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www 2020 accepted papers

Understanding security mistakes developers make: Qualitative analysis from Build It, Break It, Fix It. ACCEPTED PAPERS. Time Series Data Mining Can Increase the Productivity of Poultry Farms, Forecasting the Evolution of Hydropower Generation, Fraud Transactions Detection via Behavior Tree with Local Intention Calibration, Game Action Modeling for Fine Grained Analyses of Player Behavior in Multi-player Card Games (Rummy as case study), Gemini: A novel and universal heterogeneous graph information fusing framework for online recommendations, General-Purpose User Embeddings based on Mobile App Usage, Grale: Designing Networks for Graph Learning, GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce, HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival, Hubble: an Industrial System for Audience Expansion in Mobile Marketing, Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data, Hypergraph Convolutional Recurrent Neural Network, Identifying Homeless Youth At-Risk of Substance Use Disorder: Data-Driven Insights for Policymakers, Improving Deep Learning For Airbnb Search, Improving Movement Predictions of Traffic Actors in Bird's-Eye View Models using GANs and Differentiable Trajectory Rasterizatio, Improving Recommendation Quality in Google Drive, Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning, Interleaved Sequence RNNs for Fraud Detection, Jointly Learning to Recommend and Advertise, Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps, Large-Scale Training System for 100-Million Classification at Alibaba, Learning instrument invariant characteristics for generating high-resolution global coral reef maps, Learning to Cluster Documents into Workspaces Using Large Scale Activity Logs, Learning to Generate Personalized Query Auto-Completions via a Multi-View Multi-Task Attentive Approach, Learning to score economic development from satellite imagery, Learning with Limited Labels via Momentum Damped Differentially Weighted Training, LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition, LUMOS: A library for diagnosing metric regressions for web-scale applications, M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems, Map Generation from Large Scale Incomplete and Inaccurate Data Labels, Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective, Meta-Learning for Query Conceptualization at Web Scale, Mining Implicit Relevance Feedback from User Behavior for Web Question Answering, Molecular Inverse-Design Platform for Material Industries, Multi Modal Deep Learning Based Crop Classification Using Multispectral and Multitemporal Satellite Imagery, Multi-objective Optimization for Guaranteed Delivery in Video Service Platform, MultiSage: Empowering GCN with Contextualized Multi-Embeddings on Web-Scale Multipartite Networks, Multitask Mixture of Sequential Experts for User Activity Streams, Neural Input Search for Large Scale Recommendation Models, Octet: Online Catalog Taxonomy Enrichment with Self-Supervision, OptMatch: Optimized Matchmaking via Modeling the High-Order Interactions on the Arena, Order Fulfillment Cycle Time Estimation for On-Demand Food Delivery, Personalized Image Retrieval with Sparse Graph Representation Learning, Personalized Prefix Embedding for POI Auto-Completion in the Search Engine of Baidu Maps, Pest management in cotton farms: an AI-system case study from the global South, PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest, Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine, Predicting Individual Treatment Effects of Large-scale Team Competitions in a Ride-sharing Economy, Prediction of Hourly Earnings and Completion Time on a Crowdsourcing Platform, Price Investment using Prescriptive Analytics and Optimization in Retail, Privileged Features Distillation at Taobao Recommendations, Reconstruction and Decomposition of High-Dimensional Landscapes via Unsupervised Learning, Salience and Market-aware Skill Extraction for Job Targeting, Scaling Graph Neural Networks with Approximate PageRank, Shop The Look: Building a Large Scale Visual Shopping System at Pinterest, SimClusters: Community-Based Representations for Heterogeneous Recommendations at Twitter, Sub-Matrix Factorization for Real-Time Vote Prediction, Taming Pretrained Transformers for eXtreme Multi-label Text Classification, Temporal-Contextual Recommendation in Real-Time, TIES: Temporal Interaction Embeddings For Enhancing Social Media Integrity At Facebook, TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding, To Tune or Not to Tune? FOCS 2020. Research Track A Simple Method for Inducing Class Taxonomies in Knowledge Graphs. At most 10% of the accepted papers of POPL 2020 will be designated as Distinguished Papers. Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining. Long Papers; Short Papers; System Demonstrations; Student Research Workshop; Note that the titles/authors may change and papers may be withdrawn. Tutorials are Friday Nov 13!! ACM CCS 2020 - November 9-13, 2020. Node–attributed Spatial Graph Partitioning Daniel Bereznyi (Florida Atlantic University), Ahmad Qutbuddin (Florida Atlantic University), Younggu Her (University of Florida), Kwangsoo Yang (Florida Atlantic University) A Ubiquitous and Accurate Floor Estimation System Using Deep … Requirements of Challenge based Learning for Experiential Learning Spaces, an Industrial Engineering application case: ... 11 December 2020; Twitter TALE20201. Displays the CVPR 2020 Accepted Conference Papers in a way that they are easy to parse. Texas. List of Accepted Papers in IEEE INFOCOM 2020 Main Conference (How Much) Does a Private WAN Improve Cloud Performance? Rebuttal. ; The Program for the FOCS 2020 conference has been posted! @timnitGebru from @google will tell how data collection practices from #archives can serve #MachineLearning PaperID: PaperTitle: 295-1548: 3D CNN Acceleration on FPGA using Hardware-Aware Pruning: 295-1747: A 90nm 103.14 TOPS-W Binary-Weight Spiking Neural Network CMOS ASIC for … November 2-5, 2020 VIRTUAL CONFERENCE. Graph embedding techniques allow to learn high-quality feature vectors from graph structures and are useful in a variety of tasks, from node classification to clustering. Long Papers. Accepted Papers This information is not yet available. Main program Affiliated events Rump session Invited talks Call for papers Paper submission Accepted papers. Home; About ICCAD. Great to get a glimpse into the lives of the attendees, even so far from each other. SIAM fosters the development of applied mathematical and computational methodologies needed in various application areas. Of the 8,186 papers reviewed this year, only 1,903 papers were accepted (20.1%); 20 of those papers were co-authored by Amii researchers. Format of Accepted Papers. Please enter the word you see in the image below: Applied Data Science Track Program Committee, A Block Decomposition Algorithm for Sparse Optimization, A causal look at statistical definitions of discrimination, A Data Driven Graph Generative Model for Temporal Interaction Networks, A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks, A Geometric Approach to Predicting Bounds of Downstream Model Performance, A Non-Iterative Quantile Change Detection Method in Mixture Model with Heavy-Tailed Components, A Novel Deep Learning Model by Stacking Conditional Restricted Boltzmann Machine and Deep Neural Network, Adaptive Graph Encoder for Attributed Graph Embedding, Adversarial Infidelity Learning for Model Interpretation, AdvMind: Inferring Adversary Intent of Black-Box Attacks, Algorithmic Aspects of Temporal Betweenness, Algorithmic Decision Making with Conditional Fairness, Aligning Superhuman AI with Human Behavior: Chess as a Model System, ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization, AM-GCN: Adaptive Multi-channel Graph Convolutional Networks, An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph, An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks, ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction, Attackability Characterization of Adversarial Evasion Attack on Discrete Data, Attention and Memory-Augmented Networks for Dual-View Sequential Learning, Attentional Multi-graph Convolutional Network for Regional Economy Prediction with Open Migration Data, AutoGrow: Automatic Layer Growing in Deep Convolutional Networks, AutoML Pipeline Selection: Efficiently Navigating the Combinatorial Space, AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks, AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction, Average Sensitivity of Spectral Clustering, BLOB: A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals, Block Model Guided Unsupervised Feature Selection, BOND: Bert-Assisted Open-Domain Named Entity Recognition with Distant Supervision, CAST: A Correlation-based Adaptive Spectral Clustering Algorithm on Multi-scale Data, Catalysis Clustering With GAN By Incorporating Domain Knowledge, Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations, CICLAD: A Fast and Memory-efficient Closed Itemset Miner for Streams, Combinatorial Black-Box Optimization with Expert Advice, Competitive Analysis for Points of Interest, COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching, Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems, Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks, Context-to-Session Matching: Utilizing Whole Session for Response Selection in Information-Seeking Dialogue Systems, CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring, Correlation Networks for Extreme Multi-label Text Classification, Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions, Curb-GAN: Conditional Urban Traffic Estimation through Spatio-Temporal Generative Adversarial Networks, CurvaNet: Geometric Deep Learning based on Multi-scale Directional Curvature for 3D Shape Analysis, Data Compression as a Comprehensive Framework for Graph Drawing and Representation Learning, Deep Exogenous and Endogenous Influence Combination for Social Chatter Intensity Prediction, Deep Learning of High-Order Interactions for Protein Interface Prediction, Deep State-Space Generative Model For Correlated Time-to-Event Predictions, DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering, DeepSinger: Singing Voice Synthesis with Data Mined From the Web, DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation, Discovering Approximate Functional Dependencies using Smoothed Mutual Information, Discovering Functional Dependencies from Mixed-Type Data, Discovering Succinct Pattern Sets Expressing Co-Occurrence and Mutual Exclusivity, Disentangled Self-Supervision in Sequential Recommenders, Dual Channel Hypergraph Collaborative Filtering, Dynamic Knowledge Graph based Multi-Event Forecasting, Edge-consensus Learning: Deep Learning on P2P Networks with Nonhomogeneous Data, Efficient Algorithm for the b-Matching Graph, Enterprise Cooperation and Competition Analysis with Sign-Oriented Preference Network, Estimating Properties of Social Networks via Random Walk considering Private Nodes, Estimating the Percolation Centrality of Large Networks through Pseudo-dimension Theory, Evaluating Conversational Recommender Systems via User Simulation, Evaluating Fairness using Permutation Tests, Fast RobustSTL: Efficient and Robust Seasonal-Trend Decomposition for Time Series with Complex Patterns, Feature-Induced Manifold Disambiguation for Multi-View Partial Multi-label Learning, FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems, Finding Effective Geo-social Group for Impromptu Activities with Diverse Demands, FreeDOM: A Transferable Neural Architecture for Structured Information Extraction on Web Documents, GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training, Generic Outlier Detection in Multi-Armed Bandit, Geography-Aware Sequential Location Recommendation, GHashing: Semantic Graph Hashing for Approximate Similarity Search in Graph Databases, GPT-GNN: Generative Pre-Training of Graph Neural Networks, GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model, Grammatically Recognizing Images with Tree Convolution, Graph Attention Networks over Edge Content-Based Channels, Graph Structure Learning for Robust Graph Neural Networks, Grounding Visual Concepts for Multimedia Event Detection and Multimedia Event Captioning in Zero-shot Setting, Handling Information Loss of Graph Neural Networks for Session-based Recommendation, Heidegger: Interpretable Temporal Causal Discovery, HGCN: A Heterogeneous Graph Convolutional Network-Based Deep Learning Model Toward Collective Classification, HGMF: Heterogeneous Graph-based Fusion for Multimodal Data with Incompleteness, Hierarchical Attention Propagation for Healthcare Representation Learning, Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding, High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder, Higher-order Clustering in Complex Heterogeneous Networks, HiTANet: Hierarchical Time-Aware Attention Networks for Risk Prediction on Electronic Health Records, HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units, HOPS: Probabilistic Subtree Mining for Small and Large Graphs, How to count triangles, without seeing the whole graph, Identifying Sepsis Subphenotypes via Time-Aware Multi-Modal Auto-Encoder, Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion, Imputing Various Incomplete Attributes via Distance Likelihood Maximization, In and Out: Optimizing Overall Interaction in Probabilistic Graphs under Clustering Constraints, Incremental Mobile User Profiling: Reinforcement Learning with Spatial Knowledge Graph for Modeling Event Streams, InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity, InFoRM: Individual Fairness on Graph Mining, INPREM: An Interpretable and Trustworthy Predictive Model for Healthcare, Interactive Path Reasoning on Graph for Conversational Recommendation, Interpretability is a Kind of Safety: An Interpreter-based Ensemble for Adversary Defense, Interpretable Deep Graph Generation with Node-edge Co-disentanglement, Isolation Distributional Kernel: A new tool for kernel based anomaly detection, Joint Policy-Value Learning for Recommendation, Kernel Assisted Learning for Personalized Dose Finding, Laplacian Change Point Detection for Dynamic Graphs, LayoutLM: Pre-training of Text and Layout for Document Image Understanding, Learning Effective Road Network Representation with Hierarchical Graph Neural Networks, Learning Opinion Dynamics From Social Traces, Learning Stable Graphs from Heterogeneous Confounded Environments, Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach, Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling, Leveraging Model Inherent Variable Importance for Stable Online Feature Selection, List-wise Fairness Criterion for Point Processes, Local Community Detection in Multiple Networks, Local Motif Clustering on Time-Evolving Graphs, LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values, Malicious Attacks against Deep Reinforcement Learning Interpretations, MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation, Matrix Profile XXI: A Geometric Approach to Time Series Chains Improves Robustness, MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining, Measuring Model Complexity of Neural Networks with Curve Activation Functions, Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation, Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks, Minimizing Localized Ratio Cut Objectives in Hypergraphs, Mining large quasi-cliques with quality guarantees from vertex neighborhoods, Mining Persistent Activity in Continually Evolving Networks, MinSearch: An Efficient Algorithm for Similarity Search under Edit Distance, Missing Value Imputation for Mixed Data via Gaussian Copula, MoFlow: An Invertible Flow Model for Generating Molecular Graphs, Multi-class Data Description for Out-of-distribution Detection, Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction, Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data, MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals, Multimodal Learning with Incomplete Modalities by Knowledge Distillation, NetTrans: Neural Cross-Network Transformation, NodeAug: Semi-Supervised Node Classification with Data Augmentation, Non-Linear Mining of Social Activities in Tensor Streams, Off-policy Bandits with Deficient Support, On Sampled Metrics for Item Recommendation, On Sampling Top-K Recommendation Evaluation, Parallel DNN Inference Framework Leveraging a Compact RISC-V ISA-based Multi-core System, Parameterized Correlation Clustering in Hypergraphs and Bipartite Graphs, Partial Multi-Label Learning via Probabilistic Graph Matching Mechanism, Personalized PageRank to a Target Node, Revisited, PolicyGNN: Aggregation Optimization for Graph Neural Networks, Predicting Temporal Sets with Deep Neural Networks, Prediction and Profiling of Audience Competition for Online Television Series, Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction, Prioritized Restreaming Algorithms for Balanced Graph Partitioning, Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation, RayS: A Ray Searching Method for Hard-label Adversarial Attack, Re-identification Attack to Privacy-Preserving Data Analysis with Noisy Sample-Mean, REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs, Reciptor: An Effective Pretrained Model for Recipe Representation Learning, RECORD: Resource Constrained Semi-Supervised Learning under Distribution Shift, Recurrent Halting Chain for Early Multi-label Classification, Recurrent Networks for Guided Multi-Attention Classification, Redundancy-Free Computation for Graph Neural Networks, Representing Temporal Attributes for Schema Matching, Residual Correlation in Graph Neural Network Regression, Rethinking Pruning for Accelerating Deep Inference At the Edge, Retrospective Loss: Looking Back to Improve Training of Deep Neural Networks, Rich Information is Affordable: A Systematic Performance Analysis of Second-order Optimization Using K-FAC, Robust Spammer Detection by Nash Reinforcement Learning, Scaling choice models of relational social data, SCE: Scalable Newtork Embedding from Sparsest Cut, SEAL: Learning Heuristics for Community Detection with Generative Adversarial Networks, Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation, Semi-Supervised Multi-Label Learning from Crowds via Deep Sequential Generative Model, Sliding Sketches: A Framework using Time Zones for Data Stream Processing in Sliding Windows, Spectrum-Guided Adversarial Disparity Learning, SSumM: Sparse Summarization of Massive Graphs, ST-SiameseNet: Spatio-Temporal Siamese Networks for Human Mobility Signature Identification, Stable Learning via Differentiated Variable Decorrelation, Statistically Significant Pattern Mining with Ordinal Utility, STEAM: Self-Supervised Taxonomy Expansion via Path-Based Multi-View Co-Training, Structural Patterns and Generative Models of Real-world Hypergraphs, TAdaNet: Task-Adaptive Network for Graph-Enriched Meta-Learning, Targeted Data-driven Regularization for Out-of-Distribution Generalization, The NodeHopper: Enabling low latency ranking with constraints via a fast dual solver, The Spectral Zoo of Networks: Embedding and Visualizing Networks with Spectral Moments, Tight Sensitivity Bounds For Smaller Coresets, TinyGNN: Learning Efficient Graph Neural Networks, TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework for Deep Learning with Anonymized Intermediate Representations, Towards Automated Neural Interaction Discovering for Click-Through Rate Prediction, Towards Fair Truth Discovery from Biased Crowdsourced Answers, Towards physics-informed deep learning for turbulent flow prediction, TranSlider: Transfer Ensemble Learning from Exploitation to Exploration, Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes, Truth Discovery against Strategic Sybil Attack in Crowdsourcing, Ultrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping, Understanding Negative Sampling in Graph Representation Learning, Unsupervised Differentiable Multi-aspect Network Embedding, Unsupervised Paraphrasing via Deep Reinforcement Learning, Vamsa: Automated Provenance Tracking in Data Science Scripts, Voronoi Graph Traversal in High Dimensions with Applications to Topological Data Analysis and Piecewise Linear Interpolation, Vulnerability vs. There were 332 Long Papers and 115 Short Papers accepted to Findings of ACL: EMNLP 2020. Please also keep in mind 4 mandatory steps for COMNETSAT 2020 for author: 1. Thank you for your patience. Full Paper. Check our full list of accepted papers at CIKM 2020, This is it, #cikm2020 is officially closed! NDSS 2020 … Official notification with all details will be sent out by February 28, 2020. The conference profiles research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects. August 17-21 2020 Virtual Home; Technical Program. Accepted Papers . Asiacrypt 2020 . Accepted Papers Full Research Papers. For the final titles/authors, please refer to the proceedings on the anthology when they are out. @nuigalway #Interpretability December 7-11 2020 Virtual Home; Technical Program. 2020 International Conference On Computer Aided Design. Prepublication versions of the accepted papers from the summer submission deadline are available below. @DSIatNUIG CSoNet 2020 December 11-13, 2020 – Dallas. CONTACT US. 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