Skip to search form
Skip to main content
Skip to account menu
Semantic Scholar
Semantic Scholar's Logo
Search 217,274,198 papers from all fields of science
Search
Sign In
Create Free Account
End-to-end principle
Known as:
End to end argument
, End-to-end argument
, End-to-end connection
Expand
The end-to-end principle is a classic design principle in computer networking. In networks designed according to the principle, application-specific…
Expand
Wikipedia
(opens in a new tab)
Create Alert
Alert
Related topics
Related topics
41 relations
Acknowledgement (data networks)
Application delivery network
Argus - Audit Record Generation and Utilization System
Automatic repeat request
Expand
Broader (2)
Net neutrality
Network architecture
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2018
Highly Cited
2018
CBAM: Convolutional Block Attention Module
Sanghyun Woo
,
Jongchan Park
,
Joon-Young Lee
,
In-So Kweon
European Conference on Computer Vision
2018
Corpus ID: 49867180
We propose Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional…
Expand
Highly Cited
2016
Highly Cited
2016
XGBoost: A Scalable Tree Boosting System
Tianqi Chen
,
Carlos Guestrin
Knowledge Discovery and Data Mining
2016
Corpus ID: 4650265
Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end…
Expand
Highly Cited
2015
Highly Cited
2015
U-Net: Convolutional Networks for Biomedical Image Segmentation
O. Ronneberger
,
P. Fischer
,
T. Brox
International Conference on Medical Image…
2015
Corpus ID: 3719281
There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper…
Expand
Highly Cited
2015
Highly Cited
2015
You Only Look Once: Unified, Real-Time Object Detection
Joseph Redmon
,
S. Divvala
,
Ross B. Girshick
,
Ali Farhadi
Computer Vision and Pattern Recognition
2015
Corpus ID: 206594738
We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection…
Expand
Highly Cited
2015
Highly Cited
2015
Continuous control with deep reinforcement learning
T. Lillicrap
,
Jonathan J. Hunt
,
+5 authors
Daan Wierstra
International Conference on Learning…
2015
Corpus ID: 16326763
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model…
Expand
Highly Cited
2014
Highly Cited
2014
Sequence to Sequence Learning with Neural Networks
I. Sutskever
,
O. Vinyals
,
Quoc V. Le
Neural Information Processing Systems
2014
Corpus ID: 7961699
Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although…
Expand
Highly Cited
2013
Highly Cited
2013
Speech recognition with deep recurrent neural networks
Alex Graves
,
Abdel-rahman Mohamed
,
Geoffrey E. Hinton
IEEE International Conference on Acoustics…
2013
Corpus ID: 206741496
Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist…
Expand
Highly Cited
2002
Highly Cited
2002
TCP Westwood: End-to-End Congestion Control for Wired/Wireless Networks
C. Casetti
,
M. Gerla
,
S. Mascolo
,
M. Sanadidi
,
Ren Wang
Wirel. Networks
2002
Corpus ID: 14816486
TCP Westwood (TCPW) is a sender-side modification of the TCP congestion window algorithm that improves upon the performance of…
Expand
Highly Cited
2000
Highly Cited
2000
An end-to-end approach to host mobility
A. Snoeren
,
Hari Balakrishnan
ACM/IEEE International Conference on Mobile…
2000
Corpus ID: 263869128
We present the design and implementation of an end-to-end architecture for Internet host mobility using dynamic updates to the…
Expand
Highly Cited
1984
Highly Cited
1984
End-to-end arguments in system design
J. Saltzer
,
D. Reed
,
D. Clark
TOCS
1984
Corpus ID: 215746877
This paper presents a design principle that helps guide placement of functions among the modules of a distributed computer system…
Expand
By clicking accept or continuing to use the site, you agree to the terms outlined in our
Privacy Policy
(opens in a new tab)
,
Terms of Service
(opens in a new tab)
, and
Dataset License
(opens in a new tab)
ACCEPT & CONTINUE