AMLN: Adversarial-Based Mutual Learning Network for Online Knowledge Distillation

2020 ◽  
pp. 158-173 ◽  
Author(s):  
Xiaobing Zhang ◽  
Shijian Lu ◽  
Haigang Gong ◽  
Zhipeng Luo ◽  
Ming Liu
Keyword(s):  
Mutual Learning ◽  
Learning Network ◽  
Knowledge Distillation

scholarly journals Mutual-learning sequence-level knowledge distillation for automatic speech recognition

2021 ◽  
Vol 428 ◽  
pp. 259-267
Author(s):  
Zerui Li ◽  
Yue Ming ◽  
Lei Yang ◽  
Jing-Hao Xue
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Learning Sequence ◽  
Mutual Learning ◽  
Knowledge Distillation

Deep mutual learning network for gait recognition

2020 ◽  
Vol 79 (31-32) ◽  
pp. 22653-22672
Author(s):  
Yanchun Wang ◽  
Zhenxue Chen ◽  
Q. M. Jonathan Wu ◽  
Xuewen Rong
Keyword(s):  
Gait Recognition ◽  
Mutual Learning ◽  
Learning Network

Coexistence of Pluralities through Practices of Intercultural Relationships

10.1558/37327 ◽  
2018 ◽  
Vol 2 (2) ◽  
pp. 155-176 ◽  
Author(s):  
Jorge E. Castillo Guerra
Keyword(s):  
Latin American ◽  
Learning Processes ◽  
Intercultural Relationships ◽  
Mutual Learning ◽  
Cultural Orientations ◽  
Intercultural Philosophy

This article investigates how migrants and refugees contribute to forms of co-existence among peoples with different religious and cultural orientations. Drawing on theories of intercultural philosophy and decolonial thinking, the author focuses on transformations of identity and faith among Catholic Latin American migrants in Europe and the United Sates of America. He argues that when these migrants encounter exclusion and uprooting, processes of transformation converge in parish communities. There they create mutual learning processes leading to new intercultural practices such as the deaconry of culture and relationship.

DESAIN PEMBELAJARAN UNTUK TRANSFORMASI SOSIAL (Studi Perbandingan Pemikiran Paulo Freire dan Ivan Illich tentang Pendidikan Pembebasan)

1970 ◽  
Vol 8 (2) ◽  
pp. 113-128
Author(s):  
Muh. Hanif
Keyword(s):  
Social Transformation ◽  
Paulo Freire ◽  
Technical Skills ◽  
Stable System ◽  
Open Learning ◽  
Learning Network ◽  
Contemporary Education ◽  
Ivan Illich

Paulo Freire and Ivan Illich are prominent figures in contemporary education, who broke the stable system of education. Paulo Freire suggests to stop bank style education and to promote andragogy education, which views both teacher and students equally. Education should be actualized through facing problems and should be able to omit naïve and magic awareness replaced with critical and transformative awareness. Different from Freire, Illich offers to free the society from formal schools. Education should be run in an open learning network. Technical skills can be taught by drilling. In addition, social transformation will happen only if there are epimethean people that are minority in existence.

Convolutional Dictionary Pair Learning Network for Image Representation Learning.pdf

2019 ◽  
Author(s):  
Zhao Zhang ◽  
Yulin Sun ◽  
Yang Wang ◽  
Zhengjun Zha ◽  
Shuicheng Yan ◽  
...  
Keyword(s):  
Image Representation ◽  
Learning Network

10 pages, 6 figures

Conditional Response Augmentation for Dialogue Using Knowledge Distillation

2020 ◽  
Author(s):  
Myeongho Jeong ◽  
Seungtaek Choi ◽  
Hojae Han ◽  
Kyungho Kim ◽  
Seung-won Hwang
Keyword(s):  
Conditional Response ◽  
Knowledge Distillation

A Human/Computer Learning Network to Improve Biodiversity Conservation and Research

AI Magazine ◽  
2012 ◽  
Vol 34 (1) ◽  
pp. 10 ◽  
Author(s):  
Steve Kelling ◽  
Jeff Gerbracht ◽  
Daniel Fink ◽  
Carl Lagoze ◽  
Weng-Keen Wong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Feedback Loop ◽  
Human Computation ◽  
Computational Power ◽  
Learning Networks ◽  
Learning Network ◽  
Computer Learning ◽  
The Individual ◽  
Citizen Science Project

In this paper we describe eBird, a citizen-science project that takes advantage of the human observational capacity to identify birds to species, which is then used to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a Human-Computer Learning Network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both, and thereby continually improves the effectiveness of the network as a whole. In this paper we explore how Human-Computer Learning Networks can leverage the contributions of a broad recruitment of human observers and processes their contributed data with Artificial Intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.

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