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High- and low-level
Known as:
High-level description
, Low-level
, High-level
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High-level and low-level are typically terms used to classify, describe and point to specific goals of a systematic operation, though its uses also…
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Related topics
Related topics
21 relations
ALSA
Ant colony
Assembly language
Complex system
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Broader (3)
Cybernetics
Formal methods
Systems theory
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2017
Highly Cited
2017
Feature Pyramid Networks for Object Detection
Tsung-Yi Lin
,
Piotr Dollár
,
Ross B. Girshick
,
Kaiming He
,
Bharath Hariharan
,
Serge J. Belongie
IEEE Conference on Computer Vision and Pattern…
2017
Corpus ID: 10716717
Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid…
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Highly Cited
2011
Highly Cited
2011
Scikit-learn: Machine Learning in Python
Fabian Pedregosa
,
G. Varoquaux
,
+14 authors
E. Duchesnay
J. Mach. Learn. Res.
2011
Corpus ID: 10659969
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale…
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Review
2011
Review
2011
Classification and regression trees
W. Loh
WIREs Data Mining Knowl. Discov.
2011
Corpus ID: 17654166
Classification and regression trees are machine‐learning methods for constructing prediction models from data. The models are…
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Highly Cited
2011
Highly Cited
2011
Adaptive deconvolutional networks for mid and high level feature learning
Matthew D. Zeiler
,
Graham W. Taylor
,
R. Fergus
International Conference on Computer Vision
2011
Corpus ID: 975170
We present a hierarchical model that learns image decompositions via alternating layers of convolutional sparse coding and max…
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Review
2010
Review
2010
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot
,
Yoshua Bengio
AISTATS
2010
Corpus ID: 5575601
Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms…
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Highly Cited
2004
Highly Cited
2004
Learning Low-Level Vision
W. Freeman
,
E. Pasztor
,
Owen Carmichael
International Journal of Computer Vision
2004
Corpus ID: 1414109
We describe a learning-based method for low-level vision problems—estimating scenes from images. We generate a synthetic world of…
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Highly Cited
2003
Highly Cited
2003
Inferring High-Level Behavior from Low-Level Sensors
Donald J. Patterson
,
Lin Liao
,
D. Fox
,
Henry A. Kautz
UbiComp
2003
Corpus ID: 6443271
We present a method of learning a Bayesian model of a traveler moving through an urban environment. This technique is novel in…
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Highly Cited
2002
Highly Cited
2002
Mean Shift: A Robust Approach Toward Feature Space Analysis
D. Comaniciu
,
P. Meer
IEEE Trans. Pattern Anal. Mach. Intell.
2002
Corpus ID: 691081
A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate…
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Highly Cited
1999
Highly Cited
1999
Learning low-level vision
W. Freeman
,
E. Pasztor
Proceedings of the Seventh IEEE International…
1999
Corpus ID: 61165578
We show a learning-based method for low-level vision problems-estimating scenes from images. We generate a synthetic world of…
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Review
1989
Review
1989
Petri nets: Properties, analysis and applications
T. Murata
1989
Corpus ID: 62750145
Starts with a brief review of the history and the application areas considered in the literature. The author then proceeds with…
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