On the Efficiency of Decentralized Search in Expert Networks

2016 ◽  
Author(s):  
Liang Ma ◽  
Mudhakar Srivatsa ◽  
Derya Cansever ◽  
Xifeng Yan ◽  
Sue Kase ◽  
...  
Keyword(s):  
Decentralized Search ◽  
Expert Networks

Performance Bounds of Decentralized Search in Expert Networks for Query Answering

2019 ◽  
Vol 13 (2) ◽  
pp. 1-23
Author(s):  
Liang Ma ◽  
Mudhakar Srivatsa ◽  
Derya Cansever ◽  
Xifeng Yan ◽  
Sue Kase ◽  
...  
Keyword(s):  
Query Answering ◽  
Performance Bounds ◽  
Decentralized Search ◽  
Expert Networks

Robust learning in expert networks: a comparative analysis

2018 ◽  
Vol 51 (2) ◽  
pp. 207-234 ◽  
Author(s):  
Ashiqur R. KhudaBukhsh ◽  
Jaime G. Carbonell ◽  
Peter J. Jansen
Keyword(s):  
Comparative Analysis ◽  
Robust Learning ◽  
Expert Networks

scholarly journals Computational insights into human perceptual expertise for familiar and unfamiliar face recognition

2019 ◽  
Author(s):  
Nicholas Blauch ◽  
Marlene Behrmann ◽  
David C. Plaut
Keyword(s):  
Face Recognition ◽  
Theoretical Work ◽  
Fine Tuning ◽  
Identity Verification ◽  
High Performing ◽  
Face Identity ◽  
Unfamiliar Face ◽  
Perceptual Representations ◽  
Expert Networks ◽  
Identity Perception

Humans are generally thought to be experts at face recognition, and yet identity perception for unfamiliar faces is surprisingly poor compared to that for familiar faces. Prior theoretical work has argued that unfamiliar face identity perception suffers because the majority of identity-invariant visual variability is idiosyncratic to each identity, and thus, each face identity must be learned essentially from scratch. Using a high-performing deep convolutional neural network, we evaluate this claim by examining the effects of visual experience in untrained, object-expert and face-expert networks. We found that only face training led to substantial generalization in an identity verification task of novel unfamiliar identities. Moreover, generalization increased with the number of previously learned identities, highlighting the generality of identity-invariant information in face images. To better understand how familiarity builds upon generic face representations, we simulated familiarization with face identities by fine-tuning the network on images of the previously unfamiliar identities. Familiarization produced a sharp boost in verification, but only approached ceiling performance in the networks that were highly trained on faces. Moreover, in these face-expert networks, the sharp familiarity benefit was seen only at the identity-based output layer, and did not depend on changes to perceptual representations; rather, familiarity effects required learning only at the level of identity readout from a fixed expert representation. Our results thus reconcile the existence of a large familiar face advantage with claims that both familiar and unfamiliar face identity processing depend on shared expert perceptual representations.

scholarly journals Decentralized Search and Track with Multiple Autonomous Agents

2019 ◽  
Author(s):  
Savvas Papaioannou ◽  
Panayiotis Kolios ◽  
Theocharis Theocharides ◽  
Christos G. Panayiotou ◽  
Marios M. Polycarpou
Keyword(s):  
Autonomous Agents ◽  
Decentralized Search
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