Paper Submission:November 12, 2021, 11:59 pm (anywhere on earth) Author Notification: December 3, 2021Full conference:February 22 March 1, 2022Workshop:February 28 March 1, 2022. Submissions that do not meet the formatting requirements will be rejected without review. A fundamental problem in the use of artificial neural networks is that the first step is to guess the network architecture. Submissions will be peer-reviewed, single-blinded, and assessed based on their novelty, technical quality, significance, clarity, and relevance regarding the workshop topics. The main goal of the dialog system technology challenge (DSTC) workshop is to share the result of five main tracks of the tenth dialog system technology challenge (DSTC10). Can AI achieve the same goal without much low-level supervision? The accepted papers will be posted on the workshop website and will not appear in the AAAI proceedings. All papers must be submitted in PDF format, using the AAAI-22 author kit. The workshop is being organized by application area or other, panels, invited speakers, interactive, small groups, discussions, presentations. DeepGAR: Deep Graph Learning for Analogical Reasoning. The third AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-22) builds on the success of previous years PPAI-20 and PPAI-21 to provide a platform for researchers, AI practitioners, and policymakers to discuss technical and societal issues and present solutions related to privacy in AI applications. It drives discoveries in business, economy, biology, medicine, environmental science, the physical sciences, the humanities and social sciences, and beyond. San Francisco, USA . Submissions including full papers (6-8 pages) and short papers (2-4 pages) should be anonymized and follow the AAAI-22 Formatting Instructions (two-column format) at https://www.aaai.org/Publications/Templates/AuthorKit22.zip. ; (2) Deep Learning (DL) approaches that can exploit large datasets, particularly Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL); (3) End-to-end learning methodologies that mend the gap between ML model training and downstream optimization problems that use ML predictions as inputs; (4) Datasets and benchmark libraries that enable ML approaches for a particular OR application or challenging combinatorial problems. We encourage long papers, short papers and demo papers. . At the AAAI 2022 Workshop on Video Transcript Understanding (VTU @ AAAI 2022), we aim to bring together researchers from various domains to make the best of the knowledge that all these videos contain. No supplement is allowed for extended abstracts. We consider submissions that havent been published in any peer-reviewed venue (except those under review). Deep learning has achieved significant success for artificial intelligence (AI) in multiple fields. Submissions will be peer reviewed, single-blinded. Submissions will be accepted via the Easychair submission website. The challenge requires participants to build competitive models for diverse downstream tasks with limited labeled data and trainable parameters, by reusing self-supervised pre-trained networks. Share. Both the research papers track and the applied data science papers track will take . Saliency-Augmented Memory Completion for Continual Learning. The workshop attracted about 100 attendees. While the research community is converging on robust solutions for individual AI models in specific scenarios, the problem of evaluating and assuring the robustness of an AI system across its entire life cycle is much more complex. It will start with a 60-minute mini-tutorial covering the basics of RL in games, and will include 2-4 invited talks by prominent contributors to the field, paper presentations, a poster session, and will close with a discussion panel. Zirui Xu, Fuxun Xu, Liang Zhao, and Xiang Chen. RAISAs systems-level perspective will be emphasized via three main thrusts: AI threat modeling, AI system robustness, explainable AI, system lifecycle attacks, system verification and validation, robustness benchmarks and standards, robustness to black-box and white-box adversarial attacks, defenses against training, operational and inversion attacks, AI system confidentiality, integrity, and availability, AI system fairness and bias. Geographical Mapping and Visual Analytics for Health Data, Biomedical Ontologies, Terminologies, and Standards, Bayesian Networks and Reasoning under Uncertainty, Temporal and Spatial Representation and Reasoning, Crowdsourcing and Collective Intelligence, Risk Assessment, Trust, Ethics, Privacy, and Security, Computational Behavioral/Cognitive Modeling, Health Intervention Design, Modeling and Evaluation, Applications in Epidemiology and Surveillance (e.g., Bioterrorism, Participatory Surveillance, Syndromic Surveillance, Population Screening), Hybrid methods, combining data driven and predictive forward models, biomedical signal analysis/modeling (EEG, ECG, PPG, EMG, fMRI, IMU, medical/clinical data, etc. [Bests of ICDM]. We also invite papers that have been published at other venues to spark discussions and foster new collaborations. These challenges are widely studied in enterprise networks, but there are many gaps in research and practice as well as novel problems in other domains. Paper Submission Deadline: May 26, 2022 Author Notification: June 20, 2022 Camera Ready: July 9, 2022 Workshop: August . Submissions of technical papers can be up to 7 pages excluding references and appendices. We will end the workshop with a panel discussion by top researchers in the field. An example of the latter is theCascade Correlation algorithm, as well as others that incrementally build or modify a neural network during training, as needed for the problem at hand. Call for Participation The 3rd KDD Workshop on Data-driven Humanitarian Mapping and Policymaking solicits research papers, case studies, vision papers, software demos, and extended abstracts. Natural language reasoning and inference. Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment. This workshop aims to bring researchers from these diverse but related fields together and embark on interesting discussions on new challenging applications that require complex system modeling and discovering ingenious reasoning methods. This manual extraction process is usually inefficient, error-prone, and inconsistent. Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu, Hemant Purohit, Xiang Chen, Kai Zeng and Liang Zhao. Graph neural networks on node-level, graph-level embedding, Joint learning of graph neural networks and graph structure, Learning representation on heterogeneous networks, knowledge graphs, Deep generative models for graph generation/semantic-preserving transformation, Graph2seq, graph2tree, and graph2graph models, Spatial and temporal graph prediction and generation, Learning and reasoning (machine reasoning, inductive logic programming, theory proving), Natural language processing (information extraction, semantic parsing, text generation), Bioinformatics (drug discovery, protein generation, protein structure prediction), Reinforcement learning (multi-agent learning, compositional imitation learning), Financial security (anti-money laundering), Cybersecurity (authentication graph, Internet of Things, malware propagation), Geographical network modeling and prediction (Transportation and mobility networks, social networks), Computer vision (object relation, graph-based 3D representations like mesh), Lingfei Wu (JD.Com Silicon Valley Research Center),lwu@email.wm.edu, 757-634-5455, https://sites.google.com/a/email.wm.edu/teddy-lfwu/, Jian Pei (Simon Fraser University), jian_pei@sfu.ca, 778-782-6851, https://sites.google.com/view/jpei/jian-peis-homepage, Jiliang Tang (Michigan State University), tangjili@msu.edu, 408-744-2053, https://www.cse.msu.edu/~tangjili/, Yinglong Xia (Facebook AI), yinglongxia@gmail.com, 213-309-9908, https://sites.google.com/site/yinglongxia/, Xiaojie Guo (JD.Com Silicon Valley Research Center), Xguo7@gmu.edu, 571-224-5527, https://sites.google.com/view/xiaojie-guo-personal-site, Sutanay Choudhury (Pacific Northwest National Lab), Stephan Gnnemann (Technical University of Munich), Shen Wang, (University of Illinois at Chicago), Yizhou Sun (University of California, Los Angeles), Lingfei Wu (JD.Com Silicon Valley Research Center), Zhan Zheng (Washington University in St. Louis), Feng Chen (University at Albany State University of New York), Development of corpora and annotation guidelines for multimodal fact checking, Computational models for multimodal fact checking, Development of corpora and annotation guidelines for multimodal hate speech detection and classification, Computational models for multimodal hate speech detection and classification, Analysis of diffusion of Multimodal fake news and hate speech in social networks, Understanding the impact of the hate content on specific groups (like targeted groups), Fake news and hate speech detection in low resourced languages, Vulnerability, sensitivity and attacks against ML, Adversarial ML and adversary-based learning models, Case studies of successful and unsuccessful applications of ML techniques, Correctness of data abstraction, data trust, Choice of ML techniques to meet security and quality, Size of the training data, implied guaranties, Application of classical statistics to ML systems quality, Sensitivity to data distribution diversity and distribution drift, The effect of labeling costs on solution quality (semi-supervised learning), Software engineering aspects of ML systems and quality implications, Testing of the quality of ML systems over time, Quality implication of ML algorithms on large-scale software systems, Explainable/Interpretable Machine Learning, Fairness, Accountability and Transparency, Interactive Teaching Strategies and Explainability, Novel Research Contribution describing original methods and/or results (6 pages plus references), Surveys summarizing and organizing recent research results (up to 8 pages plus references), Demonstrations detailing applications of research findings, and/or debating relevant challenges and issues in the field (4 pages plus references), Constraint satisfaction and programming (CP), (inductive) logic programming (LP and ILP), Learning with Multi-relational graphs (alignment, knowledge graph construction, completion, reasoning with knowledge graphs, etc. the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018) (acceptance rate: 20.6%), Stockholm, Sweden, Jul 2018, accepted. Taseef Rahman, Yuanqi Du, Liang Zhao, Amarda Shehu. Papers must be between 4-8 pages with the AAAI submission format submitted to the track of regular paper, SUPERB or Zero Speech result paper. The accepted papers are allowed to be submitted to other conference venues. ), The workshop will be organized as half-day event with 2 invited speakers, follow by presentation from accepted papers (both ordinary papers, and shared task paper). We will also organize 3 shared tasks in this workshop: punctuation restoration, domain adaptation for punctuation restoration, and chitchat detection. Deep Graph Transformation for Attributed, Directed, and Signed Networks. 205-214, San Francisco, California, Aug 2016. Papers will be peer-reviewed by the Program Committee (2-3 reviewers per paper). All submissions will be peer-reviewed. The program of the workshop will include invited talks, paper presentations and a panel discussion. with other vehicles via vehicular communication systems (e.g., dedicated short range communication (DSRC), vehicular ad hoc networks (VANETs), long term evolution (LTE), and 5G/6G mobile networks) for cooperation. Interesting challenges in this domain include the drastic increase of work from home or remote work, the imbalance between the demand and supply of the job market, the popularity of independent workers, the capability of helping job seekers on their whole job seeking journey and career development, the different objectives and behaviors of all major stakeholders in the ecosystem, e.g. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. Liang Zhao, Feng Chen, Jing Dai, Ting Hua, Chang-Tien Lu, and Naren Ramakrishnan. The workshop aims at bridging formalisms for learning and reasoning such as neural and symbolic approaches, probabilistic programming, differentiable programming, Statistical Relation Learning and using non-differentiable optimization in deep models. Interactive Machine Learning (IML) is concerned with the development of algorithms for enabling machines to cooperate with human agents. It is a forum to bring attention towards collecting, measuring, managing, mining, and understanding multimodal disinformation, misinformation, and malinformation data from social media. Finally, there is an increasing interest in AI in moving beyond traditional supervised learning approaches towards learning causal models, which can support the identification of targeted behavioral interventions. 2022. The workshop welcomes the submission of work on, but not limited to, the following research directions. Interpretable Deep Graph Generation with Node-edge Codisentanglement. The accepted papers will be allocated either a contributed talk or a poster presentation. In other words, many existing FL solutions are still exposed to various security and privacy threats. Make sure your desired study programs are open for admission in the session when you would like to start your studies. Advances in IML promise to make AIs more accessible and controllable, more compatible with the values of their human partners and more trustworthy. How do metrics of capability and generality, and the trade-offs with performance affect safety? Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu, and Yanfang Ye. Yuyang Gao and Liang Zhao. Please note that foreign students must allow for 3 to 6 months to complete all the formalities required to study in Canada. Dataset(s) will be provided to hack-a-thon participants. In addition, several invited speakers with distinguished professional background will give talks related the frontier topics of GNN. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) (Acceptance Rate: 14.99%), accepted, 2022. Chen Ling, Tanmoy Chowdhury, Junji Jiang, Junxiang Wang, Xuchao Zhang, Haifeng Chen, and Liang Zhao. The positive/negative social impacts and ethical issues related to adversarial ML. "Spatiotemporal Event Forecasting in Social Media." [Best Poster Runner-Up Award]. Representation Learning on Spatial Networks. As far as we know, we are the first workshop to focus on practical deep learning in the wild for AI, which is of great significance. ISPRS International Journal of Geo-Information (IJGI), (impact factor: 1.502), 5.10 (2016): 193. The first achievements in playing these games at super-human level were attained with methods that relied on and exploited domain expertise that was designed manually (e.g. 1503-1512, Aug 2015. Such advances would enrich the range of applicability of semi-autonomous systems to real-world tasks, most of which involve cooperation with one or more human partners. Submission site:https://cmt3.research.microsoft.com/ITCI2022, Murat Kocaoglu, Chair (Purdue University, mkocaoglu@purdue.edu), Negar Kiyavash (EPFL, negar.kiyavash@epfl.ch), Todd Coleman (UCSD, tpcoleman@ucsd.edu), Supplemental workshop site:https://sites.google.com/view/itci22. Computer Science and Engineering, INESC-ID, IST Ulisboa, Lisbon, Portugal currently at Sorbonne University, Paris, France silvia.tulli@gaips.inesc-id.pt), Prashan Madumal (Science and Information Systems, University of Melbourne, Parkville, Australia pmathugama@student.unimelb.edu.au), Mark T. Keane (School of Computer Science, University College Dublin, Dublin, Ireland mark.keane@ucd.ie), David W. Aha (Navy Center for Applied Research in AI, Naval Research Laboratory, Washington, DC, USA david.aha@nrl.navy.mil), Adam Johns (Drexel University, Philadelphia, PA USA), Tathagata Chakraborti (IBM Research AI, Cambridge, MA USA), Kim Baraka (VU University Amsterdam, Netherlands), Isaac Lage (Harvard University, Cambridge, MA USA), David Martens (University of Antwerp, Belgium), Mohamed Chetouani (Sorbonne Universit, Paris, France), Peter Flach (University of Bristol, United Kingdom), Kacper Sokol (University of Bristol, United Kingdom), Ofra Amir (Technion, Haifa, Israel), Dimitrios Letsios (Kings College London, London, United Kingdom), Supplemental workshop site:https://sites.google.com/view/eaai-ws-2022/topic. In our workshop, we specifically focus on the trustworthy issues in AI for healthcare, aiming to make clinical AI methods more reliable in real clinical settings and be willingly used by physicians. Tanmoy Chowdhury, Chen Ling, Xuchao Zhang, Xujiang Zhao, Guangji Bai, Jian Pei, Haifeng Chen, Liang Zhao. DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums. AI for infrastructure management and congestion. In recent years, we have seen examples of general approaches that learn to play these games via self-play reinforcement learning (RL), as first demonstrated in Backgammon. Key obstacles include lack of high-quality data, difficulty in embedding complex scientific and engineering knowledge in learning, and the need for high-dimensional design space exploration under constrained budgets. Contrast Feature Dependency Pattern Mining for Controlled Experiments with Application to Driving Behavior. The topics of interest include, but are not limited to: The papers will be presented in poster format and some will be selected for oral presentation. The submitted papers written in English must be in PDF format according to the AAAI camera ready style. https://doi.org/10.1007/s10707-019-00376-9. Liang Gou, Bosch Research (IEEE VIS liaison), Claudia Plant, University of Vienna (KDD liaison), Alvitta Ottley, Washington University, St. Louis, Junming Shao, University of Electronic Science and Technology of China, Visualization in Data Science (VDS at ACM KDD and IEEE VIS), Visualization in Data Science (VDS at ACM KDD and IEEE VIS). 2022. This has created a strong demand for transcript understanding. Thirty-First AAAI Conference on Artificial Intelligence, pp. 4701-4707, San Francisco, California, USA, Feb 2017. Neural Networks, (impact factor: 8.05), accepted. Welcome to the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2022), which will be held in Chengdu, China on May 16-19, 2022. This workshop will encourage researchers from interdisciplinary domains working on multi-modality and/or fact-checking to come together and work on multimodal (images, memes, videos etc.) Continuous V&V and predictability of AI safety properties, Runtime monitoring and (self-)adaptation of AI safety, Accountability, responsibility and liability of AI-based systems, Avoiding negative side effects in AI-based systems, Role and effectiveness of oversight: corrigibility and interruptibility, Loss of values and the catastrophic forgetting problem, Confidence, self-esteem and the distributional shift problem, Safety of AGI systems and the role of generality, Self-explanation, self-criticism and the transparency problem, Regulating AI-based systems: safety standards and certification, Human-in-the-loop and the scalable oversight problem, Experiences in AI-based safety-critical systems, including industrial processes, health, automotive systems, robotics, critical infrastructures, among others. Workshop URL:https://rail.fzu.edu.cn/info/1014/1064.htm, Prof. Chi-Hua ChenEmail: chihua0826@gmail.comPostal address: No.2, Xueyuan Rd., Fuzhou, Fujian, ChinaTelephone: +86-18359183858. A final tribute was paid on Saturday to former Coalition Avenir Qubec (CAQ) minister Nadine Girault, who died of lung cancer last month at age 63 . November 11-17, 2023. At least one author of each accepted submission must present the paper at the workshop.
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