DL is a symposium in the IEEE Symposium Series on Computational Intelligence, December 4-7, 2021 Orlando, Florida, USA. Description Deep Learning (DL) is growing in popularity because it solves complex problems in machine learning by exploiting multi scale, multi-layer architectures making better use of the data patterns. Multi-scale machine perception tasks such as object and speech recognitions using DL have recently outperformed systems that have been under development for many years. The principles of DL, and its ability to capture multi scale representations, are very general and the technology can be applied to many other problem domains, which makes it quite attractive. Many open problems and challenges still exists, e.g. interpretability, computational and time costs, repeatability of the results, convergence, ability to learn from a very small amount of data, to evolve dynamically/continue to learn, etc. The Symposium will provide a forum for discussing new DL advances, challenges, brainstorming new solutions and directions between top scientists, researchers, professionals, practitioners and students with an interest in DL and related areas including applications to autonomous transportation, communications, medical, financial services, etc. Topics Topics of IEEE DL’21 include but are not limited to: Unsupervised, semi-, and supervised learning Deep reinforcement learning (deep value function estimation, policy learning and stochastic control) Memory Networks and differentiable programming Implementation issues (software and hardware) Dimensionality expansion and sparse modeling Learning representations from large-scale data Multi-task learning Learning from multiple modalities Weakly supervised learning Metric learning and kernel learning Hierarchical models Interpretable DL Fuzzy rule-based DL Non-Iterative DL Recursive DL Repeatability of results in DL Convergence in DL Incremental DL Evolving DL Fast DL Applications in: Image/video Audio/speech Natural language processing Robotics, navigation, control Games Cognitive architectures AI

Organized by Luca Oneto (University of Genoa, Italy), Nicolò Navarin (University of Padua, Italy), Battista Biggio (University of Cagliari, Italy), Federico Errica (Università di Pisa, Italy), Alessio Micheli (Università di Pisa, Italy), Franco Scarselli (SAILAB - University of Siena, Italy), Monica Bianchini (SAILAB - University of Siena, Italy), Alessandro Sperduti (University of Padua, Italy) Machine Learning (ML) achievements enabled automatic extraction of actionable information from data in a wide range of decision-making scenarios (e.g. health care, cybersecurity, and education). ML models are nowadays ubiquitous pushing even further the process of digitalization and datafication of the real and digital world producing more and more complex and interrelated data. This demands for improving both ML technical aspects (e.g. design and automation) and human-related metrics (e.g. fairness, robustness, privacy, and explainability), with performance guarantees at both levels. The aforementioned scenario posed three main challenges: (i) Learning from Complex Data (i.e. sequence, tree and graph data), (ii) Learning Trustworthily, and (iii) Learning Automatically with Guarantees. The scope of this special session is then to address one or more of these challenges with the final goal of Learning Trustworthily, Automatically, and with Guarantees from Complex Data. Examples of methods and problems in these challenges are: efficient and effective models capable of directly learning from data natively structured or collected from interrelated heterogeneous sources (e.g. social and relational data, knowledge graphs), characterized by entities, attributes, and relationships, without relying on human skills to encode this complexity into a rich and expressive (vectorial) representation; design ML models from a human-centered perspective, making ML trustworthy by design, by removing human biases from the data (e.g. gender discrimination), increasing robustness (e.g. to adversarial data perturbation), preserving individuals’ privacy (e.g. protecting ML models from differential attacks), and increasing transparency (e.g. via ML models and output explanation); automatizing the ML design and deployment parts which are currently handcrafted by highly skilled and trained specialists. For this reason, ML is required to be empowered with self-tuning properties (e.g. architecture and hyperparameter automatic selection), understanding and guaranteeing the final performance (e.g. with worst case and statistical bounds) with respect to both technical and human relevant metrics. The focus of this special session is to attract both solid contributions or preliminary results which show the potentiality and the limitations of new ideas, refinements, or contaminations between the different fields of machine learning and other fields of research in solving real world problems. Both theoretical and practical results are welcome to our special session.

DL is a symposium in the IEEE Symposium Series on Computational Intelligence, December 1-4, 2020 Canberra, Australia. Description Deep Learning (DL) is growing in popularity because it solves complex problems in machine learning by exploiting multi scale, multi-layer architectures making better use of the data patterns. Multi-scale machine perception tasks such as object and speech recognitions using DL have recently outperformed systems that have been under development for many years. The principles of DL, and its ability to capture multi scale representations, are very general and the technology can be applied to many other problem domains, which makes it quite attractive. Many open problems and challenges still exists, e.g. interpretability, computational and time costs, repeatability of the results, convergence, ability to learn from a very small amount of data, to evolve dynamically/continue to learn, etc. The Symposium will provide a forum for discussing new DL advances, challenges, brainstorming new solutions and directions between top scientists, researchers, professionals, practitioners and students with an interest in DL and related areas including applications to autonomous transportation, communications, medical, financial services, etc. Topics Topics of IEEE DL’19 include but are not limited to: Unsupervised, semi-, and supervised learning Deep reinforcement learning (deep value function estimation, policy learning and stochastic control) Memory Networks and differentiable programming Implementation issues (software and hardware) Dimensionality expansion and sparse modeling Learning representations from large-scale data Multi-task learning Learning from multiple modalities Weakly supervised learning Metric learning and kernel learning Hierarchical models Interpretable DL Fuzzy rule-based DL Non-Iterative DL Recursive DL Repeatability of results in DL Convergence in DL Incremental DL Evolving DL Fast DL Applications in: Image/video Audio/speech Natural language processing Robotics, navigation, control Games Cognitive architectures AI