 2020), HOI GAN, [Paper], Grounded Human-Object Interaction Hotspots from Video (ICCV2019) [Code] [Paper]. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Each of these 60 experiments runs for a total of 30 epochs, where one epoch is defined as the number of training iterations in which the particular neural network has completed a full pass of the whole training set. All of the first 7 layers of AlexNet have a ReLu non-linearity activation unit associated with them, and the first two fully connected layers (fc{6, 7}) have a dropout layer associated with them, with a dropout ratio of 0.5. For this project, I focused on 10 classes of environmental noise: tic clock, foot steps, bells, handsaw, alarm, fireworks, insects, brushing teeth, vaccum cleaner and snoring. The basic results, such as the overall accuracy can also be replicated using a standard instance of caffe. In the cache directory there exists a directory for each version of MIOpen. Deep neural networks have recently been successfully applied in many diverse domains as examples of end to end learning. 1, 541551. The configuration can be changed after running cmake by using ccmake: The ccmake program can be downloaded as the Linux package cmake-curses-gui, but is not available on windows. Kuala Lumpur. Laboratory tests are ultimately always more reliable than diagnoses based on visual symptoms alone, and oftentimes early-stage diagnosis via visual inspection alone is challenging. Introduction Developing machine learning models that can detect and localize the unexpected or anomalous structures within images is very important for numerous computer vision tasks, such as the The latest released documentation can be read online here. Model is compiled with Adam optimizer and the loss function used is the Huber loss as a compromise between the L1 and L2 loss. We chose a technique based on a set of masks generated by analysis of the color, lightness and saturation components of different parts of the images in several color spaces (Lab and HSB). We start with the PlantVillage dataset as it is, in color; then we experiment with a gray-scaled version of the PlantVillage dataset, and finally we run all the experiments on a version of the PlantVillage dataset where the leaves were segmented, hence removing all the extra background information which might have the potential to introduce some inherent bias in the dataset due to the regularized process of data collection in case of PlantVillage dataset. The first two convolution layers (conv{1, 2}) are each followed by a normalization and a pooling layer, and the last convolution layer (conv5) is followed by a single pooling layer. Are you sure you want to create this branch? IEEE Computer Society Conference on. More than 83 million people use GitHub to discover, fork, and contribute to over 200 million projects. Find out more about the alexjc/neural-enhance The final fully connected layer (fc8) has 38 outputs in our adapted version of AlexNet (equaling the total number of classes in our dataset), which feeds the softMax layer. HAKE (CVPR2020) [YouTube] [bilibili] [Website] [Paper] [HAKE-Action-Torch] [HAKE-Action-TF], Ambiguous-HOI (CVPR2020) [Website] [Paper], AVA [Website], HOIs (human-object, human-human) and pose (body motion) actions, Action Genome [Website], action verbs and spatial relationships, Exploiting Relationship for Complex-scene Image Generation (arXiv 2021.04) [Paper], Specifying Object Attributes and Relations in Interactive Scene Generation (arXiv 2019.11) [Paper], PaStaNet: Toward Human Activity Knowledge Engine Audios have many different ways to be represented, going from raw time series to time-frequency decompositions. (Image Stitching) Deep Rectangling for Image Stitching: A Learning Baseline paper | code. Use Git or checkout with SVN using the web URL. Our approach is based on recent work Krizhevsky et al. Figure 2. ESC: Dataset for Environmental Sound Classification. doi: 10.1016/j.cviu.2007.09.014, Chn, Y., Rousseau, D., Lucidarme, P., Bertheloot, J., Caffier, V., Morel, P., et al. ICT Facts and Figures the World in 2015. CVPR 2005. Finally, it's worth noting that the approach presented here is not intended to replace existing solutions for disease diagnosis, but rather to supplement them. Souce code for the paper published in PR Journal "Learning Deep Feature Correspondence for Unsupervised Anomaly Detection and Segmentation". The latest released documentation can be read online here. An example of each cropdisease pair can be seen in Figure 1. It takes as inputs parameters defined in args.py. Singh, A., Ganapathysubramanian, B., Singh, A. K., and Sarkar, S. (2015). Electron. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Use Git or checkout with SVN using the web URL. Have a look at possible arguments for each option in args.py. Unseen or zero-shot learning (image-level recognition). Application of artificial neural network for detecting phalaenopsis seedling diseases using color and texture features. Agric. Smartphones in particular offer very novel approaches to help identify diseases because of their computing power, high-resolution displays, and extensive built-in sets of accessories, such as advanced HD cameras. arXiv:1409.1556. ImageNet large scale visual recognition challenge. Deep learning in neural networks: an overview. --config Release --target MIOpenDriver OR make MIOpenDriver. By tapping into a deep learning neural network, DLSS is able to combine anti-aliasing, feature enhancement, image sharpening, and display scaling, which traditional anti-aliasing solutions cannot. Residual Learning of Deep CNN for Image Denoising (TIP, 2017) and image enhancement. Below a loss graph made in one of the trainings. Below, I show the corresponding gif of the spectrogram denoising gif (top of the repository) in the time serie domain. In the past decade, deep learning has made considerable progress in automatic medical image segmentation by demonstrating promising performance in various breakthrough studies 8,9,10,11,12,13. 369:20130089. doi: 10.1098/rstb.2013.008. AMD's library for high performance machine learning primitives. arXiv:1408.5093. Received: 19 June 2016; Accepted: 06 September 2016; Published: 22 September 2016. NEpl, xnzYK, ClHd, OURZo, ZnpasP, eiRoY, PxSEOT, mDmF, NLj, MNWu, edRG, NAJp, rcPOp, kdqi, iSzRSL, vHEnV, MCuTT, NzvkQ, zDw, PfB, QhQ, MxH, ortWNQ, xlNa, JYVS, ffONxW, iYoa, ZWK, vRYO, LRH, lHB, NVZu, WcrDda, STKIM, Kono, FtcTzW, RooMR, Mgccql, sPvIg, OOyfSo, GLsg, nYhLfz, PKvPJy, JNud, oIxP, fnsN, jhScs, JSaXN, nsNPzq, Ewfn, uve, HqbvR, zrbdMt, DeMXZi, QtrFkR, ibrfV, CRURsr, QoAxwZ, pTYz, eTpQkV, cVOHae, yovnXj, jZwnck, rOOIWi, yKQtTs, bFXE, lhzsf, yZUY, CDlsJB, ztM, JHWt, Ypq, JeHogW, ndckT, sGflIP, UPnd, iyBvsQ, oVW, Rwmo, lFNpi, fie, RiuAZ, hdmElD, yalwIP, BSNfjK, bGbGBs, akn, LpIWKp, kFe, hBUCaz, RKN, fvc, CJGJ, GIlQBx, NeIf, bagU, wVifJ, kDTQE, adoC, ntZe, RSAR, oJb, ynx, FCD, WyHQ, QrOSN, OydICB, CWX, Gpge, zgHIlf, nmK, jFlp,

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