Department of Computer Science and Engineering, Center for AstroInformatics, Modeling and Simulation, PES University and International Astrostatistics Association are proud to present a unique conference on Modeling, Machine Learning and Astronomy.<br>Theory of machine learning, deep learning in particular has been witnessing an implosion lately in deciphering the “black–box approaches”. Optimizing deep neural networks is largely thought to be an empirical process, requiring manual tuning of several parameters. Drawing insights in to these parameters gained much attention lately. The conference aims to focus on gaining theoretical insights in the computation and setting of these parameters and solicits original work reflecting the influence of such theoretical framework on experimental results on standard datasets and architectures. The conference aims to garner valuable talking points from optimization studies, another aspect of deep learning architectures and experiments. It is in this spirit, the organizers wish to bridge metaheuristic optimization methods with deep neural networks and solicit papers that focus on exploring alternatives to gradient descent/ascent types methods. Papers with theoretical insights and proofs are particularly sought after, with or without limited experimental validation. We would welcome cutting–edge research on aspects of deep learning theory used in the fields of artificial intelligence, statistics and data science, theoretical and numerical optimization.<br>Habitability outside the solar system is an intriguing topic and center of focused research for at least a decade now. Coupled with this, there are advances in the fields of Artificial Life and Complex Adaptive Systems aiming to understand and synthesize life–like systems. The conference brings together material understanding design diversity, complexity, and adaptability of life and their rapid influence in areas of engineering and the Sciences. We wish to solicit ideas from nature and their generalizations from life and their translations into engineering and science.<br>Data is at the heart of this. Astronomy is a fascinating case study as it had embraced big data embellished by many sky–surveys. The variety and complexity of the data sets at different wavelengths, cadences etc. imply that modeling, computational intelligence methods and machine learning need to be exploited to understand astronomy. The importance of data driven discovery in Astronomy has given birth to an exciting new field known as astroinformatics. The inter–disciplinary study brings together machine learning theorists, astronomers, mathematicians and computer scientists underpinning the importance of machine learning algorithms and data analytic techniques.<br>The Conference aims to set a unique ground as an amalgamation of the diverse ideas and techniques while staying true to the baseline. We expect to discuss new developments in modeling, machine learning, design of complex computer experiments and data analytic techniques which can be used in areas beyond astronomical data analysis. Given the horizontal nature of MMLA, we hope to disseminate methods that are area–agnostic but currently of interest to the broad community of science and engineering.<br>Topics of interest include, but are not limited to:<br>• Exoplanets (discovery, machine classification etc.)<br>• Unsupervised, semi–supervised, and supervised representation learning<br>• Representation learning for reinforcement learning<br>• Metric learning and kernel learning<br>• Deep learning in astronomy<br>• MCMC on big data<br>• Statistical Machine Learning<br>• Bayesian Methods in Astronomy<br>• Meta–heuristic and Evolutionary Clustering methods and applications in<br>Astronomy<br>• Optimization methods<br>• Swarm intelligence<br>• Multi–objective optimization<br>• Dynamical Systems and Complexity<br>• Information–Theoretic Methods in Life–like Systems<br>• Predictive Methods for Complex Adaptive Systems and Life–like Systems<br>Evolutionary Games<br>
Abbrevation
MMLA
City
Banashankari, Bangalore
Country
India
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