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1st International Conference on Modeling, Machine Learning and Astronomy (MMLA)

Optimizing deep neural networks is largely thought to be an empirical process, requiring manual tuning of several parameters.The conference aims to focus on gaining theoretical insights in the computation and setting of these parameters. The organizers solicit papers that focus on exploring alternatives to gradient descent/ascent types methods. 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. 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 inter-disciplinary study of astroinformatics brings together machine learning theorists, astronomers, mathematicians and computer scientists underpinning the importance of machine learning algorithms and data analytic techniques. 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. The meeting is sponsored by: Department of Computer Science and Engineering & Center for AstroInformatics, Modeling and Simulation, PES University; IEEE Computer Society Bangalore Chapter; and International Astrostatistics Association.
When 22 November 2019 05:25 PM to
23 November 2019 05:25 PM
Where Bengaluru IN
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Keynote Speech:

1. Ashish Mahabal, Caltech and Jet Propulsion Labs

2. Oleg Malkov, Russian Academy of Sciences

Invited Talk: Tarun Deep Saini, IISc

Kartik GuruMurthy, Amazon

Ajith Parmeswaran, ICTS

Anand Sengupta (IIT Gandhinagar)

Suresh Sundaram, IISc

Sriparna Saha, IIT Patna

Najam Hasan, Moulana Azad National Urdu University

Pre-conference Tutorial on Deep Neural Nets:

Archana Mathur, Indian Statistical Institute, Bangalore

Sheelu Abraham, IUCAA (tentative)

Kaustav Vaghmare, IUCAA


Topics of interest include, but are not limited to:

Exoplanets (discovery, machine classification etc.)

Unsupervised, semi-supervised, and supervised representation learning

Representation learning for reinforcement learning

Metric learning and kernel learning

Deep learning in astronomy

MCMC on big data

Statistical Machine Learning

Bayesian Methods in Astronomy

Meta-heuristic and Evolutionary Clustering methods and applications in Astronomy

Optimization methods

Swarm intelligence

Multi-objective optimization

Dynamical Systems and Complexity

Information-Theoretic Methods in Life-like Systems

Predictive Methods for Complex Adaptive Systems and Life-like Systems, Evolutionary Games

More information about this event…