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Deep Reinforced Attention Learning for Quality-Aware Visual Recognition [article]

Duo Li, Qifeng Chen
2020 arXiv   pre-print
to provide instant critique and revision for the temporary attention representation, hence coined as Deep REinforced Attention Learning (DREAL).  ...  Due to the discreteness of our designed reward, the proposed learning method is arranged in a reinforcement learning setting, where the attention actors and recurrent critics are alternately optimized  ...  Thus, the attention-aware features could adjust in a self-adaptive fashion as layers going deeper.  ... 
arXiv:2007.06156v1 fatcat:3bcwjclhararthwe3ignxdsyfi

Page 1144 of Psychological Abstracts Vol. 67, Issue 5 [page]

1982 Psychological Abstracts  
3 observed self-reinforcement while having an opportunity to use self-reinforcement.  ...  Program appraisal suggested that the Ss’ self- concepts were enhanced by the cultural awareness program. (12 ref) 10659. Cotterell, John L.  ... 

SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism [article]

Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Yuanxing Ning, Phillip S. Yu, Lifang He
2021 arXiv   pre-print
Graph representation learning has attracted increasing research attention.  ...  To differentiate subgraph representations among graphs, we present a self-supervised mutual information mechanism to encourage subgraph embedding to be mindful of the global graph structural properties  ...  sketched graph and learn subgraph embeddings by an attention mechanism and a self-supervised mutual information mechanism.  ... 
arXiv:2101.08170v3 fatcat:62nxzvfqfnb75ibuoo2b432x64

Contingency-Aware Exploration in Reinforcement Learning [article]

Jongwook Choi, Yijie Guo, Marcin Moczulski, Junhyuk Oh, Neal Wu, Mohammad Norouzi, Honglak Lee
2019 arXiv   pre-print
This paper investigates whether learning contingency-awareness and controllable aspects of an environment can lead to better exploration in reinforcement learning.  ...  The ADM is trained in a self-supervised fashion to predict the actions taken by the agent. The learned contingency information is used as a part of the state representation for exploration purposes.  ...  Self-supervised Dynamics Model and Controllable Dynamics.  ... 
arXiv:1811.01483v3 fatcat:2ock66anhfgehb7ihg667llfey

Human and animal perceptual learning: Some common and some unique features

C. J. Mitchell
2009 Learning & Behavior  
Some tentative suggestions are explored with regard to how animal learning theorists might meet these challenges. Finally, the role of awareness in perceptual learning is b briefly examined.  ...  A selective summary of the four contributions to this special issue of Learning & Behavior on perceptual r d learning is presented.  ...  That is, self-supervised learning occurs in humans, which serves to reinforce detection of the unique features, even when no explicit reinforcement is given. However, in Watanabe et al.'  ... 
doi:10.3758/lb.37.2.154 pmid:19380892 fatcat:4sujz5nspjaufdmkysjx4qtsua

Page 988 of Psychological Abstracts Vol. 52, Issue 4 [page]

1974 Psychological Abstracts  
Effects of immediate reinforcement and awareness of response on beginning counselor behavior. Counselor Education & Supervision, 1974(Mar), Vol. 13(3), 190-197.  ...  —Studied the effects of immediate reinforcement and awareness of response class on the acquisition of a complex counselor behavior by begin- ning counselors. 16 graduate students in counseling conducted  ... 

Page 69 of Social Work Vol. 1, Issue 1 [page]

1956 Social Work  
, “Self-Awareness in Profes- sional Education,” Social Casework, Vol.  ...  The process of self-awareness in professional learning does not proceed at the same rate for all workers, and thus supe”visors must take these individual needs into consideration.* The supervisor’s own  ... 

Page 69 of Social Work Vol. 1, Issue 1 [page]

1956 Social Work  
, “Self-Awareness in Profes- sional Education,” Social Casework, Vol.  ...  The process of self-awareness in professional learning does not proceed at the same rate for all workers, and thus supervisors must take these individual needs into consideration.’  ... 

Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles

Andrzej Cichocki, Alexander P. Kuleshov, Justin Dauwels
2021 Computational Intelligence and Neuroscience  
We describe various aspects of multiple human intelligences and learning styles, which may affect a variety of AI problem domains.  ...  concepts in developing a new generation of future Artificial General Intelligence (AGI) systems which relate to complex facets and different types of human intelligence, especially social, emotional, attentional  ...  In current AI systems, we extensively use six basic ways of learning: supervised learning, unsupervised learning, semisupervised learning, reinforcement learning, ensemble learning, and deep learning (  ... 
doi:10.1155/2021/8893795 fatcat:lxhcfzfbm5belbjihogf2cclda

Semi-Supervised Variational User Identity Linkage via Noise-Aware Self-Learning [article]

Chaozhuo Li, Senzhang Wang, Zheng Liu, Xing Xie, Lei Chen, Philip S. Yu
2021 arXiv   pre-print
Then, a noise-aware self-learning module is designed to faithfully augment the few available annotations, which is capable of filtering noises from the pseudo-labels generated by the supervised module.  ...  To address the mentioned limitations, in this paper we propose a novel Noise-aware Semi-supervised Variational User Identity Linkage (NSVUIL) model.  ...  Noise-Aware Self-Learning Module in which σ denotes the sigmoid function. Ωs and Ωt are the The learned supervised linkage module can locate confident trainable parameters.  ... 
arXiv:2112.07373v1 fatcat:e6zcnwrxifasbdmffrvqzmejiq

Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles [article]

Andrzej Cichocki, Alexander P. Kuleshov
2020 arXiv   pre-print
Key words: Artificial General Intelligence (AGI), multiple intelligences, learning styles, physical intelligence, emotional intelligence, social intelligence, attentional intelligence, moral-ethical intelligence  ...  , responsible decision making, creative-innovative intelligence, cognitive functions, meta-learning of AI systems.  ...  In current AI systems, we extensively use six basic ways of learning: Supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, ensemble learning and deep learning (  ... 
arXiv:2008.04793v4 fatcat:4l4wxa3bwnhlfbkfp2i63uwaou

Learning Object Relation Graph and Tentative Policy for Visual Navigation [article]

Heming Du, Xin Yu, Liang Zheng
2020 arXiv   pre-print
Specifically, trial-driven IL is a type of supervision used in policy network training, while TPN, mimicking the IL supervision in unseen environment, is applied in testing.  ...  In this task, it is critical to learn informative visual representation and robust navigation policy.  ...  [13] purpose a self-supervised approach to build a model of an environment through reinforcement learning. Wu et al . [28] propose a Bayesian relational memory to build room correlations.  ... 
arXiv:2007.11018v1 fatcat:73ftebycd5hvpkibloh2r3m45e

The Application of Ethical Decision-Making and Self-Awareness in the Counselor Education Classroom

Amanda M. Evans, Dana Heller Levitt, Stacy Henning
2012 Journal of Counselor Preparation and Supervision  
The authors provide an overview of ethical decision-making models and address the role of counselor self-awareness in the process.  ...  Practical recommendations for counselor educators to incorporate self-awareness and ethical decision making into the classroom are included.  ...  Supervision Considerations Self-awareness may be introduced in the classroom, but the practice must be reinforced through clinical training experiences and professional work throughout the career.  ... 
doi:10.7729/42.0029 fatcat:dbqgwj5pk5ft5mjlkyzohueebu

Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification

Haocong Rao, Siqi Wang, Xiping Hu, Mingkui Tan, Huang Da, Jun Cheng, Bin Hu
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
Finally, we propose Attention-based Gait Encodings (AGEs), which are built using context vectors learned by locality-aware attention, as final gait representations.  ...  Specifically, we first propose to introduce self-supervision by learning to reconstruct input skeleton sequences in reverse order, which facilitates learning richer high-level semantics and better gait  ...  Self-Supervised Skeleton Reconstruction To learn gait representations without labels, we propose to introduce self-supervision by learning to reconstruct input skeleton sequences in reverse order, i.e.  ... 
doi:10.24963/ijcai.2020/125 dblp:conf/ijcai/RaoW0TD0020 fatcat:2vhtel64gra5jp7yn4jo6ajnhi

A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-Identification [article]

Haocong Rao, Siqi Wang, Xiping Hu, Mingkui Tan, Yi Guo, Jun Cheng, Bin Hu, Xinwang Liu
2021 arXiv   pre-print
attention mechanism and a locality-aware contrastive learning scheme, which aim to preserve locality-awareness on intra-sequence level and inter-sequence level respectively during self-supervised learning  ...  Other pretext tasks are also explored to further improve self-supervised learning.  ...  during the self-supervised learning  ... 
arXiv:2009.03671v2 fatcat:e766hf4lovf6zenlggtdrs7yla
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