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Learning Intuitive Physics and One-Shot Imitation Using State-Action-Prediction Self-Organizing Maps
2021
Computational Intelligence and Neuroscience
Preserved Fulltext
Human learning and intelligence work differently from the supervised pattern recognition approach adopted in most deep learning architectures. ...
For this, a set of self-organizing maps which represent state-action pairs is combined with a causal model for sequence prediction. The proposed system is evaluated in the cartpole environment. ...
Acknowledgments e authors wish to thank Monika Stetter for numerous valuable discussions on the subject. MS was on sabbatical leave joining the CIML Lab at the University of Regensburg. ...
doi:10.1155/2021/5590445
pmid:34804145
pmcid:PMC8604601
fatcat:o5odao4rnzaaxpvjhhikopwbnu
Learning intuitive physics and one-shot imitation using state-action-prediction self-organizing maps
[article]
2021
arXiv
pre-print
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Human learning and intelligence work differently from the supervised pattern recognition approach adopted in most deep learning architectures. ...
For this, a set of self-organizing maps which represent state-action pairs is combined with a causal model for sequence prediction. The proposed system is evaluated in the cartpole environment. ...
Acknowledgments The authors wish to thank Monika Stetter for numerous valuable discussions on the subject. MS was on sabbatical leave joining the CIML Lab at University of Regensburg. ...
arXiv:2007.01647v3
fatcat:wrv62evc25b4rbzlgu7b6j2zjm
On the Learnability of Physical Concepts: Can a Neural Network Understand What's Real?
[article]
2022
arXiv
pre-print
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DeepFakes and spoofing highlight the feebleness of the link between physical reality and its abstract representation, whether learned by a digital computer or a biological agent. ...
On the other hand, architectures that incorporate recursion can represent a significantly larger class of concepts, but may still be unable to learn them from a finite dataset. ...
However, for a shared dictionary to emerge, agents must be immersed in a shared medium, and form independent representations of physical concepts that are simultaneously experienced through independent ...
arXiv:2207.12186v2
fatcat:nratucibkneunlloqe3vqwok4m
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
[article]
2021
arXiv
pre-print
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Contrary to viewing embodied intelligence as another application domain for machine learning, here we argue that it is in fact a key driver for the advancement of machine learning technology. ...
In particular, the purview of embodied intelligent agents extends significantly beyond the typical considerations of main-stream machine learning approaches, which typically (i) do not consider operation ...
Leslie Kaelbling assisted with several ideas in this paper, and her considerable time and help is also very gratefully acknowledged. ...
arXiv:2110.15245v1
fatcat:juxc4tai2jbklpul55loccnp7e
Communication-Efficient Edge AI: Algorithms and Systems
[article]
2020
arXiv
pre-print
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In this paper, we present a comprehensive survey of the recent developments in various techniques for overcoming these communication challenges. ...
Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields, ranging from speech processing, image classification to drug discovery. ...
Zhi Ding from the University of California at Davis for insightful and constructive comments to improve the presentation of this work. ...
arXiv:2002.09668v1
fatcat:nhasdzb7t5dt5brs2r7ocdzrnm
Machine learning & artificial intelligence in the quantum domain: a review of recent progress
2018
Reports on progress in physics (Print)
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For instance, quantum computing is finding a vital application in providing speed-ups for machine learning problems, critical in our "big data" world. ...
This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. ...
In contrast to such strictly behavioral and operational points of view, an alternative approach towards whole agents (or complete intelligent agents) focuses on agent architectures and cognitive architectures ...
doi:10.1088/1361-6633/aab406
pmid:29504942
fatcat:mub6kfcmnrgnpebtbf35lycyly
Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework
2022
Computational Intelligence and Neuroscience
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Mobile edge computing (MEC) is a new era of digital communication and has a rising demand for intelligent devices and applications. ...
The framework is basically a merger of a deep learning (DL) agent deployed at the network edge with a quantum memory module (QMM). ...
of R, d, and w E at a specific time, quantum t is shared in Step 1. ...
doi:10.1155/2022/6138434
pmid:35035461
pmcid:PMC8759837
fatcat:gzjswvt6djcmjihksu5x3hs3de
Intelligent D-Band wireless systems and networks initial designs
2021
Zenodo
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The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, ...
This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. ...
Multi-Agent Reinforcement Learning Multi-agent reinforcement learning is a subfield of reinforcement learning that focuses on systems with multiple independent learning agents acting in a shared environment ...
doi:10.5281/zenodo.5718378
fatcat:l3rkacgotzazha564nqm6wchiy
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
[article]
2019
arXiv
pre-print
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In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management ...
However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of "providing artificial intelligence for every person and every organization at everywhere ...
For the downlink, the weights of the global DL model are reshaped into a vector, and then subsampling and quantization are applied [203] . ...
arXiv:1907.08349v2
fatcat:4hfqgdto4fhvlguwfjxuz3ik5q
Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration
[article]
2022
arXiv
pre-print
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To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. ...
In this article, we study orchestration in the device-edge-cloud continuum, and focus on AI for edge, that is, the AI methods used in resource orchestration. ...
ACKNOWLEDGMENT The authors would like to thank the researchers and alumni at the Center for Ubiquitous Computing (UBICOMP) at the University of Oulu, and Distributed Systems Group (DSG) at TU Wien, for ...
arXiv:2205.01423v2
fatcat:phywaj2btrhpnobpeo5z4d3jv4
Advanced Applications of Neural Networks and Artificial Intelligence: A Review
2012
International Journal of Information Technology and Computer Science
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Artificial Neural Network is a branch of Artificial intelligence and has been accepted as a new computing technology in computer science fields. ...
Artificial Neural Networks have abundant features including high processing speeds and the ability to learn the solution to a problem from a set of examples. ...
The model is learnt in an unsupervised manner by tracking objects over long image sequences, and is based on a combination of a neural network implementing Vector Quantization and a type of neuron with ...
doi:10.5815/ijitcs.2012.06.08
fatcat:pjdstjcnuzhwlg4rex2uwfrexe
ToyArchitecture: Unsupervised Learning of Interpretable Models of the World
[article]
2019
arXiv
pre-print
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In this paper, we present a novel, purposely simple, and interpretable hierarchical architecture which combines multiple different mechanisms into one system: unsupervised learning of a model of the world ...
In contrast with purely theoretical approaches, the resulting architecture should be usable in realistic settings, but also form the core of a framework containing all the basic mechanisms, into which ...
This paper is structured as follows: first, we state the basic premises for a situated intelligent agent and review the important areas in which current Deep Learning (DL) methods do not perform well ( ...
arXiv:1903.08772v2
fatcat:wnknrw73pfhnpi6zy35pecriom
Resource-aware event triggered distributed estimation over adaptive networks
2017
Digital signal processing (Print)
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We propose a novel algorithm for distributed processing applications constrained by the available communication resources using diffusion strategies that achieves up to a 10 3 fold reduction in the communication ...
parameter, resulting in a greatly reduced communication requirement. ...
Acknowledgments This work is in part supported by the TUBITAK project Contract No.: 115E917. ...
doi:10.1016/j.dsp.2017.05.011
fatcat:uq4jletclzdhdicaovzfxv6swe
Roadmap for Edge AI: A Dagstuhl Perspective
[article]
2021
arXiv
pre-print
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In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation, optimization, and deployment of distributed AI/ML pipelines ...
Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. ...
ACKNOWLEDGMENTS The discussions leading to this editorial were initiated in Dagstuhl Seminar 21342 on Identifying Key Enablers in Edge Intelligence, and we thank all participants for their contributions ...
arXiv:2112.00616v1
fatcat:o7dnuchbq5gf7hmfos2kberevm
6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications
[article]
2021
arXiv
pre-print
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The goal of this paper is to promote the idea that including semantic and goal-oriented aspects in future 6G networks can produce a significant leap forward in terms of system effectiveness and sustainability ...
Combining knowledge representation and reasoning tools with machine learning algorithms paves the way to build semantic learning strategies enabling current machine learning algorithms to achieve better ...
The proposed new three-levels architecture for 6G is represented schematically in Fig. 3 . Fig. 3 has five columns. ...
arXiv:2011.14844v3
fatcat:7pz7tlylh5gu3gy7wry2ankmw4
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