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The 9th International Conference on Machine Learning and Machine Intelligence (MLMI 2026)Conference Theme: "AI-Driven Innovations for a Sustainable Future"https://mlmi.netSubmission Deadline: February 5, 2026Notification Deadline: March 5, 2026Registration Deadline: March 20, 2026 We are thrilled to announce that the 9th International Conference on Machine Learning and Machine Intelligence (MLMI 2026) will be held in Tokyo, Japan, from July 17 to 20, 2026. Organized by Rikkyo University, Japan, MLMI 2026 will bring together leading researchers, academics, and industry professionals to explore the latest advancements in machine learning and machine intelligence. The conference will feature insightful presentations, engaging discussions, and vibrant networking opportunities, all set in the culturally rich city of Tokyo. MLMI 2026 aims to advance the field of machine learning and machine intelligence by fostering interdisciplinary collaboration and showcasing cutting-edge research. The scope of MLMI 2026 includes, but is not limited to, deep learning, reinforcement learning, natural language processing, computer vision, robotics, AI ethics, and the integration of machine intelligence in domains such as healthcare, finance, autonomous systems, and environmental sustainability. By providing a platform for knowledge exchange and collaboration, MLMI 2026 seeks to drive innovation and inspire the development of intelligent systems that benefit society.MLMI 2026 offers a unique opportunity to engage with the latest research, connect with global experts, and contribute to the future of machine intelligence. We invite you to join us for four days of learning and networking. You are guaranteed to leave the event with a suitcase full of knowledge and inspiration.Submitted papers will be peer reviewed by conference committees, and accepted papers after proper registration and presentation will be published into Lecture Notes in Electrical Engineering (Electronic ISSN: 1876-1119 & Print ISSN: 1876-1100) as a proceedings book volume. The book series will be indexed by EI Compendex, SCOPUS, INSPEC, SCImago and other database.Topics of interest for submission include, but are not limited to:Track 1: Deep Learning and Neural Architectures Track 2: Reinforcement Learning and Sequential Decision Making▪ Architectural Advances ▪ Advanced Reinforcement Learning Algorithms▪ Efficient Training Techniques ▪ Multi-Agent Reinforcement Learning▪ Graph Neural Networks ▪ Time Series and Sequential Data▪ Generative Models ▪ Predictive Modeling▪ Neural Architecture Search ▪ Anomaly Detection▪ Transfer Learning ▪ Sequential Decision Making ▪ Real-Time AI Systems Track 3: Applied Machine Intelligence Track 4: Natural Language Processing and Multimodal Learning▪ Machine Learning in Healthcare ▪ Large Language Models▪ AI in Robotics ▪ Multilingual and Low-Resource NLP▪ AI for Cybersecurity ▪ Multimodal Machine Learning▪ Biometric and Behavioral Analytics ▪ Cross-Modal Retrieval and Generation▪ Industry-Specific Applications Track 5: Emerging Paradigms and Future Directions Track 6: Explainable, Ethical, and Human-Centered AI▪ Quantum Machine Learning ▪ Explainable AI (XAI)▪ Federated and Distributed Learning ▪ Ethical AI and Fairness▪ AutoML and Meta-Learning ▪ Privacy-Preserving Machine Learning▪ Sustainable and Green AI ▪ Human-Centered AI▪ Hybrid Models ▪ Augmented Intelligence

The 10th International Conference on Deep Learning TechnologiesJuly 17-19, 2026, Kunming, Chinahttps://www.icdlt.orgSubmission Deadline: March 1, 2026Notification of Acceptance: April 1, 2026Camera Ready Deadline: April 15, 2026Registration Deadline: April 15, 2026Conference Dates: July 17-19, 2026You are invited to attend 2026 10th International Conference on Deep Learning Technologies will be held during July 17-19, 2026 in Kunming, China. It's sponsored by Kunming University of Science and Technology, China, organized by the Faculty of Information Engineering and Automation of Kunming University of Science and Technology. It's to provide a valuable opportunity for researchers, scholars and scientists to exchange their ideas face to face in Deep Learning Technologies.We invite researchers from deep learning technologies to participate and submit their work to the program. Likewise, any work on deep learning that has a relation to any of these fields or potential for the usage in any of them is welcome. Please refer to the different submission categories under "Call for Papers" above for further details.Track 1: Deep Learning Model and AlgorithmRecurrent Neural Network (RNN) Sparse Coding Neuro-Fuzzy Algorithms Evolutionary Methods Convolutional Neural Networks (CNN) Deep Hierarchical Networks (DHN) Dimensionality Reduction Unsupervised Feature Learning Deep Boltzmann Machines Generative Adversarial Networks (GAN) Autoencoders Deep Belief Networks Meta-Learning and Deep Networks Deep Metric Learning Methods MAP Inference in Deep Networks Deep Reinforcement Learning Learning Deep Generative Models Deep Kernel Learning Graph Representation Learning Gaussian Processes for Machine Learning Clustering, Classification and Regression Classification ExplainabilityTrack 2: Machine Learning Theory and TechnologyNovel machine and deep learningActive learningIncremental learning and online learningAgent-based learningManifold learningMulti-task learningBayesian networks and applicationsCase-based reasoning methodsStatistical models and learningComputational learningEvolutionary algorithms and learningFuzzy logic-based learningGenetic optimizationClustering, classification and regressionNeural network models and learningParallel and distributed learningReinforcement learningSupervised, semi-supervised and unsupervised learningTensor Learning Deep and Machine Learning for Big Data Analytics:Deep/Machine learning based theoretical and computational modelsMachine learning (e.g., deep, reinforcement, statistical relational, transfer)Model-based reasoningTrack 3: Deep and Machine Learning ApplicationsDeep Learning for Computing and Network PlatformsRecommender systemsDeep Learning for Social media and networksDeep Learning in Computer VisionDeep learning in speech recognitionDeep Learning in Nature Language Processing,Deep Learning in Machine TranslationDeep learning in bioinformaticsDeep Learning in Medical Image AnalysisDeep Learning in Climate ScienceDeep Learning in Board Game ProgramsDeep and Machine Learning for Data Mining and KnowledgeTrack 4: Responsible AI, Security, and GovernanceAI safetyPrivacy preservationAlgorithmic FairnessFairness and ethicsClassification Explainability and Explainable AI (XAI)AI EthicsAI governanceGreen Deep LearningSynthetic Data GenerationSocial and Economic Impact of AISustainable AIAI Risk AssessmentThe accepted paper will be included into ICDLT 2026 Conference ProceedingsPUBLICATION HISTORYICDLT 2025 Proceedings - 979-8-4007-1852-6 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2024 Proceedings - 979-8-4007-1686-7 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2023 Proceedings - 979-8-4007-0752-0 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2022 Proceedings - 978-1-4503-9693-6 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2021 Proceedings - 978-1-4503-9016-3 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2020 Proceedings - 978-1-4503-7548-1 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2019 Proceedings - 978-1-4503-7160-5 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2018 Proceedings - 978-1-4503-6473-7 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2017 Proceedings - 978-1-4503-5232-1 | ACM Digital Library | Ei & SCOPUS Index

Programme OverviewPROGRAMME FEE: US$5,000STARTS ON: 31 March 2025DURATION: 20 weeks (4 - 6 Hours/ week)This new year, invest in a learning journey to upskill and gain a competitive edge. Emeritus is collaborating with NUS School of Computing to help you unlock transformative career growth. Enrol before 24 January 2025 using this code: APAC200ALL8364 and get USD200 programme fee benefit. Limited seats to success available. Claim yours now.Application fees: USD60Read more at https://cmu.to/A8Tt0With organisations and consumers being driven by technological innovations, such as Artificial Intelligence, Automation and Data Analytics, organisations are looking for professionals with a deep understanding of these innovations who can foster significant organisational impact. This is the time to boost your career. The NUS School of Computing Technology Leadership and Innovation Programme caters to individuals aspiring to drive strategic transformation in AI, digitalisation and innovation domains. Through the carefully curated modules and capstone project, you develop the requisite mindset and skills to implement impactful strategies using global technological innovations .Programme Modules (Module 1-18)Module 1: Overview of Digital TransformationModule 2: Digital Business ModelsModule 3: Strategy Formulation and Process of Digital TransformationModule 4: Digital Transformation Action PlanModule 5: Role of Data and AnalyticsModule 6: ML-Deep Learning and Neural NetworkModule 7: Robotics and Reinforcement Learning Module 8: Generative AIModule 9: AI and Business ApplicationModule 10: CybersecurityModule 11: Other Technologies: IoT, AR/VRModule 12: Innovation Strategy to Address Digital DisruptionModule 13: System Thinking and Agile MethodologiesModule 14: Implementing Digital InnovationModule 15: Building Team and Digital Culture for TransformationModule 16: Managing Digital Complexities and Change ManagementModule 17: Leading through ESG ChallengeModule 18: Technology and Innovation: Security and Governance

Call for Papers2025 IEEE 2nd International Conference on Deep Learning and Computer VisionJinan|ChinaApr. 18-21, 2025Full paper submission Deadline: Feb. 10th, 2025Acceptance Notification: Mar. 26th, 2025Registration Deadline: Apr. 11th , 2025Conference Date: Apr. 18-21st, 2025Link: https://www.icdlcv.orgContact: icdlcv@126.comYou are kindly encouraged to contribute to and help shape the conference through submissions of your research abstracts and papers to DLCV 2025 conference. Topics of interest for submission include:Track 1: Deep LearningMachine LearningUnsupervised LearningDeep Reinforcement LearningConvolutional Neural Networks (CNN)Deep Hierarchical Networks (DHN)Recurrent Neural Network (RNN)Neuro-Fuzzy AlgorithmsAutoencodersGraph Representation LearningClustering, Classification and RegressionEvolutionary MethodsAutonomic ComputingUser ModelingInformation Retrieval and ReuseQuestion-AnsweringCognitive ArchitecturesDeep Learning Approach in Recommendation SystemsTrack 2: Computer VisionBig data and computer visionBiomedical image analysisImage and video codingImage and video retrievalRemote sensing imageComputational photographyOptimization and method of studySensor and displayData collection and performance analysisComputer vision of deep learningDocument image analysisCharacter identificationAnalysis, behavior identityVisual modelsVideo analysisMultimodal information processingVisual and languageMotion and tracking3D reconstructionHuman-computer interaction

41st Conference on Uncertainty in Artificial IntelligenceRio de Janeiro, BrazilJuly 21-25, 2025Link: https://www.auai.org/uai2025/The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. UAI is supported by the Association for Uncertainty in Artificial Intelligence (AUAI).Important DatesNameDatePaper submission deadline (incl. supp. material)February 10th, 2025 (23:59 Anywhere on Earth, AoE)Author response/discussion periodApril 3rd - April 10th, 2025 (23:59 AoE)Author notificationMay 6th, 2025 (23:59 AoE)Camera-ready deadlineJune 6th, 2025 (23:59 AoE) Subject AreasBelow you find a non-exhaustive list of relevant topics for your reference.AlgorithmsApproximate InferenceBayesian MethodsBelief PropagationExact InferenceKernel MethodsMissing Data HandlingMonte Carlo MethodsOptimization - CombinatorialOptimization - ConvexOptimization - DiscreteOptimization - Non-ConvexProbabilistic ProgrammingRandomized AlgorithmsSpectral MethodsVariational MethodsApplicationsCognitive ScienceComputational BiologyComputer VisionCrowdsourcingEarth System ScienceEducationForensic ScienceHealthcareNatural Language ProcessingNeurosciencePlanning and ControlPrivacy and SecurityRoboticsSocial GoodSustainability and Climate ScienceText and Web DataLearningActive LearningAdversarial LearningCausal LearningClassificationClusteringCompressed Sensing and Dictionary LearningDeep LearningDensity EstimationDimensionality ReductionEnsemble LearningFeature SelectionHashing and EncodingMultitask and Transfer LearningOnline and Anytime LearningPolicy Optimization and Policy LearningRankingReinforcement Learning and BanditsRelational LearningRepresentation LearningSemi-Supervised LearningStructure LearningStructured PredictionUnsupervised LearningModelsFoundation ModelsGenerative ModelsGraphical ModelsModels for Relational DataNeural NetworksProbabilistic CircuitsRegression ModelsSpatial, Temporal and Spatio-Temporal ModelsTopic Models and Latent Variable ModelsPrinciplesCausalityComputational and Statistical Trade-OffsExplainabilityFairnessPrivacyReliabilityRobustness(Structured) SparsityRepresentationConstraintsDempster-Shafer(Description) LogicsImprecise ProbabilitiesInfluence DiagramsKnowledge Representation LanguagesTheoryComputational ComplexityControl TheoryDecision TheoryGame TheoryInformation TheoryLearning TheoryProbability TheoryStatistical Theory

Deadline: 22 May 2024Research Field: Professions and applied sciencesFunding Type: FundingMore Information: https://www.scientifyresearch.org/grant/career-development-award-basic-biological-science-italy/Project Key Words: Congenital heart disease, phonocardiogram, Artificial IntelligencePost summaryOur project focuses on harnessing the power of Artificial Intelligence (AI) to analyze phonocardiogram (PCG) data for the early detection of congenital heart disease (CHD). By developing advanced AI algorithms, we aim to revolutionize the way CHD is diagnosed and managed, leading to better outcomes for patients.Person specificationQualificationsEssentialHonours Degree (minimum 2:1) in biomedical engineering, computer science, electrical/ electronics engineering, or a related field.Strong background in signal processing and machine learning.DesirableProficiency in programming languages such as Python, R or MATLAB.Practical experience in simulation environments and Machine Learning libraries.Experience with cardiovascular physiology or cardiology research.Experience with GUI development and web applications. Knowledge & Experience EssentialExperience with machine learning techniques, including deep learning and neural networks.Strong analytical and problem solving skills. DesirablePrevious research experience in healthcare or medical signal processing projects.Experience with data visualization tools.Familiarity with phonocardiogram data analysis and interpretation. Skills & CompetenciesEssentialApplicants whose first language is not English must demonstrate on application that they meet SETU’s English language requirements and provide all necessary documentation. See Page 7 of the Code of PracticeIn order to be shortlisted for interview, you must meet the SETU English speaking requirements so please provide evidence in your application. DesirableExcellent written and verbal communication skills.Willingness and motivation to learn and experience new theoretical and technological areas.