Specifically, they are predictive of users' increased visiting to the platform in $5$ months among the group of users with the same visiting frequency to begin with. voluntary associations, friendship ties, organisational density) are neutral---they may or may not be effective mechanism for achieving intended effect. In addition, rich tutorial materials will be included and introduced to help the audience gain a systematic understanding by using our recently published book-Graph Neural Networks (GNN): Foundation, Frontiers, and Applications [12], which can easily be accessed at https://graph-neural-networks.github.io/index.html. In contrast, others focus on the private benefits derived from the web of social relationships in which individual actors find themselves. Deep Reinforcement Learning has been used widely for games, robotics etc. . Chan School of Public Health.He is also the Chief Health Economist for Microclinic Mahyar Arefi (2003) identifies consensus-building as a direct positive indicator of social capital. Our code is available at: https://github.com/RUCAIBox/JiuZhang. Half-precision floating-point support (float16) is provided as well, with full float16 compute on supporting GPUs and intermediate float16 storage provided on earlier architectures. This has resulted in recent works covering many aspects of epidemic forecasting. Find physics, physical science, engineering, and computing jobs at Physics Today Jobs. Apply to the latest jobs near you. ", "Who qualifies as a 'real expert' when it comes to coronavirus? 2010. [64], Varshney (2001) studied the correlation between the presence of interethnic networks (bridging) versus intra-ethnic ones (bonding) on ethnic violence in India. Specifically, PECOS eases complicated semantic indexing for organizing enormous output spaces, thereby efficiently training models and deriving predictions by magnitude orders on correlated output labels. Moreover, we propose a specially designed curriculum learning strategy for model training. Also see our online collection, 1,700 Free Online Courses from Top Universities. Remote. CMU is a global research university known for its world-class, interdisciplinary programs: arts, business, computing, engineering, humanities, policy and science. The recent COVID-19 pandemic has reinforced the importance of epidemic forecasting to equip decision makers in multiple domains, ranging from public health to economics. With the increasing reality of already cloud-resident datasets comes the need for distributed OD techniques. Finally, for online decision making (with bandits and reinforcement learning), we aim to resolve both the conceptual and practical learning, evaluation and deployment challenges by introducing powerful tools from robust optimization and optimal control. Such a set of theories became dominant in the last centuries, but many thinkers questioned the complicated relationship between modern society and the importance of old institutions, in particular family and traditional communities.[7]. Such efficiency enables massive exploration of chemical space given constrained computational resources. Specifically, we first transform the manually annotated borderline strokes of OB images into times series style shape representations, which are fed as input to a Generative Adversarial Network for augmenting positive pairs of rejoinable OBs for each OB fragment that does not have rejoinable counterparts. The software tools currently available arent sufficient for the database search operations described above. These cookies will be stored in your browser only with your consent. Black-box heterogeneous treatment effect (HTE) models are increasingly being used to create personalized policies that assign individuals to their optimal treatments. Learn about salary, employee reviews, interviews, benefits, and work-life balance AI Research Scientist, Neuromotor Interfaces, ML & Signal Processing. We are also home to two high-profile initiatives -Institute for Big Data Analytics and DeepSense- as well as our own innovation playground,ShiftKey Labs. However, as the field rapidly grows, it has been extremely challenging to gain a global perspective of the developments of GNNs. The optional GPU version has exactly the same interface, and there are bridges to translate between CPU and GPU indices. Knowledge, Skills and Abilities . Machine learning and data mining and visualization are integral parts of data science, and essential to enable sophisticated analysis of data. Data-centered solutions have specifically shown potential by leveraging non-traditional data sources as well as recent innovations in AI and machine learning. in both simulated datasets and industrial historical datasets. [112][113] With the assistance of software applications and web-based relationship-oriented systems such as LinkedIn, these kinds of organizations are expected to provide its members with a way to keep track of the number of their relationships, meetings designed to boost the strength of each relationship using group dynamics, executive retreats and networking events as well as training in how to reach out to higher circles of influential people. Adding to an IndexFlat just means copying them to the internal storage of the index, since there is no processing applied to the vectors. We also provide a theoretical analysis to prove the effectiveness of CausalMTA with sufficient ad journeys. So?' [11][55] For example, Sanger and Wales are historically cited or described in early news citations and press releases as co-founders. We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. We will invite researchers and practitioners from the related areas of AI, machine learning, data science, statistics, and many others to contribute to this workshop. To address these challenges on building recommender systems, NVIDIA developed an open source framework, called Merlin. Rather than technical descriptions of how individual ML models work, we emphasize how to best use models within an overall ML pipeline that takes in raw training data and outputs predictions for test data. They also contribute to the literature by measuring parent-child interaction by the indicators of how often parents and children discuss school-related activities. First, the huge inflow of new multimedia items creates billions of vectors. So, Mr. Wales, if imitation is a form of flattery, you should feel flattered. [94], On November 4, 2011, Wales delivered an hour-long address at The Sage Gateshead in the United Kingdom to launch the 2011 Free Thinking Festival on BBC Radio Three. Key Findings. Jimmy Donal Wales (born August 7, 1966), also known on Wikipedia by the pseudonym Jimbo, is an American-British Internet entrepreneur, webmaster, and former financial trader. Particularly notable is a recently renewed interest in solving partial differential equations using machine learning models, especially deep neural networks, as partial differential equations arise in many scientific problems of interest. [79], There is no widely held consensus on how to measure social capital, which has become a debate in itself. To this end, movement data from cell-phones is already used to augment epidemiological models. [8] On March 26, Alexis Madrigal, its author, re-assessed his piece and stated that "it was right in the particulars and wrong on the big picture. The problem of money laundering can be considered as a path-detection in the Graph. We employ a causal graph illuminating that duration is a confounding factor that concurrently affects video exposure and watch-time prediction---the first effect on video causes the bias issue and should be eliminated, while the second effect on watch time originates from video intrinsic characteristics and should be preserved. In this paper, we propose a reinforcement learning enhanced experts method. Much attention was paid to efficient tiling strategies and implementation of kernels used for approximate search. Dowley, Kathleen M., and Brian D. Silver. In recent years, graph neural networks have emerged as a prevalent paradigm of learning with structured data. Based on the organizers' expertise and communities, BIOKDD 2022 features 2 closely related themes "Biomedical Ontologies" and "Biological Data Visualization". 249, Yao et al (2022) Carbon neutrality vs. neutralit carbone: A comparative study on French and English users perceptions and social capital on Twitter, Frontiers in Environmental Sciences 10:969039, doi: 10.3389/fenvs.2022.969039, Hazleton V., and W. Kennan. The Fragile EarthWorkshop is a recurring event that gathers the research community to find and explore howdata science can measure and progress climate and social issues, following the framework of the United Nations Sustainable Development Goals (SDGs). In 1984, Anthony Giddens developed a theory in which he relates social structures and the actions that they produce. Deep Reinforcement Learning uses best of both Reinforcement Learning and Deep Learning for solving problems which cannot be addressed by them individually. Motivated by the limitations in existing works, we propose a novel framework named GraphGeo, which provides a complete processing methodology for street-level IP geolocation with the application of graph neural networks. Latin Text with Introduction, Study Questions, Commentary and English Translation, A First Course in Electrical and Computer Engineering, Artificial Intelligence: Foundations of Computational Agents, Bits, Signals, and Packets: An Introduction to Digital Communications and Networks. The tutorial combines an introduction of fundamental anomaly detection techniques with hands-on exercises. On January 15, 2001, with Larry Sanger and others, Wales launched Wikipedia, a free open-content encyclopedia that enjoyed rapid growth and popularity. They take into account of a detailed counting of family structure, not only with two biological parents or stepparent families, but also with types of single-parent families with each other (mother-only, father-only, never-married, and other). It analyzes spatial and temporal correlations using diffusion and temporal convolution networks, which are then fused to parameterize the probabilistic distributions of travel demand. In this tutorial, we introduce recent advances in pretrained text representations, as well as their applications to a wide range of text mining tasks. This work represents an important step towards a full characterization of user engagement in mobile health applications, which can significantly enhance the abilities of health workers and, ultimately, save lives. Apply to the latest jobs near you. This application necessitates efficient inquiry of relevant disease symptoms in order to make accurate diagnosis recommendations. Physics Today has listings for the latest assistant, associate, and full professor roles, plus scientist jobs in specialized disciplines like theoretical physics, astronomy, condensed matter, materials, applied physics, astrophysics, optics and lasers, computational physics, plasma physics, and others! A literature review", "A systematic review of the relationships between social capital and socioeconomic inequalities in health: a contribution to understanding the psychosocial pathway of health inequalities", "Is ethnic density associated with health in a context of social disadvantage? [26][28] It was Jimmy Wales, along with other people, who came up with the broader idea of an open-source, collaborative encyclopedia that would accept contributions from ordinary people. These results are significant, especially for resource-poor countries, where vaccines are less available, have lower efficacy, and are more slowly distributed. Faiss implements a dozen index types that are often compositions of other indices. We will focus on two main topics of responsible RSs: (1) developing reliable and trustworthy RS models and algorithms, to provide reliable recommendation results when facing a complex, uncertain and dynamic scenario; (2) assessing the social influence of RSs on human's recognition and behaviours and ensuring the influence is positive to the society. We validate Fed-LTD via large-scale trace-driven experiments with Didi GAIA dataset. The diverse advertiser demands (brand or instant effect) result in different selling (in bulk or via auction) and pricing (fixed unit price or various bids) patterns, which naturally raises the fusion allocation issue of breaking the two markets' barrier and selling out at the global highest price boosting the total revenue. The effectiveness, transferability, and efficiency of AutoShard make it desirable for production use. "[126] When asked about his view on the ECJ's "right to be forgotten" ruling, Wales replied: I think the decision will have no impact on people's right to privacy, because I don't regard truthful information in court records published by court order in a newspaper to be private information. As part of this work, we release two large-scale datasets consisting of 3.56 million and 6.01 million user behaviors with rich context and fashion information in millions of combo items. Senior Big Data Engineer average salary is $130,965, median salary is $135,000 with a salary range from $72,000 to $250,000.Senior Big Data Engineer salaries are collected from government agencies and Necessary cookies are absolutely essential for the website to function properly. Models trained on this imbalanced data face the risk of bias amplification, which misguides platforms to over-recommend videos with long duration but overlook the underlying user interests. Geographic and social isolation limit access to this resource. [120] He has rejected the notion that his role in promoting Wikipedia is altruistic, which he defines as "sacrificing your own values for others", and he states that the idea that "participating in a benevolent effort to share information is somehow destroying your own values makes no sense to me". It involves the effective functioning of social groups through interpersonal relationships, a shared sense of identity, a shared understanding, shared norms, shared values, trust, cooperation, and reciprocity. The characteristics of such data pose unique challenges to the adoption of deep learning in these applications, including modeling, training, and online serving, etc. We make comprehensive offline and online experiments to evaluate the proposed techniques, whose findings may provide useful insights for the future development of EBR systems. Unlike other standard NLP tasks, mathematical texts are difficult to understand, since they involve mathematical terminology, symbols and formulas in the problem statement. The mainstream RS ranking framework is composed of two parts: a Multi-Task Learning model (MTL) that predicts various user feedback, i.e., clicks, likes, sharings, and a Multi-Task Fusion model (MTF) that combines the multi-task outputs into one final ranking score with respect to user satisfaction. The similarity search functions that are available in packages like OpenCV are severely limited in terms of scalability, as are other similarity search libraries considering small data sets (for example, only 1 million vectors). [35], In January 2001, Sanger was introduced to the concept of a wiki by extreme programming enthusiast Ben Kovitz after explaining to Kovitz the slow pace of growth Nupedia endured as a result of its onerous submission process. There is a significant connection between leisure and democratic social capital. While Lale comes with hyperparameter specifications for 216 operators out-of-the-box, users can also add more operators of their own, and this tutorial covers how to do that. In accordance with the multi-year goal to continue fostering this community as a series of KDD workshops via timely topics, this year the workshop will focus on the transparency and human-centered AI in healthcare. In the first half of the 19th century, de Tocqueville had observations about American life that seemed to outline and define social capital. For a long time, graphs have been widely used for defining the structure of social and information networks. "[62] Although his formal designation is board member and chairman emeritus of the Wikimedia Foundation, Wales's social capital within the Wikipedia community has accorded him a status that has been characterized as benevolent dictator, constitutional monarch and spiritual leader. Experimental results demonstrate the effectiveness of our approach compared with a number of competitive baselines. 2011. [11] Wales was quoted by The Boston Globe as calling Sanger's statement "preposterous" in February 2006,[56] and called "the whole debate" "silly" in an April 2009 interview. Find out more about Nova Scotia's digital success story and get involved. Even though German society was, at the time, a "joining" society these groups were fragmented and their members did not use the skills they learned in their club associations to better their society but to, encourage their values across all cultures to provide a better society for people. Properties of these nodes and edges directly map to business problems in the financial world. To address gaps in knowledge and improve the livelihood of marginalized populations, we have established the Data-driven Humanitarian Mapping and Policymaking, an interdisciplinary initiative. Donors might not see a direct repayment, but, most commonly, they will be held by the society in greater honor. He is also the Chief Health Economist for Microclinic International, and co-founder of the World Health Network. Participants will then work on modeling with Merlin Models library, building the fundamental recommendation models such as MF and then transitioning to more complex deep learning-based models for candidate retrieval. Other authors, however, disagree about the positive correlation between social capital and microfinance, Kanak and Iiguni argue that formation of social capital is largely dependent on strategies implemented by Microfinance Institutions. In a collaboration between ML and human-computer interaction researchers, physicians, and data scientists, we develop GAM Changer, the first interactive system to help domain experts and data scientists easily and responsibly edit Generalized Additive Models (GAMs) and fix problematic patterns. [18] In a talk at SXSW in 2016, he recalled that he wrote the first words on Wikipedia: "Hello world", a phrase computer programmers often use to test new software. This provides the gold standard reference result list. The intra-CL enables more effective and balanced training inside the target domain via a graph augmentation, while the inter-CL builds different types of cross-domain interactions from user, taxonomy, and neighbor aspects. Thus, scalability, not just polynomial-time computability, should be elevated as the central complexity notion for characterizing efficient computation. Bourdieu thus points out that the wealthy and powerful use their "old boys network" or other social capital to maintain advantages for themselves, their social class, and their children. We provide the source code and documents at https://www.mindspore.cn/. [citation needed] Putnam also suggests that a root cause of the decline in social capital is women's entry the workforce, which could correlate with time restraints that inhibit civic organizational involvement like parent-teacher associations. To illustrate this, we assume that an individual wishes to better his place in society. Accelerating the search involves some pre-processing of the data set, an operation that we call indexing. Nowadays, sensing technologies and large-scale computing infrastructures have produced a variety of big data in urban spaces, e.g., human mobility, air quality, traffic patterns, and geographical data. Deep1B comes with a small collection of query images, and the ground-truth similarity search results are provided from a brute-force algorithm on these images. We have collected 220 million students' memory behavior logs with time-series features and built a memory model with Markov property. 2009. However, in reality, several hundred thousand orders are canceled per day in the Meituan meal delivery platform since they are not accepted by the crowd soucing drivers. To provide better experience and assist users in their activities, it is critical to mine certain information from this data. They are applied in various guises including anomaly detection, out-of-distribution example detection, adversarial example recognition and detection, curiosity-driven reinforcement learning, and open-set recognition and adaptation, all of which are of great interest to the SIGKDD community. 3 days ago. All forms of "capital" were, for Marx, possessed only by capitalists and he emphasized the basis of labour in capitalist society, as a class constituted by individuals obliged to sell their labour power, because they lacked sufficient capital, in any sense of the word, to do otherwise. In such a task, the matching effect between these two single items plays a crucial role, and greatly influences the users' preferences; however, it is usually neglected by previous approaches in CTR prediction. It provides data interfaces, common algorithms, and evaluation metrics for each direction. Nevertheless, both research areas are currently still rather separated and investigated by different communities rather independently. For practitioners who are applying DNNs into real-world problems, understanding the characteristics of different kinds of attacks will not only help them improve the robustness of their models, but also can help them have deeper insights into the working mechanism of DNNs. Given a query vector, return the list of database objects that are nearest to this vector in terms of Euclidean distance. In the second, he elaborated on his "constitutional monarch" designation, saying that, like Queen of the United Kingdom Elizabeth II, he has no real power. It is able to k-select input data in a single pass, operating at up to 55 percent of peak possible performance, as given by peak GPU memory bandwidth. Learning Differential Operators for Interpretable Time Series Modeling, ML4S: Learning Causal Skeleton from Vicinal Graphs, Non-stationary Time-aware Kernelized Attention for Temporal Event Prediction, CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation, Discovering Invariant and Changing Mechanisms from Data, Learning Models of Individual Behavior in Chess, Minimizing Congestion for Balanced Dominators, Extracting Relevant Information from User's Utterances in Conversational Search and Recommendation, Nonlinearity Encoding for Extrapolation of Neural Networks, Learning Fair Representation via Distributional Contrastive Disentanglement, Predicting Opinion Dynamics via Sociologically-Informed Neural Networks, FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy, MetaV: A Meta-Verifier Approach to Task-Agnostic Model Fingerprinting, Compute Like Humans: Interpretable Step-by-step Symbolic Computation with Deep Neural Network, Bilateral Dependency Optimization: Defending Against Model-inversion Attacks, Evaluating Knowledge Graph Accuracy Powered by Optimized Human-machine Collaboration, Rep2Vec: Repository Embedding via Heterogeneous Graph Adversarial Contrastive Learning, External Knowledge Infusion for Tabular Pre-training Models with Dual-adapters, Releasing Private Data for Numerical Queries, Importance Prioritized Policy Distillation, Synthesising Audio Adversarial Examples for Automatic Speech Recognition, p-Meta: Towards On-device Deep Model Adaptation, Fair and Interpretable Models for Survival Analysis, Graph-Flashback Network for Next Location Recommendation, SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs, DICE: Domain-attack Invariant Causal Learning for Improved Data Privacy Protection and Adversarial Robustness, Semi-supervised Drifted Stream Learning with Short Lookback, Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking, A Generalized Backward Compatibility Metric, Balancing Bias and Variance for Active Weakly Supervised Learning, On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification, Active Model Adaptation Under Unknown Shift, Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting, Multi-View Clustering for Open Knowledge Base Canonicalization, Deep Learning for Prognosis Using Task-fMRI: A Novel Architecture and Training Scheme, Pairwise Adversarial Training for Unsupervised Class-imbalanced Domain Adaptation, State Dependent Parallel Neural Hawkes Process for Limit Order Book Event Stream Prediction and Simulation, Robust and Informative Text Augmentation (RITA) via Constrained Worst-Case Transformations for Low-Resource Named Entity Recognition, GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks, Learning on Graphs with Out-of-Distribution Nodes, RGVisNet: A Hybrid Retrieval-Generation Neural Framework Towards Automatic Data Visualization Generation, Towards an Optimal Asymmetric Graph Structure for Robust Semi-supervised Node Classification, ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data, Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems, Knowledge Enhanced Search Result Diversification, Causal Attention for Interpretable and Generalizable Graph Classification, Demystify Hyperparameters for Stochastic Optimization with Transferable Representations, GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks, pureGAM: Learning an Inherently Pure Additive Model, Learning Optimal Priors for Task-Invariant Representations in Variational Autoencoders, Clustering with Fair-Center Representation: Parameterized Approximation Algorithms and Heuristics, Incremental Cognitive Diagnosis for Intelligent Education, Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertainty, Aligning Dual Disentangled User Representations from Ratings and Textual Content, Dense Feature Tracking of Atmospheric Winds with Deep Optical Flow, Towards Representation Alignment and Uniformity in Collaborative Filtering, Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction, A Model-Agnostic Approach to Differentially Private Topic Mining, Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation, Estimating Individualized Causal Effect with Confounded Instruments, Make Fairness More Fair: Fair Item Utility Estimation and Exposure Re-Distribution, Streaming Graph Neural Networks with Generative Replay, Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing, Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer, Task-Adaptive Few-shot Node Classification, Partial Label Learning with Discrimination Augmentation, Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning, Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage, Graph Neural Networks with Node-wise Architecture, Debiasing Learning for Membership Inference Attacks Against Recommender Systems, Invariant Preference Learning for General Debiasing in Recommendation, An Embedded Feature Selection Framework for Control, Comprehensive Fair Meta-learned Recommender System, SagDRE: Sequence-Aware Graph-Based Document-Level Relation Extraction with Adaptive Margin Loss, Disentangled Dynamic Heterogeneous Graph Learning for Opioid Overdose Prediction, Beyond Point Prediction: Capturing Zero-Inflated & Heavy-Tailed Spatiotemporal Data with Deep Extreme Mixture Models, Multi-fidelity Hierarchical Neural Processes, Domain Adaptation with Dynamic Open-Set Targets, Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction, CLARE: A Semi-supervised Community Detection Algorithm, Geometric Policy Iteration for Markov Decision Processes, Robust Tensor Graph Convolutional Networks via T-SVD based Graph Augmentation, Self-Supervised Hypergraph Transformer for Recommender Systems, Sample-Efficient Kernel Mean Estimator with Marginalized Corrupted Data, RetroGraph: Retrosynthetic Planning with Graph Search, Ultrahyperbolic Knowledge Graph Embeddings, End-to-End Semi-Supervised Ordinal Regression AUC Maximization with Convolutional Kernel Networks, MetaPTP: An Adaptive Meta-optimized Model for Personalized Spatial Trajectory Prediction, Towards a Native Quantum Paradigm for Graph Representation Learning: A Sampling-based Recurrent Embedding Approach, Solving the Batch Stochastic Bin Packing Problem in Cloud: A Chance-constrained Optimization Approach, On-Device Learning for Model Personalization with Large-Scale Cloud-Coordinated Domain Adaption, Enhancing Machine Learning Approaches for Graph Optimization Problems with Diversifying Graph Augmentation, HICF: Hyperbolic Informative Collaborative Filtering, Toward Real-life Dialogue State Tracking Involving Negative Feedback Utterances, Numerical Tuple Extraction from Tables with Pre-training, Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions, Reinforcement Subgraph Reasoning for Fake News Detection, Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation, TrajGAT: A Graph-based Long-term Dependency Modeling Approach for Trajectory Similarity Computation, Learning Classifiers under Delayed Feedback with a Time Window Assumption, Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting, LeapAttack: Hard-Label Adversarial Attack on Text via Gradient-Based Optimization, Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes, Nimble GNN Embedding with Tensor-Train Decomposition, Accurate Node Feature Estimation with Structured Variational Graph Autoencoder, Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream, ROLAND: Graph Learning Framework for Dynamic Graphs, Multiplex Heterogeneous Graph Convolutional Network, MDP2 Forest: A Constrained Continuous Multi-dimensional Policy Optimization Approach for Short-video Recommendation, Intrinsic-Motivated Sensor Management: Exploring with Physical Surprise, Dual Bidirectional Graph Convolutional Networks for Zero-shot Node Classification, M3Care: Learning with Missing Modalities in Multimodal Healthcare Data, Physics-infused Machine Learning for Crowd Simulation, Few-shot Heterogeneous Graph Learning via Cross-domain Knowledge Transfer, M-Mix: Generating Hard Negatives via Multi-sample Mixing for Contrastive Learning, Multi-Agent Graph Convolutional Reinforcement Learning for Dynamic Electric Vehicle Charging Pricing, MetroGAN: Simulating Urban Morphology with Generative Adversarial Network, Model Degradation Hinders Deep Graph Neural Networks, Counteracting User Attention Bias in Music Streaming Recommendation via Reward Modification, Improving Social Network Embedding via New Second-Order Continuous Graph Neural Networks, COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning, Unsupervised Key Event Detection from Massive Text Corpora, FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients, Adaptive Learning for Weakly Labeled Streams, Adaptive Fairness-Aware Online Meta-Learning for Changing Environments, MT-FlowFormer: A Semi-Supervised Flow Transformer for Encrypted Traffic Classification, Contrastive Learning with Complex Heterogeneity, Instant Graph Neural Networks for Dynamic Graphs, KRATOS: Context-Aware Cell Type Classification and Interpretation using Joint Dimensionality Reduction and Clustering, Unified 2D and 3D Pre-Training of Molecular Representations, How does Heterophily Impact the Robustness of Graph Neural Networks?
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