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Bottom-up proteomics
Known as:
Bottom-up
Bottom-up proteomics is a common method to identify proteins and characterize their amino acid sequences and post-translational modifications by…
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Broader (1)
Bioinformatics
Proteomics
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2013
Highly Cited
2013
Saliency Detection via Graph-Based Manifold Ranking
Chuan Yang
,
L. Zhang
,
Huchuan Lu
,
Xiang Ruan
,
Ming-Hsuan Yang
IEEE Conference on Computer Vision and Pattern…
2013
Corpus ID: 10024504
Most existing bottom-up methods measure the foreground saliency of a pixel or region based on its contrast within a local context…
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Highly Cited
2009
Highly Cited
2009
Learning to predict where humans look
Tilke Judd
,
Krista A. Ehinger
,
F. Durand
,
A. Torralba
IEEE International Conference on Computer Vision
2009
Corpus ID: 16445820
For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a…
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Highly Cited
2009
Highly Cited
2009
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Honglak Lee
,
R. Grosse
,
R. Ranganath
,
A. Ng
International Conference on Machine Learning
2009
Corpus ID: 12008458
There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks. Scaling…
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Highly Cited
2005
Highly Cited
2005
A Multilevel Model of Resistance to Information Technology Implementation
L. Lapointe
,
S. Rivard
MIS Q.
2005
Corpus ID: 10417384
To better explain resistance to information technology implementation, we used a multilevel, longitudinal approach. We first…
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Highly Cited
2000
Highly Cited
2000
A Bayesian Computer Vision System for Modeling Human Interactions
N. Oliver
,
Barbara Rosario
,
A. Pentland
IEEE Transactions on Pattern Analysis and Machine…
2000
Corpus ID: 1545504
We describe a real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual…
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Highly Cited
1994
Highly Cited
1994
Genetic Programming II: Automatic Discovery of Reusable Programs.
Una-May O’Reilly
Artificial Life
1994
Corpus ID: 31795221
Reading Genetic Programming IE Automatic Discovery ofReusable Programs (GPII) in its entirety is not a task for the weak-willed…
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Highly Cited
1993
Highly Cited
1993
Magnetic bistability in a metal-ion cluster
R. Sessoli
,
D. Gatteschi
,
A. Caneschi
,
M. Novak
Nature
1993
Corpus ID: 4235125
MAGNETIC materials of mesoscopic dimensions (a few to many thousands of atoms) may exhibit novel and useful properties such as…
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Highly Cited
1987
Highly Cited
1987
Three-Dimensional Object Recognition from Single Two-Dimensional Images
D. Lowe
Artificial Intelligence
1987
Corpus ID: 678619
Highly Cited
1987
Highly Cited
1987
Competitive Learning: From Interactive Activation to Adaptive Resonance
S. Grossberg
Cognitive Sciences
1987
Corpus ID: 17690372
Functional and mechanistic comparisons are made between several network models of cognitive processing: competitive learning…
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Highly Cited
1970
Highly Cited
1970
An efficient context-free parsing algorithm
J. Earley
CACM
1970
Corpus ID: 35664
A parsing algorithm which seems to be the most efficient general context-free algorithm known is described. It is similar to both…
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