{"id":3181,"date":"2024-02-25T13:34:39","date_gmt":"2024-02-25T13:34:39","guid":{"rendered":"https:\/\/e-nns.org\/icann2024\/?page_id=3181"},"modified":"2024-08-22T10:18:15","modified_gmt":"2024-08-22T10:18:15","slug":"workshops-and-special-sessions","status":"publish","type":"page","link":"https:\/\/e-nns.org\/icann2024\/workshops-and-special-sessions\/","title":{"rendered":"Workshops and Special Sessions"},"content":{"rendered":"\n

Workshop: AI in Drug Discovery<\/strong><\/h3>\n\n\n\n

The Workshop on AI in Drug Discovery invites cutting-edge contributions in the rapidly evolving field of AI-driven drug discovery. We are seeking submissions encompassing various facets such as generative models, explainable AI, model distillation, uncertainty quantification, reaction informatics and synthetic route prediction, quantum machine learning
for reactivity, methodologies for mining very large compound data sets, federated learning, analysis of HTS data and identification of frequent hitters and other topics related to the use of ML in chemistry. <\/p>\n\n\n\n

ORGANIZERS<\/strong>: Igor Tetko (Institute of Structural Biology, Helmholtz Zentrum Munich Neuherberg, Germany), Djork-Arn\u00e9 Clevert (Pfizer, Berlin, Germany)<\/p>\n\n\n\n

TOPICS<\/strong>:
Big Data and advanced machine learning in chemistry
eXplainable AI (XAI) in chemistry
Chemoinformatics
Use of deep learning to predict molecular properties
Modeling and prediction of chemical reaction data
Generative models<\/p>\n\n\n\n

See details in AI Drug Discovery Call for Papers<\/a>.<\/strong><\/p>\n\n\n\n

The AI in Drug Discovery Workshop invites all conference participants to take part in the Tox24 Challenge<\/a>. The final model submission deadline is August 31st<\/strong>.<\/p>\n\n\n\n

We invite authors of all papers presented at the Workshop to submit their contributions to the AI in Drug Discovery<\/a> collection to be published by Journal of Cheminformatics. Any authors participating in the ICANN2024 conference will receive a 20% discount on the APC fee for their submission to this collection. Please, clearly state that you are eligible for such a discount when submitting your article. Submissions can be made anytime up until December 31st<\/strong>.<\/p>\n\n\n\n

Workshop: Explainable AI in Human-Robot Interaction<\/strong><\/h3>\n\n\n\n

The workshop will be held for the dissemination and discussion of results from
the MSCA EU-project TRAIL<\/strong><\/a> (Transparent, Interpretable Robots). The topic
of the project is Explainability and Transparency in artificial neural networks
(decision transparency) and in human-robot interactions (behavior
transparency). <\/p>\n\n\n\n

ORGANIZERS<\/strong>: Stefan Wermter (University of Hamburg, Germany), Angelo Cangelosi (University of Manchester, UK), Igor Farka\u0161 (Comenius University of Bratislava, Slovakia), Theresa Pekarek Rosin (University of Hamburg, Germany)<\/p>\n\n\n\n

See details in XAI in HRI Call for Papers<\/a>.<\/strong><\/p>\n\n\n\n

<\/p>\n\n\n\n

Workshop: <\/strong>Reservoir Computing<\/h3>\n\n\n\n

After the success of the first edition, the 2nd International Workshop on Reservoir Computing (RC 2024) intends to bring back together researchers to update the discussion on the state-of-the-art and the cutting-edge challenges in the field of RC, in all its declinations. These include, among the others, new models of Echo State Networks and Liquid State Machines, applications to problems of AI also in the human-centric perspective, emerging paradigms, RC for structured data, deep RC, hybrid RC\/fully trained RNN models, and many more.<\/p>\n\n\n\n

ORGANIZERS<\/strong>: Alessio Micheli (University of Pisa, Italy), Gouhei Tanaka (Nagoya Institute of Technology, Japan), Claudio Gallicchio (University of Pisa, Italy), Benjamin Paassen (University of Bielefeld, Germany), Domenico Tortorella (University of Pisa, Italy)<\/p>\n\n\n\n

See details in <\/strong>RC 2024 Call for Papers<\/strong><\/a>.<\/strong><\/p>\n\n\n\n

Special Session: Spiking Neural Networks and Neuromorphic Computing<\/strong><\/h3>\n\n\n\n

The special session invites contributions on recent advances in spiking neural networks. Spiking neural networks have gained substantial attention recently as a candidate for low latency and low power AI substrate, with implementations being explored in neuromorphic hardware. This special session aims to bring together practitioners interested in efficient learning algorithms, data representations, and applications.<\/p>\n\n\n\n

ORGANIZERS<\/strong>: Sander Boht\u00e9 (CWI Amsterdam, Netherlands), Sebastian Otte (University of L\u00fcbeck, Germany)<\/p>\n\n\n\n

TOPICS<\/strong>:
Spiking Neural Network Models
Spike Response Models
Temporal Codes
Learning Algorithms for SNNs
Neuromorphic Computing<\/p>\n\n\n\n

See details in SNNC Call for Papers<\/a>.<\/strong><\/p>\n\n\n\n

Special Session: Accuracy, Stability, and Robustness in Deep Neural Networks<\/strong><\/h3>\n\n\n\n

This special session will provide a forum for discussing simultaneous robustness to perturbations, accuracy on the test sets and generalisation, overfitting and learning from few examples in high-dimensional settings, various notions of data dimension and its benefits, and influence of the choice of network architectures (numbers of their layers and types of computational units) on accuracy and robustness of network performance. Other pressing challenges which we would like to discuss include the issue of errors, their identification and correction with provable performance guarantees, and the fundamental understanding of uncertainties and their formalisation and capture in modern learning algorithms.<\/p>\n\n\n\n

ORGANIZERS<\/strong>: Vera Kurkova (Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic), Ivan Tyukin (King\u2019s College, London, UK)<\/p>\n\n\n\n

TOPICS<\/strong>:
Robustness and reliability of deep networks
Accuracy on test sets and generalization
Influnce of network depth on its accuracy
Stability wrt small perturbations
Learning in high-dimensional settings
Errors of neural networks and methods to reliably address them<\/p>\n\n\n\n

See details in RSADNN Call for Papers<\/a>.<\/strong><\/p>\n\n\n\n

Special Session: Neurorobotics<\/strong><\/h3>\n\n\n\n

Neurorobotics brings together interdisciplinary research in machine learning, robotics and bio-inspired artificial intelligence, most prominently using artificial neural networks. After the successful Neurorobotics special session in ICANN 2023 we are announcing a second edition of this session organized under the Horizon Europe project TERAIS<\/strong><\/a>. We want to bring together researchers with diverse backgrounds and expertise to promote collaboration and knowledge sharing in the field of Neurorobotics and cognitive robotics for HRI.<\/p>\n\n\n\n

ORGANIZERS<\/strong>: Igor Farka\u0161 (Comenius University Bratislava UKBA, Slovakia), Krist\u00edna Malinovsk\u00e1 (UKBA, SK), Andrej L\u00fa\u010dny (UKBA, SK), Pavel Petrovi\u010d (UKBA, SK), Michal Vavre\u010dka (UKBA, SK), Matthias Kertzel (HiTeC, Hamburg, Germany), Hassan Ali (University of Hamburg, Germany), Carlo Mazzola (Italian Institute for Technology, Italy) <\/p>\n\n\n\n

TOPICS<\/strong>:
Neural control and learning in robotics
Cognitive architectures for robots
Bio-inspired and developmental robotics
Human-robot interaction and collaboration
Trustworthy, human-aware and explainable robots
Computational models for robotic applications<\/p>\n\n\n\n

See details in Neurorobotics Call for Papers<\/a>.<\/strong><\/p>\n\n\n\n

Tutorial: FEDn – A scalable federated machine learning framework for cross-device and cross-silo environments<\/strong><\/h3>\n\n\n\n

Federated machine learning has opened new avenues for privacy-preserving data analysis. Instead of pooling data in a central location, different data owners or IoT devices keep data local and training is decentralized where only
model parameters are exchanged. Despite much progress in the field, production-grade federated machine learning frameworks that deal with fundamental properties such as scalability, fault tolerance, security and performance in geographically distributed settings have not been available
to the ML-engineer. To fill this gap, Scaleout Systems and SciML at Uppsala University have designed and developed the FEDn framework. FEDn is an open-source framework dedicated to address federated machine learning challenges at scale. This tutorial aims to provide a knowledge sharing platform and highlight challenges and possible solutions related to federated machine learning. <\/p>\n\n\n\n

PRESENTERS<\/strong>: Salman Toor (Uppsala University, Sweden), Andreas Hellander (Uppsala University, Sweden)<\/p>\n\n\n\n

See details in <\/strong>FEDn Tutorial Announcement<\/strong><\/a>.<\/strong><\/p>\n\n\n\n

Tutorial: A Hands-on Introduction to Time Series Feature Extraction<\/strong><\/strong> with the TSFEL Library<\/h3>\n\n\n\n

\u2014 Are you extracting all the relevant information from your time series data?<\/em><\/p>\n\n\n\n

Time series are a fundamental data type for understanding the behavior of real-world systems across several domains in data science. This hands-on tutorial supported with code examples will provide an accessible overview of the recent research in time series classification, with a strong emphasis on the task of feature extraction. We will use the Time Series Feature Extraction Library (TSFEL) that computes over 65 different features across the statistical, temporal, spectral, and fractal domains. Alongside a brief theoretical introduction to the feature sets, we will cover important practical recommendations for their successful use with biosignal data.<\/p>\n\n\n\n

Session type and duration<\/strong><\/p>\n\n\n\n

A hands-on tutorial session of 2 hours including a short lecture-style introduction.<\/p>\n\n\n\n

Intended audience (introductory, intermediate, advanced)<\/strong><\/p>\n\n\n\n

Introductory and intermediate.<\/p>\n\n\n\n

Agenda<\/strong><\/p>\n\n\n\n

    \n
  1. A general overview of time series classification (20 min.)<\/li>\n\n\n\n
  2. Feature extraction in time series – introduction to statistical, temporal, spectral, and fractal feature sets (20 min.)<\/li>\n\n\n\n
  3. Hands-on introduction with the Time Series Feature Extraction Library (TSFEL) (60 min.)<\/li>\n\n\n\n
  4. Wrap-up and closing remarks (20 min.)<\/li>\n<\/ol>\n\n\n\n

    Requirements for the hands-on session<\/strong><\/p>\n\n\n\n

    A laptop with an internet connection is required. We will use Jupyter Notebooks to support the hands-on exercises. Attendees might use Google Colab or JupyterLab to host and run the notebooks.<\/p>\n\n\n\n

    Prerequisite knowledge or skills required for attendees<\/strong><\/p>\n\n\n\n

    Basic level of familiarity with Python.<\/p>\n\n\n\n

    Bibliography<\/strong><\/p>\n\n\n\n

    Barandas, M., Folgado, D., Fernandes, L., Santos, S., Abreu, M., Bota, P., … & Gamboa, H. (2020). TSFEL: Time Series Feature Extraction Library. SoftwareX<\/em>, 11<\/em>, 100456<\/a>.<\/p>\n\n\n\n

    PRESENTERS<\/strong>: Duarte Folgado (Fraunhofer AICOS, Portugal, and FCT Nova, Portugal) and Hui Liu (Cognitive Systems Lab, University of Bremen, Germany)<\/p>\n\n\n\n

    Dr. Duarte Folgado is a Senior Scientist at the Intelligent Systems research group at Fraunhofer AICOS and also an Invited Assistant Professor in the Physics Department of NOVA FCT. His main research interests include data mining, machine learning, and explainable AI, specializing in techniques for time series data.<\/p>\n\n\n\n

    Dr. Hui Liu is a researcher and lecturer at the Cognitive Systems Lab (CSL), University of Bremen, focusing on biomedical engineering. His research interests include biosignal processing, human activity recognition, multimodal time series analysis, and music information retrieval.<\/p>\n\n\n\n

    <\/p>\n","protected":false},"excerpt":{"rendered":"

    Workshop: AI in Drug Discovery The Workshop on AI in Drug Discovery invites cutting-edge contributions in the rapidly evolving field of AI-driven drug discovery. We are seeking submissions encompassing various facets such as generative models, explainable AI, model distillation, uncertainty quantification, reaction informatics and synthetic route prediction, quantum machine learningfor reactivity, methodologies for mining very … Continue reading “Workshops and Special Sessions”<\/span><\/a><\/p>\n","protected":false},"author":682,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-3181","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/e-nns.org\/icann2024\/wp-json\/wp\/v2\/pages\/3181","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/e-nns.org\/icann2024\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/e-nns.org\/icann2024\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/e-nns.org\/icann2024\/wp-json\/wp\/v2\/users\/682"}],"replies":[{"embeddable":true,"href":"https:\/\/e-nns.org\/icann2024\/wp-json\/wp\/v2\/comments?post=3181"}],"version-history":[{"count":21,"href":"https:\/\/e-nns.org\/icann2024\/wp-json\/wp\/v2\/pages\/3181\/revisions"}],"predecessor-version":[{"id":4271,"href":"https:\/\/e-nns.org\/icann2024\/wp-json\/wp\/v2\/pages\/3181\/revisions\/4271"}],"wp:attachment":[{"href":"https:\/\/e-nns.org\/icann2024\/wp-json\/wp\/v2\/media?parent=3181"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}