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Leading Experts from Around the World

Uwe Gäbler

Uwe Gäbler

Infineon Technologies
    James L. McClelland

    James L. McClelland

    Stanford University / DeepMind
      Louise Dennis

      Louise Dennis

      University of Manchester
        Mihaela Rosca

        Mihaela Rosca

        UC London / DeepMind
          Pradeep Banerjee

          Pradeep Banerjee

          Max Planck Institute for Mathematics in the Sciences
            Manuel Cebrian

            Manuel Cebrian

            Max Planck Institute for   Human Development
              Katja Seeliger

              Katja Seeliger

              Max Planck Institute for Human Cognitive and Brain Sciences
                Martin Suda

                Martin Suda

                Czech Technical University in Prague
                  Simon Dobson

                  Simon Dobson

                  University of St Andrews
                    Marcos Cramer

                    Marcos Cramer

                    TU Dresden
                      Nigel Davies

                      Nigel Davies

                      Lancaster University
                        Louise Dennis

                        Louise Dennis

                        University of Manchester

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                          Event Speakers

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                          SCHEDULE DETAILS

                          Event Schedules

                          • 09:30 - 12:00 CEST
                          • Thomas Schmid & Sven Linker
                          • Lecture Hall 1

                          Workshop "Hybrid Artificial Intelligence"

                          In this face-to-face workshop, on-campus participants will together explore the growing, diverse, and exciting field of hybrid AI concepts. In particular, we will discuss not only classical hybrid approaches likes neural-symbolic integration, but also more general combinations of different strategies from different fields of artificial intelligence. The overall goal of this interactive workshop is to find the connecting dots between diverse research topics of the participants.

                            Lunch Break

                            Opening "Welcome to LEISYS 2022"
                            • 13:45 - 14:00 CEST
                            • Nigel Davies
                            • Lecture Hall 1 (+ONLINE)

                            Opening "Welcome to LEISYS 2022"

                            Nigel Davies, Head of the School of Computing and Communications at Lancaster University and Co-Director of the Data Science Institute at Lancaster University, will introduce the audience to LEISYS and the campus of Lancaster University in Leipzig.

                            Keynote "Embedding the Future: Opportunities and Prospects of Edge AI from the Perspective of a Chipmaker"
                            • 14:00 - 15:00 CEST
                            • Uwe Gäbler
                            • Lecture Hall 1 (+ONLINE)

                            Keynote "Embedding the Future: Opportunities and Prospects of Edge AI from the Perspective of a Chipmaker"

                            AI has the potential to sustainably change social, economic and ecological processes. The semiconductor industry plays an important role in this development, because AI applications are usually based on semiconductor solutions. Infineon is already a user and provider of AI solutions today. Infineon connects the real world with the digital world through system expertise in hardware, software as well as algorithms and hence helps to make AI applications more robust, energy-efficient and secure. In this context, Edge or Embedded AI applications are becoming increasingly important. Within Edge AI solutions, data is processed close to sensors making applications energy-efficient, fast and secure. Therefore, Embedded AI creates special opportunities for European semiconductor manufacturers such as Infineon and the Silicon Saxony industry cluster.

                            Research Talk "Convolutional Neural Networks and Human Visual Information Processing"
                            • 15:00 - 15:30 CEST
                            • Katja Seeliger
                            • Lecture Hall 1 (+ONLINE)

                            Research Talk "Convolutional Neural Networks and Human Visual Information Processing"

                            In parallel to the successes of neural networks for computer vision during the past decade, computational visual neuroscience has experienced a paradigm shift: The type of neural network behind their resurgence in computer vision happened to be the convolutional neural network, which originally had been proposed – in the form of the neocognitron – as the mechanism of object recognition behind the experimental findings in the early visual system by Hubel & Wiesel in the 1960s. In a rare succession of mutual scientific inspiration, convolutional neural networks trained on object recognition tasks turned out to be the best model for information processing in the human visual cortex as well. Recent approaches are using this processing similarity as a given, and train the networks directly (end-to-end) on newly recorded large-scale human neuroimaging data sets. This leads to biological properties learned implicitly, and to data-driven visual cortex models that allow wide-scaled in-silico exploration what higher brain areas are responding to.

                            Research Talk "Defining Rational Argumentation Semantics through the Principal-based Approach"
                            • 15:30 - 16:00 CEST
                            • Marcos Cramer
                            • Lecture Hall 1 (+ONLINE)

                            Research Talk "Defining Rational Argumentation Semantics through the Principal-based Approach"

                            In the field of abstract argumentation one studies the possibility to make decisions about the acceptability of arguments based on the structure of the attack relation between arguments. For this purpose multiple argumentation semantics have been proposed. But which of these argumentation semantics give outcomes in line with what humans judge to be rational? This question can be tackled in different ways. In this talk, we focus on the principle-based approach to abstract argumentation, in which one defines certain principles that argumentation semantics should satisfy. One then studies which semantics proposed in the literature satisfy which principles, and if some desired combination of principles is not satisfied by any existing semantics, one studies the possibility of defining a new semantics that does satisfy this combination of principles. In this talk we pay special attention to the principle of Irrelevance of Necessarily Rejected Arguments and two argumentation semantics that were developed in order to satisfy this principle as well as certain other principles: SCF and choice-preferred semantics.

                            Research Talk "Information Stability, Stochastic Complexity and Generalization"
                            • 16:00 - 16.30 CEST
                            • Pradeep Banerjee
                            • Lecture Hall 1 (+ONLINE)

                            Research Talk "Information Stability, Stochastic Complexity and Generalization"

                            The generalization capability of a learning algorithm is intrinsically related to the information that the output hypothesis reveals about the input training dataset: The lesser the information revealed, the better the generalization. This argument has been formalized in recent years by appealing to different notions of information stability. In this talk, I will present a unifying picture of information stability-based upper bounds on the generalization error of randomized learning algorithms under different assumptions on the loss function. Optimizing these bounds naturally gives rise to a method called Stochastic Complexity Minimization for which we discuss two practical examples for learning with neural networks, namely Entropy- and PAC-Bayes- SGD.

                              Coffee Break

                              • 17:00 - 18:00 CEST
                              • Thomas Schmid & Sven Linker
                              • Lecture Hall 1

                              Award & Panel Discussion "What is an Intelligent System?"

                              In this face-to-face closing session, we will bring together on-campus participants in order to review day 1 of LEISYS 2022, discuss lessons learned and identify future directions of research.

                              Research Talk "Verifying Machine Ethics"
                              • 14:00 - 14:30 CEST
                              • Louise Dennis
                              • ONLINE

                              Research Talk "Verifying Machine Ethics"

                              Machine ethics is concerned with the challenge of constructing ethical and ethically behaving artificial agents and systems.  One important theme within machine ethics concerns explicitly ethical agents – those which are not ethical simply because they are constrained by their programming or deployment to be so but which use a concept of ethics in some way as part of their operation.  Normally this requires the provision of rules, utilities or priorities by a programmer, knowledge engineer or user.  In this talk I will address the question of how such explicitly ethical programs can be verified.  What kind of properties can we consider and what kind of errors might we find?

                              Research Talk "Networked and Crowdsourced Response to Time-critical Threats"
                              • 14:30 - 15:00 CEST
                              • Manuel Cebrian
                              • ONLINE

                              Research Talk "Networked and Crowdsourced Response to Time-critical Threats"

                              This talk explores the physical, behavioral, and computational limits of crowd-assembly for time-critical problem-solving. I follow several real-world experiments where we utilized social media to mobilize the masses in tasks of unprecedented complexity. From finding red weather balloons, to locating thieves in distant cities, to reconstructing shredded classified documents, the potential of crowdsourcing is real, but so are exploitation, sabotage, and hidden biases that undermine the power of crowds.

                              Research Talk "Sensor Tensors"
                              • 15:00 - 15:30 CEST
                              • Simon Dobson
                              • ONLINE

                              Research Talk "Sensor Tensors"

                              Sensor networks are becoming increasingly common, but the torrent of data they provide is not without its problems. It's intuitively clear that issues such as the placement of the sensors, their accuracy, the degradations caused by physical wear and tear, and deliberate attacks will all affect the confidence we should place in the conclusions we draw from the data collected, but we have only a limited understanding of how these issues affect what we observe. This talk describes work in progress that represents a sensor system as a tensor -- a three-dimensional generalisation of a matrix -- that can be used to perform data interpolation. It might also help us understand the effects of errors and develop additional algorithms for in-network data analytics.

                                Coffee Break

                                Research Talk "Boosting Automated Reasoning using Machine Learning"
                                • 16:00 - 16:30 CEST
                                • Martin Suda
                                • ONLINE

                                Research Talk "Boosting Automated Reasoning using Machine Learning"

                                Clause selection is one of the key decision points within the saturation-style architecture of automated theorem provers (ATPs) for first-order logic. I will describe how machine learning (ML) can be used to greatly improve the clause selection heuristics and thus the prover performance. I will then try to put this prototypical example of the ATP+ML synergy into a broader context.

                                Research Talk "The Importance of Discretisation Drift in Deep Learning"
                                • 16:30 - 17:00 CEST
                                • Mihaela Rosca
                                • ONLINE

                                Research Talk "The Importance of Discretisation Drift in Deep Learning"

                                Gradient descent is an ubiquitous tool in the area of deep learning. Still, much of the underpinnings of gradient descent in the deep learning context have yet to be understood. In this talk we will explore a line of work which uses backward error analysis to quantify the discretisation drift induced by gradient descent and shed light on its effects in supervised learning and two-player games. We will uncover the implicit regularisation effect that gradient descent has in supervised learning, and see how it can aid generalisation. In two-player games however, we will find a more complicated picture which shows that for adversarial games such as GANs discretisation drift can have a harmful effect, and that by cancelling parts of the drift using explicit regularisation we can improve performance and stability. 

                                AI- & Human-Inspired Attentional Learners as Models of Human Thinking & Planning
                                • 17:00 - 17:30 CEST
                                • James L. McClelland
                                • ONLINE

                                AI- & Human-Inspired Attentional Learners as Models of Human Thinking & Planning

                                James will consider the Transformer Architecture that has become a standard tool for AI and Natural Language Processing (NLP) and examine its relevance for understanding human thinking and planning. He will suggest that human-like instantiations of this architecture might allow us to build models that capture many aspects of human performance using a general-purpose architecture that has its roots in models of human memory that originated almost 50 years ago, and he will discuss some of the open questions that James and his group is grappling with as they seek to implement such systems to model human goal-directed problem solving and planning.

                                REGISTRATION

                                Join us on campus or online - for free!

                                DAY 1 On-Campus

                                FREE

                                17/30

                                • Leipzig (Germany)

                                DAY 1 Online

                                FREE

                                51/150

                                • Via Microsoft Teams

                                DAY 2 Online

                                FREE

                                51/150

                                • Via Microsoft Teams

                                INFO UPDATE

                                Latest News


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                                • March 30, 2022

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                                One of The Highest Mountains!
                                • August 21, 2020

                                One of The Highest Mountains!

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                                • August 21, 2020

                                Cheerful Loving Couple Bakers Drinking Coffee

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                                WHO HELPS US

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                                • Location

                                  Lancaster University Leipzig
                                  Strohsack-Passage
                                  7th Floor
                                  Nikolaistraße 10
                                  D-04109 Leipzig
                                  GERMANY

                                • Line Phone

                                  +49 341 33975808

                                • Website

                                  www.lancasterleipzig.de

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