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An Efficient Image Classification of Malaria Parasite Using Convolutional Neural Network and ADAM Optimizer
2021
Turkish Journal of Computer and Mathematics Education
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This paper is used for observation of protozoan infection with a deep learning idea. ...
Machine learning can be a technique of nursing lysis that automatically develops an analytical model. ...
can be extremely efficient, low-cost and scalable. ...
doi:10.17762/turcomat.v12i2.2398
fatcat:nx2ejttqyncbrnnbxhji5a4b5a
Building Watson: An Overview of the DeepQA Project
2010
The AI Magazine
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IBM Research undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV Quiz show, Jeopardy! ...
Our results strongly suggest that DeepQA is an effective and extensible architecture that may be used as a foundation for combining, deploying, evaluating and advancing a wide range of algorithmic techniques ...
and develop all aspects of Watson and the DeepQA architecture. ...
doi:10.1609/aimag.v31i3.2303
fatcat:bqzu6kcak5gnhpabvyv6vuc7n4
The DARPA High-Performance Knowledge Bases Project
1998
The AI Magazine
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The project is supported by the Defense Advanced Research Projects Agency and includes more than 15 contractors in universities, research laboratories, and companies. ...
s Now completing its first year, the High-Performance Knowledge Bases Project promotes technology for developing very large, flexible, and reusable knowledge bases. ...
Definitive information on the year 1 crisis-management challenge problem, including updated specification, evaluation procedures, and evaluation results, is available at www.iet. com/Projects/HPKB/Y1Eval ...
doi:10.1609/aimag.v19i4.1423
dblp:journals/aim/CohenSJPLSGB98
fatcat:je5iyiv3zrcgrnee5w4asydnou
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
[article]
2018
arXiv
pre-print
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Uncertainty computation in deep learning is essential to design robust and reliable systems. ...
the learning rate. ...
Finally, we are thankful for the RAIDEN computing system at the RIKEN Center for AI Project, which we extensively used for our experiments. ...
arXiv:1806.04854v3
fatcat:i3oye53djngihi6atih3bvti7m
Project repositories for machine learning with TensorFlow
2020
Procedia Computer Science
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Models learn during the training phase; an iterative process in which parameters are tuned to improve the prediction accuracy. ...
Models learn during the training phase; an iterative process in which parameters are tuned to improve the prediction accuracy. ...
It is a platform for reproducible and scalable Machine Learning and Deep Learning applications by providing an interactive workspace with notebooks, Tensorboards, visualizations, and dashboards. ...
doi:10.1016/j.procs.2020.04.020
fatcat:tin3ivao3fanlbzywhd6lc72ty
Perceive Project Deliverable Report On Urban Policies For Building Smart Cities
2017
Zenodo
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This analysis helps to systematically unveil the narrative used by practitioners and policy makers in shaping the perception of smart city policies and projects. ...
A reasonably well-rounded and comprehensive definition of a smart city may be found in Caraglui et al (2011) who assert that a city is smart when ".....investments in human and societal capital and traditional ...
The EnerGAware solution will provide an innovative IT ecosystem in which users can play to learn about the potential energy savings from installing energy-efficiency measures and changing user behaviour ...
doi:10.5281/zenodo.821094
fatcat:qmvxv3l3zffj7lbthjerbxvoby
VELOC: VEry Low Overhead Checkpointing in the Age of Exascale
[article]
2021
arXiv
pre-print
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applications and systems. ...
VeloC offers a simple API at user level, while employing an advanced multi-level resilience strategy that transparently optimizes the performance and scalability of checkpointing by leveraging heterogeneous ...
In this case, approaches such as [7] build deep learning ensembles and workflows to construct a system for automatically identifying data subsets that have a large impact on the trained models. ...
arXiv:2103.02131v1
fatcat:53tvxe2iszde5gwkr4dy6gxeeq
PyTorch: An Imperative Style, High-Performance Deep Learning Library
[article]
2019
arXiv
pre-print
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Deep learning frameworks have often focused on either usability or speed, but not both. ...
PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy ...
Acknowledgements We are grateful to the PyTorch community for their feedback and contributions that greatly influenced the design and implementation of PyTorch. ...
arXiv:1912.01703v1
fatcat:von3pwhvhbcjtcyo2ht6wnwygu
Neural Point Cloud Rendering via Multi-Plane Projection
[article]
2020
arXiv
pre-print
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We present a new deep point cloud rendering pipeline through multi-plane projections. ...
The layered images are then blended based on learned weights to produce the final rendering results. ...
Data preparation and training details For each scene, our network is trained for 21 epochs over 1,925 images on average, using Adam optimizer [27] . ...
arXiv:1912.04645v2
fatcat:qg6ogniob5brvmxwuni7jgdxca
Deep Speech: Scaling up end-to-end speech recognition
[article]
2014
arXiv
pre-print
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We present a state-of-the-art speech recognition system developed using end-to-end deep learning. ...
Key to our approach is a well-optimized RNN training system that uses multiple GPUs, as well as a set of novel data synthesis techniques that allow us to efficiently obtain a large amount of varied data ...
Acknowledgments We are grateful to Jia Lei, whose work on DL for speech at Baidu has spurred us forward, for his advice and support throughout this project. ...
arXiv:1412.5567v2
fatcat:cfqvlbcrbbh23ingwt4zmnz2ka
Lessons in Project Management: How Projects Can Go Right or Wrong Examples from Global Companies
[article]
2022
Zenodo
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The idea for the book arose while teaching project management as a component information systems management to M.B.A. students. ...
This work introduces readers, students of project management and practitioners to a number of global companies and projects, that went right and/or wrong. ...
looking for scalability of operations and an advantage. ...
doi:10.5281/zenodo.5982293
fatcat:5wzicvpbq5f6tdhlmd5sdar5oq
Generating High Resolution Climate Change Projections through Single Image Super-Resolution: An Abridged Version
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. ...
Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. ...
Each network is trained using Adam optimization [Kingma and Ba, 2014] with a learning rate of 10 −4 for the first two layers and 10 −5 for the last layers. ...
doi:10.24963/ijcai.2018/759
dblp:conf/ijcai/VandalKGMNG18
fatcat:wytynt2havcvlj5giuovlnd63y
Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning
2019
Nature Biomedical Engineering
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Here we show that a deep-learning model trained to map projection radiographs of a patient to the corresponding 3D anatomy can subsequently generate volumetric tomographic X-ray images of the patient from ...
Volumetric reconstruction via deep learning could be useful in image-guided interventional procedures such as radiation therapy and needle biopsy, and might help simplify the hardware of tomographic imaging ...
Acknowledgements This research is partially supported by NIH (R01CA176553 and R01EB016777). ...
doi:10.1038/s41551-019-0466-4
pmid:31659306
pmcid:PMC6858583
fatcat:omcbj45befdxnlzg2m4jmy4bqa
Learning Feature Representations with K-Means
[chapter]
2012
Lecture Notes in Computer Science
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Many algorithms are available to learn deep hierarchies of features from unlabeled data, especially images. ...
., neural networks) that are sometimes tricky to train and tune and are difficult to scale up to many machines effectively. ...
)
Conclusion In this chapter we have reviewed many results, observations and tricks that are useful for building feature-learning systems with K-means as a scalable unsupervised learning module. ...
doi:10.1007/978-3-642-35289-8_30
fatcat:aqax4bbthfhqbbthvzstpjjj6a
Defense Advanced Research Projects Agency (Darpa) Fiscal Year 2016 Budget Estimates
2015
Zenodo
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The Defense Advanced Research Projects Agency (DARPA) FY2016 amounted to $2.868 billion in the President's request to support high-risk, high-reward research. ...
Recent breakthroughs in deep learning, sparse representations, manifold learning, and embedded systems offer promise for dramatic improvements in ATR. ...
ADAMS will develop flexible, scalable, and highly interactive approaches to extracting actionable information from information system log files, sensors, and other instrumentation. ...
doi:10.5281/zenodo.1215366
fatcat:cqn5tyfixjanzp5x3tgfkpedri
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