TPNC is a conference series intending to cover the wide spectrum of computational principles, models and techniques inspired by information processing in nature. TPNC 2017 will reserve significant room for young scholars at the beginning of their career and particular focus will be put on methodology. The conference aims at attracting contributions to nature-inspired models of computation, synthesizing nature by means of computation, nature-inspired materials, and information processing in nature.
SLSP is a yearly conference series aimed at promoting and displaying excellent research on the wide spectrum of statistical methods that are currently in use in computational language or speech processing. It aims at attracting contributions from both fields. Though there exist large, well-known conferences and workshops hosting contributions to any of these areas, SLSP is a more focused meeting where synergies between subdomains and people will hopefully happen. In SLSP 2018, significant room will be reserved to young scholars at the beginning of their career and particular focus will be put on methodology.
DeepLearn 2018 will be a research training event with a global scope aiming at updating participants about the most recent advances in the critical and fast developing area of deep learning. This is a branch of artificial intelligence covering a spectrum of current exciting machine learning research and industrial innovation that provides more efficient algorithms to deal with large-scale data in neurosciences, computer vision, speech recognition, language processing, human-computer interaction, drug discovery, biomedical informatics, healthcare, recommender systems, learning theory, robotics, games, etc. Renowned academics and industry pioneers will lecture and share their views with the audience.
AlCoB aims at promoting and displaying excellent research using string and graph algorithms and combinatorial optimization to deal with problems in biological sequence analysis, genome rearrangement, evolutionary trees, and structure prediction.