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Signal Processing for Big Data: Distributed signal and information processing for big data on networks

CrowNN: Human-in-the-loop Network with Crowd-generated Inputs


Input features are indispensable for almost all machine learning methods; however, their definitions themselves are sometimes too abstract to extract automatically. Human-in-the- loop machine learning is a promising solution to such cases where humans extract the feature values for machine learning models. We use crowdsourcing for feature value extraction and consider a problem to aggregate the feature values to improve machine learning classifiers.

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Authors:
Yukino Baba, Hisashi Kashima
Submitted On:
10 May 2019 - 4:36am
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CROWNN_poster_icassp2019

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[1] Yukino Baba, Hisashi Kashima, "CrowNN: Human-in-the-loop Network with Crowd-generated Inputs", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4289. Accessed: Aug. 25, 2019.
@article{4289-19,
url = {http://sigport.org/4289},
author = {Yukino Baba; Hisashi Kashima },
publisher = {IEEE SigPort},
title = {CrowNN: Human-in-the-loop Network with Crowd-generated Inputs},
year = {2019} }
TY - EJOUR
T1 - CrowNN: Human-in-the-loop Network with Crowd-generated Inputs
AU - Yukino Baba; Hisashi Kashima
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4289
ER -
Yukino Baba, Hisashi Kashima. (2019). CrowNN: Human-in-the-loop Network with Crowd-generated Inputs. IEEE SigPort. http://sigport.org/4289
Yukino Baba, Hisashi Kashima, 2019. CrowNN: Human-in-the-loop Network with Crowd-generated Inputs. Available at: http://sigport.org/4289.
Yukino Baba, Hisashi Kashima. (2019). "CrowNN: Human-in-the-loop Network with Crowd-generated Inputs." Web.
1. Yukino Baba, Hisashi Kashima. CrowNN: Human-in-the-loop Network with Crowd-generated Inputs [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4289

Provably Accelerated Randomized Gossip Algorithms


In this work we present novel provably accelerated gossip algorithms for solving the average consensus problem. The proposed protocols are inspired from the recently developed accelerated variants of the randomized Kaczmarz method - a popular method for solving linear systems. In each gossip iteration all nodes of the network update their values but only a pair of them exchange their private information. Numerical experiments on popular wireless sensor networks showing the benefits of our protocols are also presented.

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Authors:
Nicolas Loizou, Michael Rabbat, Peter Richtarik
Submitted On:
9 May 2019 - 4:21pm
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[1] Nicolas Loizou, Michael Rabbat, Peter Richtarik, "Provably Accelerated Randomized Gossip Algorithms", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4237. Accessed: Aug. 25, 2019.
@article{4237-19,
url = {http://sigport.org/4237},
author = {Nicolas Loizou; Michael Rabbat; Peter Richtarik },
publisher = {IEEE SigPort},
title = {Provably Accelerated Randomized Gossip Algorithms},
year = {2019} }
TY - EJOUR
T1 - Provably Accelerated Randomized Gossip Algorithms
AU - Nicolas Loizou; Michael Rabbat; Peter Richtarik
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4237
ER -
Nicolas Loizou, Michael Rabbat, Peter Richtarik. (2019). Provably Accelerated Randomized Gossip Algorithms. IEEE SigPort. http://sigport.org/4237
Nicolas Loizou, Michael Rabbat, Peter Richtarik, 2019. Provably Accelerated Randomized Gossip Algorithms. Available at: http://sigport.org/4237.
Nicolas Loizou, Michael Rabbat, Peter Richtarik. (2019). "Provably Accelerated Randomized Gossip Algorithms." Web.
1. Nicolas Loizou, Michael Rabbat, Peter Richtarik. Provably Accelerated Randomized Gossip Algorithms [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4237