62,957 Hits in 7.4 sec

Inferring genetic networks: An information theoretic approach [article]

L. Diambra
2009 arXiv   pre-print
This information gain can be used to chose genes to be perturbed in subsequent experiments in order to refine the knowledge about the architecture of an underlying gene regulatory network.  ...  This approach also allows the introduction of prior knowledge and the quantification of the information gain from experimental data used in the inference procedure.  ...  This IT approach enables the effective use of all the available information, in which each experiment is used as an individual constraint.  ... 
arXiv:0908.0146v1 fatcat:kkihxgybvbghfeogcj7l2fz6cy

Ensemble creation and reconfiguration for activity recognition: An information theoretic approach

Ricardo Chavarriaga, Hesam Sagha, Jose del R. Millan
2011 2011 IEEE International Conference on Systems, Man, and Cybernetics  
Recent works propose measures of accuracy and diversity based on an information theoretical approach.  ...  These sensors can be integrated into an ensemble that fuse their information to obtain the final decision.  ...  In the case of an architecture relying on nodes self-descriptions, as proposed by Kurz et al.  ... 
doi:10.1109/icsmc.2011.6084090 dblp:conf/smc/ChavarriagaSM11 fatcat:oljmroswfzdq3al2w2gtb2sfdy

Optimal Control of Probabilistic Boolean Networks: An Information-Theoretic Approach

K. Sonam, S. Sutavani, S. R. Wagh, N. M. Singh
2021 IEEE Access  
The optimal control of PBCNs in the Markovian framework is developed in this paper employing an information-theoretic approach which includes Kullback-Leibler (KL) divergence.  ...  Owing to the exponential growth in network size with the increase in the number of genes, we need an optimal control approach that scales to large systems without imposing any limitations on network dynamics  ...  of the inherent stochastic behavior of PBCNs, an information-theoretic formulation utilizing the augmented state space is proposed for optimal control of PBCNs.2) To obtain the solution to information-theoretic  ... 
doi:10.1109/access.2021.3130118 fatcat:7qgiyf74ebcyzknrrlwpaum36a

An Iterative Information-Theoretic Approach to the Detection of Structures in Complex Systems

Marco Villani, Laura Sani, Riccardo Pecori, Michele Amoretti, Andrea Roli, Monica Mordonini, Roberto Serra, Stefano Cagnoni
2018 Complexity  
This approach is able to highlight the organization of a complex system into sets of variables, which interact with one another at different hierarchical levels, detected, in turn, in the different iterations  ...  Systems that exhibit complex behaviours often contain inherent dynamical structures which evolve over time in a coordinated way.  ...  The proposed approach, based on information-theoretic measures, has proven to be able to extract hidden information about the organization of the three complex systems we have analysed.  ... 
doi:10.1155/2018/3687839 fatcat:mxbyw6drbvetpd3ykurobeuc6q

A comparative approach for the investigation of biological information processing: An examination of the structure and function of computer hard drives and DNA

David J D'Onofrio, Gary An
2010 Theoretical Biology and Medical Modelling  
The robust storage, updating and utilization of information are necessary for the maintenance and perpetuation of dynamic systems.  ...  Biological systems do not have an external source for a map of their stored information or for an operational instruction set; rather, they must contain an organizational template conserved within their  ...  D'Onofrio and An Theoretical Biology and Medical Modelling 2010, 7:3 D'Onofrio and An Theoretical Biology and Medical Modelling 2010, 7:3  ... 
doi:10.1186/1742-4682-7-3 pmid:20092652 pmcid:PMC2829000 fatcat:pun6ql3ftbfihln6et37k7i66i

Semantic enablers for dynamic digital–physical object associations in a federated node architecture for the Internet of Things

Suparna De, Benoit Christophe, Klaus Moessner
2014 Ad hoc networks  
for an IoT 62 architecture.  ...  and consuming information -requires adopting a different 51 approach to avoid scalability issues.  ... 
doi:10.1016/j.adhoc.2013.02.003 fatcat:c7lwbr5xefbn3ibpgvytuoskge

Interpreting Deep Learning: The Machine Learning Rorschach Test? [article]

Adam S. Charles
2018 arXiv   pre-print
Theoretical understanding of deep learning is one of the most important tasks facing the statistics and machine learning communities.  ...  Unfortunately, DNN adoption powered by recent successes combined with the open-source nature of the machine learning community, has outpaced our theoretical understanding.  ...  theory [85] • analysis of the role of layered representations on DNN properties via the learning dynamics [86, 87, 88, 89, 90] • speed and accuracy guarantees (or lack thereof) of learning methods  ... 
arXiv:1806.00148v1 fatcat:g2zyzixazfht3b33buxokfp524

Learning with hidden variables [article]

Yasser Roudi, Graham Taylor
2015 arXiv   pre-print
Here we review recent advancements in this area emphasizing, amongst other things, the processing of dynamical inputs by networks with hidden nodes and the role of single neuron models.  ...  These points and the questions they arise can provide conceptual advancements in understanding of learning in the cortex and the relationship between machine learning approaches to learning with hidden  ...  Given the dynamic nature of inputs to the brain, deep dynamic architectures should offer another interesting area of research for understanding learning in the cortical circuits.  ... 
arXiv:1506.00354v2 fatcat:h6lg63puqvghrmpsusik75vrra

Meta-Learning - Concepts and Techniques [chapter]

Ricardo Vilalta, Christophe Giraud-Carrier, Pavel Brazdil
2009 Data Mining and Knowledge Discovery Handbook  
We begin by describing an idealized meta-learning architecture comprising a variety of relevant component techniques.  ...  In addition we show how meta-learning has already been identified as an important component in real-world applications.  ...  can be performed under a variety of statistical, information-theoretic, and model-based approaches (Section 3.1).  ... 
doi:10.1007/978-0-387-09823-4_36 fatcat:obz2mb7xwfdgfpzv7vtfh7ouwm

Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments [article]

Andri Ashfahani, Mahardhika Pratama
2019 arXiv   pre-print
The maximum information compression index (MICI) method plays an important role as a complexity reduction module eliminating redundant layers.  ...  The feasibility of deep neural networks (DNNs) to address data stream problems still requires intensive study because of the static and offline nature of conventional deep learning approaches.  ...  Maximum information compression index (MICI) method plays an important role as a complexity reduction module eliminating redundant layers.  ... 
arXiv:1810.07348v2 fatcat:5eavov7icnagdmbgzhaf7k3x2a

Genetics-Based Machine Learning [chapter]

Tim Kovacs
2012 Handbook of Natural Computing  
Simple method: fix architecture How to make nodes specialise?  ...  Introducing a Genetic Generalization Pressure to the Anticipatory Classifier System -Part 1: Theoretical Approach. In Whitley et al. [307], pages 34-41.  ... 
doi:10.1007/978-3-540-92910-9_30 fatcat:rm5bx5lwdvfalolrky6lpyt67a

Network representation learning: models, methods and applications

Anuraj Mohan, K. V. Pramod
2019 SN Applied Sciences  
Generating an efficient network representation is one important challenge in applying machine learning to network data.  ...  In this survey, we focus on the recent methods for node embedding which are inspired by the recent advancements in representation learning.  ...  ANE [24] proposes a different approach which uses adversarial learning as a regularizer to learn more robust network representations.  ... 
doi:10.1007/s42452-019-1044-9 fatcat:zvlbj4qozzfw3dxoyevb6wgska

Deep Learning for Learning Graph Representations [chapter]

Wenwu Zhu, Xin Wang, Peng Cui
2019 Studies in Computational Intelligence  
The investigation on efficient representation of a graph has profound theoretical significance and important realistic meaning, we therefore introduce some basic ideas in graph representation/network embedding  ...  learning architectures [24] .  ...  DVNE focuses on the uncertainties in graph representations and DepthLGP aims to learn accurate embeddings for new nodes in dynamic networks.  ... 
doi:10.1007/978-3-030-31756-0_6 fatcat:vp7wcpdz4rfndlskanplakd34e

Learning by Doing

Paromita Pain, Gina Masullo Chen, Christopher P. Campbell
2016 Journalism and Mass Communication Educator  
This paper puts forward an idea that 3D virtual learning environment (3DVLE ) is an effective way to improve the feasibility of implementing constructivist learning theory in distance education.  ...  Then we provide a learning and designing scheme on Web-based system. Finally, we briefly describe our 3DVLE system, present system architecture and some implementation considerations.  ...  VRML provides an LOD node, which can be explicitly change different level detail version of model. The value of level specifies a sub-node list in a group.  ... 
doi:10.1177/1077695815613711 fatcat:i5a47hxwozhq3k6i7uhioz7n4u

Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks

Javier Villalba-Díez, Martin Molina, Joaquín Ordieres-Meré, Shengjing Sun, Daniel Schmidt, Wanja Wellbrock
2020 Sensors  
continuous improvement of lean management systems in this context is determined by their ability to recognize behavioral patterns in these big data structured within non-Euclidean domains, such as these dynamic  ...  This is why this work focuses on proposing geometric deep lean learning, a mathematical methodology that describes deep-lean-learning operations such as convolution and pooling on cyber–physical Industry  ...  be interpreted by classical approaches, or to generalize the concept of deep learning to dynamic networks.  ... 
doi:10.3390/s20030763 pmid:32019148 pmcid:PMC7038400 fatcat:2pqzvrzvmfaj7mmpqtz2h6mwse
« Previous Showing results 1 — 15 out of 62,957 results