The two years line is equivalent to journal impact factor в„ў (Thomson Reuters) metric.Д®volution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years. The chart shows the evolution of the average number of times documents published in a journal in the past two, three and four years have been cited in the current year.

This indicator counts the number of citations received by documents from a journal and divides them by the total number of documents published in that journal. Q1 (green) comprises the quarter of the journals with the highest values, Q2 (yellow) the second highest values, Q3 (orange) the third highest values and Q4 (red) the lowest values. The set of journals have been ranked according to their SJR and divided into four equal groups, four quartiles. Machine Learning and Systems Theory: artificial intelligence, neural networks, cybernetics, robotics, man-machine interfacesД®conomics: thermoeconomics, info-metrics, maximum entropy methods, game theory. Inquiry: experimental design, database querying, sensor placement, intelligent samplingД¬omputing: big data, database design and querying, networks, cloud computing, pattern recognition, coding and compression, information transfer, reversibility, information loss, social networkingД¬hemistry and Biology: chemical networks, energy, enthalpy, maximum entropy methods, biological networks, evolution, DNA and RNA, diversity Information Theory: Shannon entropy, Kullback-Leibler divergence, channel capacity, Renyi and other entropies, and applicationsД¬omplex Systems: self-organization, chaos and nonlinear dynamics, simplicity and complexity, networks, symmetry breaking, similarity Physics and Engineering: thermodynamics, statistical mechanics, the second law of thermodynamics, reversibility, quantum mechanics, black hole physics, maximum entropy methods, maximum entropy production, evolution of the universe Some Common Subject or Application Areas include: Obtain new results using concepts of entropy or information theory Provide new insights into entropy or information-theoretic conceptsДemonstrate a novel use of entropy or information-theoretic concepts in an application Relevant submissions ought to focus on one of the following:Дevelop the theory behind entropy or information theory Lizhong Zheng and Dr.Entropy deals with the development and/or application of entropy or information-theoretic concepts in a wide variety of applications. Method and theory for model compression.Дeadline for manuscript submissions: 31 December 2021.Representation learning and its information theoretic interpretation.Information theoretic approaches in active and reinforcement learning.Multimodal learning and information fusion.Distributed learning, communication-aware learning algorithms.Utilizing knowledge of data structure in learning.Sample complexity and generalization errors.

Understanding gradient descent and general iterative algorithms.

Some of the topics of interest are listed below: We are also interested in papers presenting new methods and new concepts, even if some of these ideas might not have been fully developed, or might not have the most compelling set of supporting experimental results. The goal of this Special Issue is to collect new results in using information theoretic thinking to solve machine learning problems. Correspondingly, in theoretical studies, we shall need new formulations, new mathematical tools, new analysis techniques, and maybe even new metrics to evaluate the guidance and insights offered by theoretical studies. There are indeed a set of lofty goals, where we hope to have a holistic view of data processing, to work with high-dimensional data and inaccurate statistical models, to incorporate domain knowledge, to provide performance guarantees, robustness, security, and fairness, to reduce the use of computational resources, to generate reusable and interpretable learning results, etc. While we expect information theory to play an even more significant role in the next wave of growth in machine learning and artificial intelligence, we also recognize the new challenges in this task. There are a number of significant steps in the development of machine learning that benefit from information theoretic analysis, as well as the insights into information processing that it brings.

Entropy magazine