Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence最新文献_第4页

C-NTPP: Learning Cluster-Aware Neural Temporal Point Process C-NTPP:学习簇感知神经时间点过程

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i6.25897

Fangyu Ding, Junchi Yan, Haiyang Wang

引用次数: 0

PEN: Prediction-Explanation Network to Forecast Stock Price Movement with Better Explainability PEN:预测-解释网络以更好的可解释性预测股价走势

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i4.25648

Shuqi Li, Weiheng Liao, Yuhan Chen, Rui Yan

{"title":"PEN: Prediction-Explanation Network to Forecast Stock Price Movement with Better Explainability","authors":"Shuqi Li, Weiheng Liao, Yuhan Chen, Rui Yan","doi":"10.1609/aaai.v37i4.25648","DOIUrl":"https://doi.org/10.1609/aaai.v37i4.25648","url":null,"abstract":"Nowadays explainability in stock price movement prediction is attracting increasing attention in banks, hedge funds and asset managers, primarily due to audit or regulatory reasons. Text data such as financial news and social media posts can be part of the reasons for stock price movement. To this end, we propose a novel framework of Prediction-Explanation Network (PEN) jointly modeling text streams and price streams with alignment. The key component of the PEN model is an shared representation learning module that learns which texts are possibly associated with the stock price movement by modeling the interaction between the text data and stock price data with a salient vector characterizing their correlation. In this way, the PEN model is able to predict the stock price movement by identifying and utilizing abundant messages while on the other hand, the selected text messages also explain the stock price movement. Experiments on real-world datasets demonstrate that we are able to kill two birds with one stone: in terms of accuracy, the proposed PEN model outperforms the state-of-art baseline; on explainability, the PEN model are demonstrated to be far superior to attention mechanism, capable of picking out the crucial texts with a very high confidence.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"43 5 1","pages":"5187-5194"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84198529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Second-Order Quantified Boolean Logic 二阶量化布尔逻辑

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i4.25515

J. H. Jiang

引用次数: 1

MaskBooster: End-to-End Self-Training for Sparsely Supervised Instance Segmentation MaskBooster:稀疏监督实例分割的端到端自我训练

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i3.25481

Shida Zheng, Chenshu Chen, Xi Yang, Wenming Tan

{"title":"MaskBooster: End-to-End Self-Training for Sparsely Supervised Instance Segmentation","authors":"Shida Zheng, Chenshu Chen, Xi Yang, Wenming Tan","doi":"10.1609/aaai.v37i3.25481","DOIUrl":"https://doi.org/10.1609/aaai.v37i3.25481","url":null,"abstract":"The present paper introduces sparsely supervised instance segmentation, with the datasets being fully annotated bounding boxes and sparsely annotated masks. A direct solution to this task is self-training, which is not fully explored for instance segmentation yet. In this paper, we propose MaskBooster for sparsely supervised instance segmentation (SpSIS) with comprehensive usage of pseudo masks. MaskBooster is featured with (1) dynamic and progressive pseudo masks from an online updating teacher model, (2) refining binary pseudo masks with the help of bounding box prior, (3) learning inter-class prediction distribution via knowledge distillation for soft pseudo masks. As an end-to-end and universal self-training framework, MaskBooster can empower fully supervised algorithms and boost their segmentation performance on SpSIS. Abundant experiments are conducted on COCO and BDD100K datasets and validate the effectiveness of MaskBooster. Specifically, on different COCO protocols and BDD100K, we surpass sparsely supervised baseline by a large margin for both Mask RCNN and ShapeProp. MaskBooster on SpSIS also outperforms weakly and semi-supervised instance segmentation state-of-the-art on the datasets with similar annotation budgets.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"10 1","pages":"3696-3704"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84328246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Embeddings of Logical Variables for Query Answering over Incomplete Knowledge Graphs 不完全知识图查询回答中逻辑变量的高效嵌入

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i4.25588

Dingmin Wang, Yeyuan Chen, B. C. Grau

引用次数: 2

Fast Fluid Simulation via Dynamic Multi-Scale Gridding 基于动态多尺度网格的快速流体仿真

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i2.25255

Jinxian Liu, Ye Chen, Bingbing Ni, Wei Ren, Zhenbo Yu, Xiaoyang Huang

{"title":"Fast Fluid Simulation via Dynamic Multi-Scale Gridding","authors":"Jinxian Liu, Ye Chen, Bingbing Ni, Wei Ren, Zhenbo Yu, Xiaoyang Huang","doi":"10.1609/aaai.v37i2.25255","DOIUrl":"https://doi.org/10.1609/aaai.v37i2.25255","url":null,"abstract":"Recent works on learning-based frameworks for Lagrangian (i.e., particle-based) fluid simulation, though bypassing iterative pressure projection via efficient convolution operators, are still time-consuming due to excessive amount of particles. To address this challenge, we propose a dynamic multi-scale gridding method to reduce the magnitude of elements that have to be processed, by observing repeated particle motion patterns within certain consistent regions. Specifically, we hierarchically generate multi-scale micelles in Euclidean space by grouping particles that share similar motion patterns/characteristics based on super-light motion and scale estimation modules. With little internal motion variation, each micelle is modeled as a single rigid body with convolution only applied to a single representative particle. In addition, a distance-based interpolation is conducted to propagate relative motion message among micelles. With our efficient design, the network produces high visual fidelity fluid simulations with the inference time to be only 4.24 ms/frame (with 6K fluid particles), hence enables real-time human-computer interaction and animation. Experimental results on multiple datasets show that our work achieves great simulation acceleration with negligible prediction error increase.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"63 1","pages":"1675-1682"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85630739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network 面向异构图神经网络的细粒度可解释性

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i7.26040

Tong Li, Jiale Deng, Yanyan Shen, Luyu Qiu, Hu Yongxiang, Caleb Chen Cao

{"title":"Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network","authors":"Tong Li, Jiale Deng, Yanyan Shen, Luyu Qiu, Hu Yongxiang, Caleb Chen Cao","doi":"10.1609/aaai.v37i7.26040","DOIUrl":"https://doi.org/10.1609/aaai.v37i7.26040","url":null,"abstract":"Heterogeneous graph neural networks (HGNs) are prominent approaches to node classification tasks on heterogeneous graphs. Despite the superior performance, insights about the predictions made from HGNs are obscure to humans. Existing explainability techniques are mainly proposed for GNNs on homogeneous graphs. They focus on highlighting salient graph objects to the predictions whereas the problem of how these objects affect the predictions remains unsolved. Given heterogeneous graphs with complex structures and rich semantics, it is imperative that salient objects can be accompanied with their influence paths to the predictions, unveiling the reasoning process of HGNs. In this paper, we develop xPath, a new framework that provides fine-grained explanations for black-box HGNs specifying a cause node with its influence path to the target node. In xPath, we differentiate the influence of a node on the prediction w.r.t. every individual influence path, and measure the influence by perturbing graph structure via a novel graph rewiring algorithm. Furthermore, we introduce a greedy search algorithm to find the most influential fine-grained explanations efficiently. Empirical results on various HGNs and heterogeneous graphs show that xPath yields faithful explanations efficiently, outperforming the adaptations of advanced GNN explanation approaches.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"16 1","pages":"8640-8647"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85875455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Social Relation Reasoning Based on Triangular Constraints 基于三角约束的社会关系推理

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i1.25151

Yunfei Guo, Fei Yin, Wei Feng, Xudong Yan, Tao Xue, Shuqi Mei, Chengxiao Liu

{"title":"Social Relation Reasoning Based on Triangular Constraints","authors":"Yunfei Guo, Fei Yin, Wei Feng, Xudong Yan, Tao Xue, Shuqi Mei, Chengxiao Liu","doi":"10.1609/aaai.v37i1.25151","DOIUrl":"https://doi.org/10.1609/aaai.v37i1.25151","url":null,"abstract":"Social networks are essentially in a graph structure where persons act as nodes and the edges connecting nodes denote social relations. The prediction of social relations, therefore, relies on the context in graphs to model the higher-order constraints among relations, which has not been exploited sufficiently by previous works, however. In this paper, we formulate the paradigm of the higher-order constraints in social relations into triangular relational closed-loop structures, i.e., triangular constraints, and further introduce the triangular reasoning graph attention network (TRGAT). Our TRGAT employs the attention mechanism to aggregate features with triangular constraints in the graph, thereby exploiting the higher-order context to reason social relations iteratively. Besides, to acquire better feature representations of persons, we introduce node contrastive learning into relation reasoning. Experimental results show that our method outperforms existing approaches significantly, with higher accuracy and better consistency in generating social relation graphs.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"18 1","pages":"737-745"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85945175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

End-to-End Pipeline for Trigger Detection on Hit and Track Graphs 端到端管道触发检测命中和轨迹图

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i13.26870

Tingting Xuan, Yimin Zhu, Giorgian Borca-Tasciuc, Ming Liu, Yu Sun, Cameron Dean, Y. C. Morales, Z. Shi, Dantong Yu

{"title":"End-to-End Pipeline for Trigger Detection on Hit and Track Graphs","authors":"Tingting Xuan, Yimin Zhu, Giorgian Borca-Tasciuc, Ming Liu, Yu Sun, Cameron Dean, Y. C. Morales, Z. Shi, Dantong Yu","doi":"10.1609/aaai.v37i13.26870","DOIUrl":"https://doi.org/10.1609/aaai.v37i13.26870","url":null,"abstract":"There has been a surge of interest in applying deep learning in particle and nuclear physics to replace labor-intensive offline data analysis with automated online machine learning tasks. This paper details a novel AI-enabled triggering solution for physics experiments in Relativistic Heavy Ion Collider and future Electron-Ion Collider. The triggering system consists of a comprehensive end-to-end pipeline based on Graph Neural Networks that classifies trigger events versus background events, makes online decisions to retain signal data, and enables efficient data acquisition. The triggering system first starts with the coordinates of pixel hits lit up by passing particles in the detector, applies three stages of event processing (hits clustering, track reconstruction, and trigger detection), and labels all processed events with the binary tag of trigger versus background events. By switching among different objective functions, we train the Graph Neural Networks in the pipeline to solve multiple tasks: the edge-level track reconstruction problem, the edge-level track adjacency matrix prediction, and the graph-level trigger detection problem. We propose a novel method to treat the events as track-graphs instead of hit-graphs. This method focuses on intertrack relations and is driven by underlying physics processing. As a result, it attains a solid performance (around 72% accuracy) for trigger detection and outperforms the baseline method using hit-graphs by 2% higher accuracy.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"13 1","pages":"15752-15758"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85974712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack against Federated Learning 用Cerberus中毒:对联邦学习的秘密和串通后门攻击

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i7.26083

Xiaoting Lyu, Yufei Han, Wen Wang, Jingkai Liu, Bin Wang, Jiqiang Liu, Xiangliang Zhang

{"title":"Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack against Federated Learning","authors":"Xiaoting Lyu, Yufei Han, Wen Wang, Jingkai Liu, Bin Wang, Jiqiang Liu, Xiangliang Zhang","doi":"10.1609/aaai.v37i7.26083","DOIUrl":"https://doi.org/10.1609/aaai.v37i7.26083","url":null,"abstract":"Are Federated Learning (FL) systems free from backdoor poisoning with the arsenal of various defense strategies deployed? This is an intriguing problem with significant practical implications regarding the utility of FL services. Despite the recent flourish of poisoning-resilient FL methods, our study shows that carefully tuning the collusion between malicious participants can minimize the trigger-induced bias of the poisoned local model from the poison-free one, which plays the key role in delivering stealthy backdoor attacks and circumventing a wide spectrum of state-of-the-art defense methods in FL. In our work, we instantiate the attack strategy by proposing a distributed backdoor attack method, namely Cerberus Poisoning (CerP). It jointly tunes the backdoor trigger and controls the poisoned model changes on each malicious participant to achieve a stealthy yet successful backdoor attack against a wide spectrum of defensive mechanisms of federated learning techniques. Our extensive study on 3 large-scale benchmark datasets and 13 mainstream defensive mechanisms confirms that Cerberus Poisoning raises a significantly severe threat to the integrity and security of federated learning practices, regardless of the flourish of robust Federated Learning methods.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"31 1","pages":"9020-9028"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76604395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6