Konferenzen  >  Mathematik  >  Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen

Algorithmen und Datenstrukturen,    Implementierung und Anwendungen von künstlicher Intelligenz

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Wissenschaftliches Rechnen und Datenwissenschaft,    Informations & Wissensmanagement, Big Data Computing

SACAIR is affiliated with the Centre for AI Research (CAIR).

This multi-, inter-, and transdisciplinary conference is the premium, annual AI conference in Southern Africa. The conference will bring together nationally and internationally established and emerging researchers from various disciplines, including Computer Science, Mathematics, Physics, Statistics, Informatics, Humanities and Philosophy. As in the past, the focus is on growing a formidable network of talented students working in AI from across Africa.

Algorithmic and Data-Driven AI (Computer Science), Symbolic AI (Knowledge Representation and Reasoning), Socio-technical and human-centred AI (Information Systems), Responsible and Ethical AI (Philosophy and Law / Humanities), Inter- and transdisciplinary AI research

International Association of Computer Science and Information Technology

International Conference on Medical Computer Vision and Robotics (MCVR 2023) will be held in Ningbo, China during December 8-10, 2023. It is hosted by the Cixi Institute of BioMedical Engineering.

Email: aimee@iacsit.net

Medical Image Analysis; Segmentation and feature extraction in medical images; Biomedical Signal and Image Processing; Signal processing and analysis of physiological data; Hyperspectral imaging for medical diagnosis; Machine Learning and Artificial Intelligence; Deep learning for medical image analysis; Computer-Assisted Surgery; Virtual and Augmented Reality in Medicine; Medical Robotics and Automation;

California Institute of Technology (Caltech)

Computer-gestütztes Beweisen,    Optimierungsrechnung, Entscheidungsunterstützung

California Institute of Technology (Caltech)

This annual workshop welcomes academics and industry practitioners at the interface of theoretical physics, mathematics, and computer science to discuss research directions centred on the application of machine learning and data science to open problems in fundamental theory, and equivalently the application of techniques from theory to describe the successes of machine learning.

Co-sponsored by Sensors and Systems Society of Singapore and Beijing Institute of Control Robotics and Intelligent Technology, hosted by Robotics Society of Singapore.

The Conference Committee warmly invites you to join us at 2023 the 3rd International Conference on Robotics, Automation and Artificial Intelligence (RAAI 2023), which will be held in Singapore during December 14-16, 2023.

Email: riorob@robotics.ac.cn

Artificial intelligence; Artificial muscle actuators; Augmented reality; Chaotic systems and control; Computational intelligence; Control applications; Digital/sampled-data control; Discrete-event systems; Emerging technology; Evolutionary computation; Feature extraction; Functional safety; Fuzzy and neural systems; Ground robotics;

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The rise of Machine Learning and Artificial Intelligence in nearly all aspects of society stands in stark contrast with the irreproducibility of the literature and its inaccessibility to the general public. Therefore, we have developed OpenML, an online collaborative science platform for Machine Learning, available at http://openml.org. The aim of this workshop is to bring together developers and users of the platform to stimulate further development.

Graphic Era Hill University, Dehradun, Uttarakhand, India

In recent years, Artificial Intelligence (AI) systems and algorithms have become a potential tool for automating wide range of tasks in various fields such as healthcare, engineering, gaming, automobiles, education etc. However, most of the existing algorithms faces significant challenges, as they lack required knowledge to solve computational complexities and sometimes return with inaccurate and simple solutions. To address these problems, Multi-Strategy Learning (MSL) environment is preferred in recent times. MSL is focused with on the research and development of new learning techniques and systems that combine several inferential and/or representational strategies in solving the assigned learning task. The integration of different learning strategies helps to gain more knowledge from the input information available to the learner. This International Conference on Multi-Strategy Learning Environment 2024 serves as the platform for the academicians, researchers and industrialists in the field of Machine Learning, Deep learning etc. to discuss about the integration of Multi-Strategy Learning Environment to enhance the computational process. The primary objective of this conference event is to explore combinations of several different learning methods for the same goal – Enabling automation in almost all the emerging fields.

Tel.: [+918148124693];     Email: conference.icmsle@gmail.com

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The goal of this Dagstuhl Seminar is to provide an interdisciplinary forum to discuss the fundamental principles of fault management and diagnosis, bringing together international researchers from the fields of symbolic reasoning, machine learning, and control engineering. The seminar plans to identify an integrated framework to harmonize problems and algorithms from the different fields, as well as ideas for novel, integrated solutions; and to produce a comprehensive agenda for future research.

Computer-gestütztes Beweisen,    Soft Computing, Evolutionäre Algorithmen, metaheuristische Optimierung, Fuzzylogik

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Peer review is the best mechanism for assessing scientific validity of new research that we have so far. But this mechanism has many well-known issues, such as the different incentives of the authors and reviewers, difficulties with preserving reviewer and author anonymity, confirmation and other cognitive biases that even researchers fall prey to. These intrinsic problems are exacerbated in interdisciplinary fields like Natural Language Processing (NLP) and Machine Learning (ML), where groups of researchers may vary so much in their methodology, terminology, and research agendas, that sometimes they have trouble even recognizing each other's contributions as "research". This Dagstuhl Seminar will cover a range of topics related to organization of peer review in NLP and ML

von Karman Institute for Fluid Dynamics | Universit� libre de Bruxelles (ULB)

The integration of machine learning methods in fluid mechanics continues to grow and open new frontiers. Combining first principles and machine learning offers new avenues to improve predictions and control design, identify patterns, reduce computational costs, enhance measurement techniques, and more generally, get deeper insights into complex fluid phenomena. This lecture series provides an overview of the current state of the art and a pedagogical introduction to the fundamentals of machine learning tools for fluid dynamics. The course is in its second edition. It follows the successful lecture series held in 2020, which attracted more than 200 participants and resulted in an edited book published by Cambridge University Press.

Berufliche Weiterbildung, Kurse (allgemein),    Thermodynamik, Hydrodynamik und Statistische Mechanik

The Alan Turing Institute

This event will gather up to 12 teams from around the world to engage in a challenge using open spending data, development indicators, and the recently released web-based modelling app from the Policy Priority Inference research programme. The organisers would like to invite anyone interested in the intersection of sustainable development, artificial intelligence, open government, and computational social science to attend this one-of-a-kind event. The opening and closing sessions will be open to the public (no need to have team), while the challenger teams will work on a case study for a week with mentorship facilitated by the organisers.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Graphs and knowledge bases have been around for many decades, and research outcomes have tremendously impacted areas like mathematics, artificial intelligence, and databases. However, despite being already coined by the scientific community, technological developments and astronomical data growth make knowledge graph management a fundamental topic nowadays in various computer science areas, supporting novel applications at the science (e.g., biomedicine) and industry (e.g., Google’s Knowledge Graph) level.

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

Kurse und Veranstaltungen für Studenten der Mathematik,    Statistik, Spieltheorie

Institute for Pure and Applied Mathematics (IPAM), Los Angeles, CA

The purpose of this workshop is to bring together neuroscientists who collect and study these data sets with mathematicians and other theorists who develop techniques to model and analyze networks, network dynamics, and dynamical processes on networks. We expect that crossing disciplinary boundaries will greatly facilitate progress, as neuroscientists working in connectomics often lack exposure to recent mathematical developments, while the biological and technical details that underlie connectomic data may not be familiar to mathematicians. This workshop will help define directions of future work in connectomics, with deep links to neuroscience, mathematics, and data science.

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The past decade has seen a rapid growth in the ability to collect large-scale spatiotemporal data sets about sports. Ideally, such data should be used to inform strategic and tactical decision making. On the one hand, strategy is the long-term planning of training sessions, signing of coaches and athletes, rotation and the plan made before a match/race. On the other hand, tactics are short-term and involve the execution and adaptation to the match/race plan. Having insights into the efficacy and feasibility of strategies and tactics is particularly important and challenging within sports because effective and novel strategy & tactics allow weaker teams or athletes to win against stronger ones. Unfortunately, the size, richness, and complexity of modern spatiotemporal sports data means that automated analysis is essential. Alas, the nature of the data has posed a number of challenges for classic analysis techniques. This has spurred the development of novel statistical and machine learning techniques in order to perform more fine-grained analysis of every action and decision during a competitive event. In this Dagstuhl Seminar, we aim to bring together sports researchers in academia and industry to understand how they are using machine learning and statistical techniques to analyze strategy and tactics.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Artificial intelligence and machine learning have made impressive strides in the last decade and – especially in the eyes of the general public – even in the last months, with innovations that have entered the daily life of billions of people. Given the magnitude of the impact of AI, the social good should not be an afterthought: market forces alone may not guarantee that these technologies benefit everyone. Instead, we believe that AI should empower those already championing humanitarian and development causes. In order to accelerate adoption of AI methods where their impact on the social good is largest, we propose to bring together non-governmental organizations working in international development and on humanitarian issues, with AI technical experts (academics, researchers, data scientists, engineers). Primary objectives of this Dagstuhl Seminar are to establish partnerships and build trust, to iterate on concrete problems in a hands-on hackathon, and to demonstrate what is feasible today via case studies.

United Arab Emirates University, Department of Mathematical Sciences

Stochastic calculus finds so extensive applications and enables us to analyze various phenomena affected by random factors, such as asset price movements, option pricing, risk assessment, and geopolitical or climate risks. Moreover, stochastic calculus's effectiveness extends beyond finance, with its use in physics, biology, engineering, and economics, where it helps describe and analyze systems influenced by random events. Its ability to handle stochastic processes makes it an invaluable resource for comprehending and navigating uncertain environments and investigating the panel of risks that affect and impact financial markets or supply chains in commodity one. Another powerful tool for modeling risks is machine learning. Machine learning has emerged as a powerful tool for modeling complex (financial and or economic) systems and making data-driven decisions. By exploiting advanced algorithms, machine learning enables the analysis of vast datasets to identify patterns, forecast market trends, and manage risks. The capabilities of these models range from forecasting stock prices and optimizing investment portfolios to detecting fraudulent activities and assessing credit risk. Machine learning's ability to adapt to changing market conditions and handle non-linear relationships makes it indispensable in the ever-evolving world of finance but also all possible relationships and contagion on commodity markets, offering the potential to enhance decision-making, reduce errors, and unlock valuable insights from data.

Organizing committee;     Email: youssef_elkhatib@uaeu.ac.ae

Financial mathematics, stochastic models, pricing financial derivatives, commodity markets, energy markets, global energy markets, geopolitical risk, climate change, natural resources, energy transition, volatility modeling, high-frequency data analysis, machine learning prediction models, asymmetric structures, software development with applications, complex networks, big data analytics, nonlinear time series analysis, recurrent neural networks, Stochastic calculus and machine Learning with applications to other sciences.

Finanz und Wirtschaftswissenschaft,    Statistik, Spieltheorie

Institute for Pure and Applied Mathematics (IPAM), UCLA

In this workshop, we will explore the statistical and computational requirements for solving various learning problems. The statistical limit is the minimum number of samples needed to solve a learning problem. In contrast, the computational limit is the minimum number of samples required for the problem to be solvable by an efficient algorithm. There is much research on the statistical requirements for many important learning problems, but the computational requirements are less well-understood. We often have large gaps between the two for several important problems (e.g., sparse linear regression). In addition, there are also gaps in our understanding of the costs of various constraints on learning, such as privacy, fairness, interpretability, robustness, and parallelization. This workshop will provide a forum to discuss the latest research and develop new ideas on the above questions. It will help build bridges between different disciplines, such as applied mathematics, statistics, optimization, and theoretical computer science, which will lead to more effective solutions to challenges in statistical inference.

Qazvin Islamic Azad University (QIAU)

With the aim of expanding specialized knowledge in the field of artificial intelligence and robotics engineering and familiarizing students and professors with the latest achievements, Islamic Azad University of Qazvin branch is organizing the 10th international conference on artificial intelligence and robotics. Acquaintance and exchange of the latest scientific information between researchers, students and faculty members of universities and higher education institutions in various fields of artificial intelligence and robotics is one of the goals of this conference.

Email: alirezamortezaei50@gmail.com

Artificial Intelligence, Robotic, IoT, Smart City, Machine Learning, Data Science, Neural Network

Robotik, Automatisierung & Mechatronik,    Internet der Dinge (IoT) und Sensornetze

Far Western University, Bhimdatta, Nepal

The 3rd International Conference on Sentiment Analysis and Deep Learning (ICSADL 2024) is a prominent event organized with the motivation for providing an international forum for all researchers, academicians and students for sharing their research findings on the different facets of Deep Learning and Sentiment Analysis techniques. ICSADL 2024 organized by Far Western University, Nepal will be held on 13-14, March 2024. ICSADL 2024 aims to exchange and share new ideas and research results about all aspects of Sentiment analysis and deep learning which includes Artificial Intelligence, Innovations in robotics, Big Data and also converse the practical challenges encountered and the solutions adopted. ICSADL 2024 will highlight the areas of Statistical Learning Methods, Applied Intelligence, Machine Learning, Natural Language Processing and Cognitive Computing.

Tel.: [8870489968];     Email: icsadl.conf@gmail.com

Call for Papers Sentiment Analysis ♦ Cognitive Computing ♦ Deep Learning ♦ Semantics & Syntactic Analysis ♦ Natural Language Processing ♦ Emotion-Driven Systems ♦ Data Mining for Ontology Matching, Instance Matching and Search ♦ Recommender Systems Applications in Social Networks ♦ AI Driven Sentiment Analysis ♦ Information Retrieval and Document Analysis ♦ Intelligent Decision Making Deep Learning ♦ Fog Computing ♦ Data Analytics and Computing ♦ Reinforcement Learning ♦ Image Processing ♦ Cognitive Networks ♦ Evolutionary Computing ♦ Genetic Algorithms ♦ Statistical Learning Methods ♦ Fuzzy Models ♦ Security and Privacy Methods in Deep Learning Big Data Analytics ♦ Foundational Theoretical or Computational Models for Big Data ♦ Big Data Quality Evaluation and Assurance Technologies ♦ Artificial Intelligence for Big Data ♦ Visualization Analytics for Big Data ♦ Real-Time Big Data Services and Applications ♦ Big Data Services and Applications for Healthcare ♦ Web Intelligence ♦ Cryptographic Algorithms and Protocols ♦ Web Mining and Graph Mining ♦ Machine Learning in Cloud Computing

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The purpose of the seminar will be to initiate a debate around both theoretical foundations and practical methodologies for a "Trustworthiness & Responsibility in AI" framework that integrates quantifiable responsibility and verifiable correctness into all stages of the software engineering process. Such a framework will allow governance and regulatory practises to be viewed not only as rules and regulations imposed from afar, but instead as an integrative process of dialogue and discovery to understand why an autonomous system might fail and how to help designers and regulators address these through proactive governance. In particular, we will consider how to reason about responsibility, blame, and causal factors affecting the trustworthiness of the system.

The Alan Turing Institute

Hosted by The Alan Turing Institute, AI UK 2024 will be an in-depth exploration of how data science and AI can be used to solve real-world challenges. Our diverse programme has been thematically structured around the latest innovations from across the AI ecosystem. With a broad range of interactive content, expect to hear the latest thinking on foundational AI, digital twins, algorithmic bias, AI ethics – and much more.

Wissenschaftliches Rechnen und Datenwissenschaft,    Geschichte und Philosophie von Wissenschaft und Technik

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Autonomous systems are intended to operate without human intervention over prolonged time periods, perceive their operating environment, and adapt to changes – while pursuing defined goals or generating new ones. The perception functions process the inputs of various sensors and generate an internal model of the operating environment. By relying on this model, the decision functions plan and execute the actions required to achieve the goals of the mission. To achieve safety for an autonomous system, the engineers should ensure that the perception functions can sufficiently accurately build the model of the environment, i.e., perception and establishing a context for prediction are reliable. They also seek to ensure that the planned actions are safe, i.e., decisions do not result in actions that endanger humans or other agents in the operating environment.

Softwareentwicklung,    Robotik, Automatisierung & Mechatronik

From April 8 to 10, 2024 the Heinz Maier-Leibnitz Zentrum (MLZ) located in Garching near Munich will host the Machine Learning Conference for X-Ray and Neutron-Based Experiments. The first two days (April 8/9) will take place at the Community Center (Bürgerhaus) Garching with all participants. The third day (April 10) offers optional hands-on workshops and reactor tours at the site of research neutron source FRM II for all interested participants.

Mechanik, Rheologie und Tribologie,    Angewandte Physik: Neutronenstreuung

Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

ACM

Track Chairs

machine learning, artificial intelligence, graph neural networks, graphs, deep learning, pattern recognition

KPR Institute of Engineering and Technology, Coimbatore, India

ICC-ROBINS'24 invites review and research articles from academicians, researchers, and industrial personnel. Status of the paper will be communicated to corresponding author after peer review. Selected papers will be invited for presentation at the discretion of the selection panel.

Tel.: [9994762822];     Email: icc_robins@kpriet.ac.in

Cognitive Theory, Neuromorphic Computing, Cognitive Data Analytics, Artificial Cognition, Cognitive Sensors, Human-Computer Interface, Decision Models, Probabilistic Computing, Natural Language Processing, Cognitive Data Analytics, Edge/Fog/Cloud Computing, Brain Machine Interface, Neuro and Cognitive Science, AI Optimized Hardware, Wireless Sensor Networks, Cyber Physical Systems, Cryptography & Blockchain, Neural Network Design, Pattern Recognition, Classification & Computer Vision, Image Processing, Industrial Robotics, Autonomous Driving System, Robot Communication, Data Sharing, M2M Communications, UAVs, Multi-Robot Coordination, Service Robots, Robots for Inspection, Rehabilitation Robots, Personal/Underwater Robots

Robotik, Automatisierung & Mechatronik,    Implementierung und Anwendungen von künstlicher Intelligenz

RIOROB

2024 the 5th International Conference on Artificial Intelligence, Robotics and Control (AIRC 2024) which will be held during April 22-24, 2024 in The British University in Egypt, Cairo, Egypt. AIRC is an annually held international conference and it's sponsored by The British University in Egypt (BUE), hosted by The British University in Egypt (BUE). AIRC 2024 is intended to provide an international forum for those who wish to present their latest research results, innovative ideas, and experiences in the fields of Artificial Intelligence, Robotics and Control.

Email: riorob@robotics.ac.cn

Artificial Intelligence; AI architectures and applications Agents; Information retrieval and integration; Data mining; Intelligent Robotics; Robot design; Robot kinematics/dynamics/control; Control Engineering; Man-machine interactions; System integration;

Algorithm Lab.

With the recent development of AI, a large amount of research is being actively conducted, and by combining various types of research, ICAITD will explore the manifestation of new electronic intelligence. Deep learning has greatly improved the performance of image recognition, but the basic structure of CNN and pooling has not yet changed. Image generation as a generative AI has achieved remarkable effects using an adversarial learning structure. In natural language processing, epoch-making effects have been achieved through sentence generation. As technology advances, harmful effects such as fake images and fake texts are emerging, and it is necessary to consider engineering countermeasures against these problems. It is important to consider the details of these ideas and technologies regarding AI and proceed with a structural analysis of the current chaotic state of AI.

Tel.: [+81-90-3606-8145];     Email: ai.tokyo.seminar@gmail.com

Data Mining, Deep Learning, Transfer Learning, Image Generation, GAN, Natural Language Genaration, Translation Federated learning, Explain of AI behaviour, Library, Operating System, Hardware, GPU, AI for Games, Ethical Consideration, Negative proposal AI for the social sciences, experimental, and applied research. annotation guidelines.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

This Dagstuhl Seminar provides a unique opportunity for discussing the discrepancy between human and AI generalization mechanisms and crafting a vision on how to align the two streams in a compelling and promising way that combines the strengths of both. To ensure an effective seminar, we aim to bring together cross-disciplinary perspectives across computer and cognitive science fields. Our participants will include experts in Interpretable Machine Learning, Neuro-Symbolic Reasoning, Explainable AI, Commonsense Reasoning, Case-based Reasoning, Analogy, Cognitive Science, and Human-Computer Interaction. Specifically, the seminar will focus on the following questions: How can cognitive mechanisms in people be used to inspire generalization in AI? What Machine Learning methods hold the promise to enable such reasoning mechanisms? What is the role of data and knowledge engineering for AI and human generalization? How can we design and model human-AI teams that can benefit from their complementary generalization capabilities? How can we evaluate generalization in humans and AI in a satisfactory manner?

Interpretable Machine Learning, Human-AI Collaboration, Cognitive Science, Neuro-Symbolic Reasoning, Explainability

Implementierung und Anwendungen von künstlicher Intelligenz,    Wissenschaftliche Philosophie

Superior School of Technology (ESTC) of Casablanca , Hassan II University, Morocco.

After the great success of the first edition of the International Conference on Connected Objects and Artificial Intelligence (COCIA'2023) held at EST Casablanca, Morocco 2023. The Second Edition of the International Conference on Connected Objects and Artificial Intelligence (COCIA'2024) is going to be the international forum for the presentation of new advances and research results in the fields of Connected Objects, Systems, Telecommunications, Artificial Intelligence and Electronic Engineering. The objective of the conference is to bring together academicians, researchers, professionals, executives and practicing engineers, from various industries, research institutes and educational bodies to share and exchange ideas and information on the theme of the conference.

General Chair;     Email: ymejdoub@yahoo.fr

Connected objects, Design of connected objects, Connected Car, Automotive, Connected Systems, Industrial IoT, Smart systems, Smart IoT, Embedded Systems & Industry 4.0, Smart Homes, Smart Cities, Telecommunications, Telecommunication Systems, Communication and connectivity, Wireless Sensor Networks, Sensors, Communications and Network, Wireless and Mobile Communication, Cognitive Radio, RF, antennas, 5G and next generation, Artificial intelligence in RE applications, Artificial Intelligence, IA for embedded systems, IA for IoT, Big data for IoT, Deep Learning, Machine Learning for IoT, Technologies in intelligent systems, Electronic Engineering, Ciruits and Systems, Electronics Engineering and Microelectronics, Biomedical devices&Medical image processing, Robotics and Autonomous Systems, Electric Vehicles, Control of Connected Vehicle Systems, Smart Grids & Microgrids

Elektronik,    Internet der Dinge (IoT) und Sensornetze

Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

Statistik, Spieltheorie ,    Kurse und Veranstaltungen für Studenten der Mathematik

International Association of Computer Science and Information Technology

The 16th International Conference on Digital Image Processing (ICDIP 2024) will be held in Haikou, China during May 24-26, 2024, which is hosted by School of Computer Science and Technology, Hainan University, China.

Email: aimee@iacsit.net

Image & Video Analysis and Segmentation; Cyclo-stationary Signal Analysis; Neural Networks for Signal Processing; Image & Video Coding and Transmission; Data Mining Techniques; Noise Control; Image & Video Labeling and Retrieval; Deep Learning for Images and Video; Nonlinear Signals and Systems; Image & Video Perception and Quality Models; Discrete Hilbert Transforms; Pattern recognition and analysis; Image and Video Compression; Document Analysis; Pattern Recognition in New Modalities;

von Karman Institute for Fluid Dynamics

This course gives an overview and practical hands-on experience in integrating machine learning in fluid dynamics. The course originated as a compressed version of the course Machine Learning for Fluid Dynamics, given at the Research Master program at the von Karman Institute. After a brief review of the machine learning landscape, we show how to frame problems in fluid mechanics as machine learning problems, and we explore challenges and opportunities. Attendees will be guided through a series of tutorial sessions in Python and will tackle several relevant applications: aeroacoustics' noise prediction, turbulence modelling, reduced-order modelling and forecasting, meshless integration of (partial) differential equations, super-resolution and flow control.

Berufliche Weiterbildung, Kurse (allgemein),    Thermodynamik, Hydrodynamik und Statistische Mechanik

Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

Kurse und Veranstaltungen für Studenten der Mathematik,    Veranstaltungen für Studenten der Informatik

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The fundamental and simple model of Stochastic Games was introduced in the fifties by prominent mathematicians (Shapley, Bellman) and form today a well adopted and thoroughly studied notion, with applications in computer science, mathematics, economics, and beyond. This wide adoption implies that many different research communities are involved in the study of stochastic games, oftentimes with different but related goals. Each community develops different tools towards understanding stochastic games, leading to a very broad and diverse literature with a variety of techniques and approaches. The goal of this Dagstuhl Seminar is to bring together researchers interested in the algorithmic aspects of Stochastic Games, from a theoretical as well as practical perspective.

Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

This symposium will treat the impact of Artificial intelligence (AI) and Machine Learning (ML) on chemistry and biology, including drug design. The underlying theme of the discussions will be what we can now do with AI and ML that we couldn’t do before. For instance, is AI simply a new interpolation method in the old traditional computer-aided drug design (CADD)? Are the methods applied nowadays intransparent in contrast to the those applied in the early days? How can we combine the development and application of new materials with AI-generated knowledebases? How much can AI help in the prediction and retro-synthesis of new chemical compounds, and how useful are these compounds? Still, the quality of published data matters as meaningful AI applications depend on the availability of high-quality test datasets.

Yildiz Technical University, Faculty of Mathematical Engineering

This conference aims to provide an overview of some of the most important results recently obtained in various fields of applied mathematics. By bringing together experts in different areas of research (partial differential equations, approximation, applied linear and multilinear algebra, probability and statistics, artificial intelligence and deep learning), the M3A_24 conference will bring together researchers, students and members of industry to discuss the applications of mathematics to practical problems.

Conference Support;     Email: m3a24conference@gmail.com

Numerical Linear and Multilinear Algebra, Approximation, Numerical methods for PDE's, Machine Learning, Optimization, Image Processing, Model Reduction, Control Problems, Inverse and Ill-posed Problems, Computational Statistics

Angewandte Mathematik (allgem.),    Numerische Analysis

Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

Festkörperphysik,    Quanteninformationstheorie und Quanteninformatik

CIRM – Centre International de Rencontres Mathématiques

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Game design and implementation are tasks which require a high amount of creativity, and which must lead to products which require a high amount of fine-tuned functionality. For example, a game “level” should not only look appealing, it should also be playable and it should be interesting to play. These are not features which can be acquired simply by “training on big data”, which is what most developments in modern artificial intelligence are based on. The goal of this Dagstuhl Seminar is to investigate to what extent modern artificial intelligence techniques can produce meaningful and functional game content, and what changes to or extensions of these techniques can improve this AI-driven creative process.

Benutzerschnittstellen, User Experience,    Mulitimedia und Computergrafik, Spiele

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The workshop addresses all application aspects of artificial intelligence from symbolic methods of semantic technologies to sub-symbolic methods of machine learning, neuro-symbolic and hybrid approaches combining symbolic and non-symbolic methods. The focus of this workshop series is on the practical use of these methods to solve real-world application problems.

The South Asia Institute of Science and Engineering

The 7th International Conference on Big Data and Artificial Intelligence (BDAI2024) will be held at North China University of Technology, Beijing, China during July 5-7, 2024. The conference is co-sponsored by IEEE, Research Institute of Big Data Analytics at XJTLU, North China University of Technology, Beijing, China; Co-sponsored by Ocean University of China, China Agricultural University and South China University of Technology.

Ms. Ashley Liu;     Email: brandon@saise.org

Big Data Models and Algorithms; Big Data Architectures; Big Data Management; Big Data Protection, Integrity and Privacy; Big Data Search and Mining; Artificial Intelligence;

Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

Kurse und Veranstaltungen für Studenten der Mathematik,    Veranstaltungen für Studenten der Informatik

Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

This seminar aims at reasoning critically about how we build software and hardware for end-to-end machine learning. We hope that the discussions will lead to increased awareness for understanding the utilization of modern hardware and kickstart future developments to minimize hardware underutilization. We thus would like to bring together academics and industry across fields of data management, machine learning, systems, and computer architecture covering expertise of algorithmic optimizations in machine learning, job scheduling and resource management in distributed computing, parallel computing, and data management and processing. The outcome of the discussions in the seminar will therefore also positively impact the research groups and companies that rely on machine learning.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

AI is a disruptive force. With growing applications in fields like healthcare, transportation, game playing, finance, or robotics in general, AI systems and methods are entering our everyday lives. Such tight interaction with AI requires serious safety, correctness, and reliability considerations. Recently, the field of safety in AI has triggered a vast amount of research. Via a diverse program with ample space for open yet guided discussion, we aim to address a number of key challenges that range across all fields.

TOBB Economics and Technology University

ATSF is an international conference series organized to bring together researchers from ALL areas of Approximation Theory and Special Functions to discuss new ideas and new applications. This organization, which has been held seven times so far as mini-symposia, has grown gradually over the years and will be held for the eighth time on September 4-7, 2024, hosted by TOBB Economics and Technology University (Ankara, Türkiye).

Conference Chair;     Email: atsf2024@gmail.com

Approximation Theory, Summability Theory, Applied Mathematics, Special Functions; Analysis and Functions Theory

Angewandte Mathematik (allgem.),    Analysis

AIPR2024 is mainly organised by Huaqiao University, co-sponsored by Wuhan Institute of Technology and Minnan Normal University, China.

Today, AI is surrounding us, it is coordinated into our every day lives. Artificial Intelligence is empowering machines to behave like humans by adding Emotional Quotient(EQ) to them. Artificial intelligence is a blooming technological advancement, from software’s like Alexa, to basic human command system. Big data can be used to speed up AI development and soon AI will significantly impact our everyday lives. AI is the future and it’s here to stay. In the future, there will be a greater demand for people who can program machines to “think” and enrich human lives. A pattern is a short description of the data. Pattern recognition is how agents make predictions, which is a central problem in AI. Pattern recognition forms the basis of learning and action for all living things in nature. The importance of Pattern Recognition can be traced from its needs in daily life- pattern recognition plays an important role: reading texts, identifying people, retrieving objects, or finding the way in a city. Once patterns are established, learned from some examples or from a teacher, we are able to classify new objects or phenomena into a class of known patterns. Pattern Recognition is a vast area of research and there are endless number of possibilities as pattern recognition is still under research as it is getting more futuristic and intelligent with a great affect in human life in a positive manner.

Email: Dunn@iact.net

Pattern Recognition and Machine Learning; Computer Vision and Robot Vision; Image, Speech, Signal and Video Processing; Document Analysis, Biometrics and Pattern Recognition Applications; Biomedical Image Analysis and Applications;

International Academy of Computing Technology

2024 7th International Conference on Big Data and Machine Learning will be held in Xiamen, China during September 20-22, 2024. Machine Learning (ML) and Big Data Processing & Analytics (BDA) are both evolutionary fields of computing and the developments in these fields are complementing each other. The ever changing data landscape in modern digital world have resulted in newer ways of data processing frameworks in order to get meaningful insights which are unprecedented. ML based Big Data Processing has gained popularity and new developments are on the rise for efficient data processing. This field is witnessing unparalleled emergence of new methods and approaches for efficient data processing in order to discover interestingness for decision making. Thus, more and more ML based data processing approaches are being used for Big Data Processing. With the splurge data from different newer sources, heterogeneous nature of data, uncertain & unstructured data, the so called Big Data with all its characteristics (5 Vs) there is an ever increasing need to use approaches which aid in modelling and processing of these data, provide automated approach to data processing and so on. Thus the BDML conference series welcomes the scholars who are researching in this interesting & challenging domain of ML and Big Data Processing and provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Big Data and Machine Learning.

Email: Dunn@iact.net

Big Data; Machine learning;

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Institute 2024 has an overarching focus on Next-Generation Human-Centred AI and Cognitive Technologies; its technical programme addresses the formal & cognitive foundations for human-centred computing (for AI), and the human-centred design, development, and usability of cognitive technologies aimed at human-in-the-loop assistance & empowerment in decision-making, planning, creative-technical problem-solving, and automation. Application areas to be addressed in the institute include autonomous vehicles, cognitive robotics, social robotics, cognitive/creative design assistance technologies (e.g., architecture and built environment design, visuo-auditory narrative media design), clinical diagnostics, technology-assisted education/learning.

Institute for Pure and Applied Mathematics (IPAM), UCLA

The study of biological intelligences has been transformed by breakthroughs in experimental and observational methods. New data have expanded our appreciation of the sophisticated behaviors exhibited by human and non-human animals across a range of taxa. Traces of intelligent behavior are typically high-dimensional, with complex structure in space and time. They often involve multiple data streams: for example, positional information from individual members of a bird flock, as well as “point-of-view” recordings showing what birds are looking at, moment by moment; or electrocorticographic recordings of human brain activity, as well as detailed traces of motor behavior. These traces are often buried beneath complex noise environments: individual animal movements must be pulled out of extensive visual clutter; whale song must be isolated from ocean noise. This complex, high-dimensional data requires entirely new approaches to data representation, integration, and analysis. This workshop will focus on the challenges raised by high-throughput, high-dimensional studies of intelligent behavior. It will bring together experts in animal cognition, computational neuroscience, and cognitive science with mathematicians and computer scientists focused on relevant methods in machine learning, network science, high-dimensional statistics, and information theory.

Part of the Long Program Mathematics of Intelligences

Institute for Pure and Applied Mathematics (IPAM), UCLA

Neural networks have confounded traditional ML beliefs about the dangers of overfitting and the need for regularization. They have also given rise to many new empirical findings around transfer learning, adversarial examples, compressibility, scaling laws (relating the size of datasets, models, and compute), grokking, and so on. What is needed to explain and predict all this is a rich new theory of learning capable of addressing the delicate interplay between model, data, and optimizers at large scale. This workshop will bring together top researchers driving the frontiers of this work with experts in both theory and experiment for natural intelligence. The result will be a scholarly discussion on how to frame questions about learning and how to distill the similarities and differences between learning with biological and artificial systems.

Part of the Long Program Mathematics of Intelligences

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

Kurse und Veranstaltungen für Studenten der Mathematik,    Implementierung und Anwendungen von künstlicher Intelligenz

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The vision of NFDI4DS is to support all steps of the complex and interdisciplinary research data lifecycle in Data Science and Artificial Intelligence. The overarching objective of NFDI4DS is the development, establishment, and sustainment of a national research data infrastructure for the Data Science and Artificial Intelligence community. The key idea is to work towards increasing the transparency, reproducibility and fairness of Data Science and Artificial Intelligence projects, by making all digital artifacts available, interlinking them, and offering innovative tools and services. Within this Dagstuhl Research Meeting, the NFDI4DS partners will reflect on their progress so far, and will work on their long-term strategy.

Informations & Wissensmanagement, Big Data Computing,    Wissenschaftliches Rechnen und Datenwissenschaft

Institute for Pure and Applied Mathematics (IPAM), UCLA

This workshop will draw together researchers creating new algorithms and architectures (e.g., active symbol architectures, evolutionary programming approaches, neural program synthesis) with mathematicians and theoretical computer scientists who specialize in non-convex optimization, the theory of programming languages, type theory, proof theory, and category theory. It aims to promote cross-fertilization between these paradigms and more traditional approaches, while stimulating the development of rigorous foundations for evolutionary computing, program synthesis, and other naturalistic approaches to AI.

Part of the Long Program Mathematics of Intelligences

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Non-Bayesian approaches appear to solve many problems that Bayesians once dreamt of solving using Bayesian methods. We thus believe that it is timely and important to rethink and redefine the promises and challenges of Bayesian approaches; and also to elucidate which Bayesian methods might prevail against their non-Bayesian competitors; and finally identify key application areas where Bayes can shine.

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

Kurse und Veranstaltungen für Studenten der Mathematik,    Astronomie, Astrophysik und Kosmologie

Institute for Pure and Applied Mathematics (IPAM), UCLA

This workshop will bring together leading experts in the mathematical modeling of collective intelligence. While the workshop will explore collective intelligence in many different systems, substrates (both natural and artificial), and scales, it will focus on three broadly applicable modeling frameworks: dynamical systems, which have been applied to systems as various as brains and insect swarms; statistical physics, which has illuminated the behavior of flocking birds and deep learning architectures; and game theory, which has been applied to animal, human, and AI collectives. The collective intelligence domain cultivates the further development of formal tools for treating compositionality; the emergence of new capacities and collective degrees of freedom at multiple scales; and the higher-level institutions that coordinate the intelligent behavior of lower-level parts.

Part of the Long Program Mathematics of Intelligences

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

At the Dagstuhl Seminar on RNA Regulation and Multidimensional Transcriptomics, we will delve into the intersection of computational biology, deep learning, and RNA research. The event will explore how cutting-edge AI technologies are reshaping our understanding of RNA-based gene regulation and its role in health and disease.

Genomforschung und Bioinformatik,    Implementierung und Anwendungen von künstlicher Intelligenz

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Artificial Intelligence, Computers and Society, Databases, Trust, Transparency, Accountability, Knowledge Graphs, Web Data