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End-to-end principle

Known as: End to end argument, End-to-end argument, End-to-end connection 
The end-to-end principle is a classic design principle in computer networking. In networks designed according to the principle, application-specific… Expand
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2016
Highly Cited
2016
We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection… Expand
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Highly Cited
2016
Highly Cited
2016
Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end… Expand
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Highly Cited
2015
Highly Cited
2015
There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper… Expand
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Highly Cited
2014
Highly Cited
2014
Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although… Expand
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Highly Cited
2013
Highly Cited
2013
Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist… Expand
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Highly Cited
2002
Highly Cited
2002
TCP Westwood (TCPW) is a sender-side modification of the TCP congestion window algorithm that improves upon the performance of… Expand
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Highly Cited
2000
Highly Cited
2000
We present the design and implementation of an end-to-end architecture for Internet host mobility using dynamic updates to the… Expand
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Highly Cited
1996
Highly Cited
1996
This memorandum describes RTP, the real-time transport protocol. RTP provides end-to-end network transport functions suitable for… Expand
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Highly Cited
1993
Highly Cited
1993
This paper presents a scheme to implement congestion control at the gateway nodes. The superiority of such a scheme (congestion… Expand
Highly Cited
1984
Highly Cited
1984
This paper presents a design principle that helps guide placement of functions among the modules of a distributed computer system… Expand