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Record Nr. |
UNISA996500066103316 |
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Titolo |
PRICAI 2022 . Part III : trends in artificial intelligence : 19th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2022, Shanghai, China, November 10-13, 2022, proceedings / / Sankalp Khanna [and three others] |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2022] |
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©2022 |
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ISBN |
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Descrizione fisica |
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1 online resource (667 pages) |
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Collana |
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Lecture Notes in Computer Science |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Artificial intelligence - Design and construction |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Intro -- Preface -- Organization -- Contents - Part III -- Recommender System -- Mixture of Graph Enhanced Expert Networks for Multi-task Recommendation -- 1 Introduction -- 2 The Proposed Method -- 2.1 Deep Interaction Context Exploitation with Multi-channel Graph Neural Network -- 2.2 Graph Enhanced Expert Network -- 2.3 Model Learning -- 2.4 Discussion -- 3 Experiments -- 3.1 Performance Comparison (RQ1) -- 3.2 Effect of the MGNN Module -- 3.3 Study of MoGENet -- 4 Conclusion -- References -- MF-TagRec: Multi-feature Fused Tag Recommendation for GitHub -- 1 Introduction -- 2 Related Work -- 2.1 Tag Recommendation -- 2.2 Tag Recommendation in Open-Source Communities -- 3 Method -- 3.1 Problem Formulation -- 3.2 Overview of MF-TagRec -- 3.3 CNN for Tag Prediction -- 3.4 Network Training Process -- 4 Experiments -- 4.1 Experimental Dataset -- 4.2 Evaluation Metrics -- 4.3 Experimental Settings -- 4.4 Experimental Results -- 5 Conclusions and Future Work -- References -- Co-contrastive Learning for Multi-behavior Recommendation -- 1 Introduction -- 2 Preliminaries -- 3 Methodology -- 3.1 Interactive View Encoder -- 3.2 Fold View Encoder -- 3.3 Divergence Constraint -- 3.4 Co-contrastive Learning -- 3.5 Efficient Joint Learning Without Sampling -- 4 Experiments -- 4.1 Datasets -- 4.2 Compared Models |
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-- 4.3 Experimental Settings -- 4.4 Performance Comparison -- 4.5 Effectiveness Analysis on Data Sparsity Issue -- 4.6 Ablation Study -- 4.7 Parameter Sensitivity Analysis -- 5 Conclusion -- References -- Pattern Matching and Information-Aware Between Reviews and Ratings for Recommendation -- 1 Introduction -- 2 Related Work -- 3 The Proposed MIAN Model -- 3.1 Global Matching Module -- 3.2 Specific Matching Module -- 3.3 Information-Aware Layer -- 3.4 Interaction Aggregation Layer -- 3.5 Joint Learning of Review Matching and Rating Prediction. |
4 Experiments -- 4.1 Experimental Settings -- 4.2 Overall Performance (RQ1) -- 4.3 Ablation Experimental Study (RQ2) -- 4.4 Case Study (RQ3) -- 5 Conclusion -- References -- Cross-View Contrastive Learning for Knowledge-Aware Session-Based Recommendation -- 1 Introduction -- 2 Notations and Problem Statement -- 3 Method -- 3.1 View Generation -- 3.2 Dual-channel Graph View Encoder -- 3.3 Contrastive Learning and Recommendation -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Over Performance -- 4.3 Model Ablation Study -- 4.4 Handling Different Session Lengths -- 4.5 Hyperparameter Study -- 5 Conclusion -- References -- Reinforcement Learning -- HiSA: Facilitating Efficient Multi-Agent Coordination and Cooperation by Hierarchical Policy with Shared Attention -- 1 Introduction -- 2 Preliminaries and Related Work -- 2.1 Communicative Decentralized Partially Observable Markov Decision Process -- 2.2 Communicative Methods in MAS -- 2.3 Attention Mechanism -- 2.4 Hierarchical Policy with Attention -- 3 Method -- 3.1 Shared Attention Map for Communication -- 3.2 Hierarchical Structure with Shared Attention Mechanism -- 3.3 HiSA for Multi-agent Reinforcement Learning -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Experiments on the StarCraft Multi-Agent Challenge -- 4.3 Experiments on the Overcooked -- 5 Summary and Outlook -- References -- DDMA: Discrepancy-Driven Multi-agent Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 3.1 Partially Observable Stochastic Game -- 3.2 Reinforcement Learning -- 4 Method -- 4.1 Initialization of the Multi-agent Policy -- 4.2 Focused Learning of the Multi-agent Policy -- 4.3 Training -- 5 Experiments -- 5.1 Collision Corridor -- 5.2 MPE Scenarios -- 5.3 Ablation Study -- 6 Conclusion -- References -- PRAG: Periodic Regularized Action Gradient for Efficient Continuous Control. |
1 Introduction -- 2 Background -- 3 From TD-Error to Action Gradient Error -- 3.1 TD-Error and TD-Learning -- 3.2 Action Gradient Error and Action Gradient Regularizer -- 3.3 Periodic Regularized Action Gradient Algorithm -- 4 Experiment -- 4.1 Overall Performance -- 4.2 Ablation Study -- 4.3 Parameter Study -- 5 Related Work -- 6 Conclusion -- References -- Identifying Multiple Influential Nodes for Complex Networks Based on Multi-agent Deep Reinforcement Learning -- 1 Introduction -- 2 Problem Formulation -- 3 Multi-agent Identification Framework -- 3.1 General Framework of MAIF -- 3.2 Framework Elements -- 3.3 Independent Actor-Critic Model -- 3.4 Counterfactual Multi-agent (COMA) Policy Gradients and Gate Recurrent Unit (GRU) Network -- 4 Experimental Preliminaries -- 4.1 Dataset -- 4.2 Comparison Method -- 4.3 Susceptible-Infected-Recovered (SIR) Model -- 5 Experimental Results -- 6 Conclusion -- References -- Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behavior -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Experimental Setup -- 4.1 Iterated Prisoner's Dilemma (IPD) -- 4.2 Behavioral Cloning with Demonstration Rewards -- 4.3 Online Learning Agents -- 5 Results: Algorithms' Tournament -- 5.1 Multi-agent Tournament -- 6 Behavioral Cloning with Human Data -- 7 Clinical |
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Evidences and Implications -- 8 Discussion -- 9 Conclusion -- References -- Optimizing Exploration-Exploitation Trade-off in Continuous Action Spaces via Q-ensemble -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Proposed Method -- 4.1 Ensemble-Based Exploration Strategy -- 4.2 Selective Repeat Update -- 5 Experiments -- 5.1 Parameter Settings -- 5.2 Comparative Evaluation -- 5.3 Ablation Study -- 6 Conclusion -- References -- Hidden Information General Game Playing with Deep Learning and Search -- 1 Introduction -- 2 Background. |
2.1 General Game Playing -- 2.2 Generalised AlphaZero -- 2.3 Recursive Belief-Based Learning -- 3 Method -- 3.1 Propositional Networks for GDL-II -- 3.2 Sampling GDL-II States -- 3.3 CFR Search -- 3.4 Reinforcement Learning -- 4 Experiments -- 4.1 Evaluation Methodology -- 4.2 Results and Discussion -- 5 Conclusion and Future Work -- References -- Sequential Decision Making with ``Sequential Information'' in Deep Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Deep Reinforcement Learning -- 3.2 Depthwise Separable Convolution -- 3.3 3D Temporal Convolution -- 4 Temporal Aggregation Network in DRL -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Results Analysis -- 6 Conclusion and Future Work -- References -- Two-Stream Communication-Efficient Federated Pruning Network -- 1 Introduction -- 2 Proposed Method -- 2.1 Preliminary -- 2.2 Downstream Compression via DRL Agent -- 2.3 Upstream Compression Based on Proximal Operator -- 3 Experimental Setup and Results -- 3.1 Compared Methods -- 3.2 Datasets and Simulation Settings -- 3.3 Experiment Results -- 3.4 Ablation Study -- 4 Conclusion -- References -- Strong General AI -- Multi-scale Lightweight Neural Network for Real-Time Object Detection -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Network Architecture -- 3.2 Fast Down-Sampling Module -- 3.3 Reduced Computational Block -- 3.4 Detection Part -- 4 Experiments -- 4.1 Experiments Setup -- 4.2 Results -- 4.3 Ablation Study -- 5 Conclusion -- References -- Hyperspectral Image Classification Based on Transformer and Generative Adversarial Network -- 1 Introduction -- 2 Related Works -- 2.1 Superpixelwise PCA -- 2.2 Auxiliary Classifier GANs -- 3 Proposed Method -- 3.1 The Framework of the Proposed SPCA-TransGAN -- 3.2 The Network Framework of Generator. |
3.3 The Network Framework of Multi-scale Discriminator -- 4 Experimental Results and Analysis -- 4.1 DataSets -- 4.2 Classification Results on Two Data Sets -- 5 Conclusion -- References -- Deliberation Selector for Knowledge-Grounded Conversation Generation -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Problem Statement -- 3.2 Model Description -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Baselines -- 4.4 Supplementary Details -- 4.5 Experimental Results -- 4.6 Ablation Test -- 4.7 Case Study -- 5 Conclusion -- References -- Training a Lightweight ViT Network for Image Retrieval -- 1 Introduction -- 2 Methodology -- 2.1 Knowledge Distillation with Relaxed Contrastive Loss -- 2.2 Quantized Heterogeneous Knowledge Distillation -- 2.3 Distillation Heterogeneous Quantization for Multi-exit Networks -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Comprehensive Comparison Results -- 3.3 Analysis of Distillation Quantization of Multi-exit Networks -- 4 Conclusions -- References -- Vision and Perception -- Segmented-Original Image Pairs to Facilitate Feature Extraction in Deep Learning Models -- 1 Introduction -- 2 Method -- 2.1 Datasets -- 2.2 Segmented-Original Image Pair Training Method -- 3 Experiments -- 3.1 Segmentation Algorithm -- 3.2 Classification |
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Tasks -- 3.3 Unsupervised Learning Tasks -- 4 Conclusion -- References -- FusionSeg: Motion Segmentation by Jointly Exploiting Frames and Events -- 1 Introduction -- 2 Related Work -- 2.1 Motion Segmentation -- 2.2 Visual Transformer -- 3 Methodology -- 3.1 Input Representation -- 3.2 Network Architecture -- 3.3 Feature Fusion Method -- 3.4 Multi-Object Association -- 3.5 Feature Matching and Propagation -- 4 Experiment and Results -- 4.1 Implementation Details -- 4.2 Overview of Datasets -- 4.3 Discussion of Results -- 5 Conclusions and Future Work -- References. |
Weakly-Supervised Temporal Action Localization with Multi-Head Cross-Modal Attention. |
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2. |
Record Nr. |
UNICASVEA0071919 |
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Autore |
Wat, Aleksander |
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Titolo |
L'ebreo errante / Aleksander Wat ; a cura di Luigi Marinelli |
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Pubbl/distr/stampa |
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Titolo uniforme |
Bezrobotny Lucyfer |
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ISBN |
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Descrizione fisica |
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Collana |
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Disciplina |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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3. |
Record Nr. |
UNINA9910254838703321 |
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Titolo |
Advances in Computing : 12th Colombian Conference, CCC 2017, Cali, Colombia, September 19-22, 2017, Proceedings / / edited by Andrés Solano, Hugo Ordoñez |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (XX, 807 p. 310 illus.) |
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Collana |
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Communications in Computer and Information Science, , 1865-0937 ; ; 735 |
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Disciplina |
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Soggetti |
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Software engineering |
Computer networks |
Artificial intelligence |
Software Engineering |
Computer Communication Networks |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Information and knowledge management -- Software engineering and IT architectures -- Educational informatics -- Intelligent systems and robotics -- Human-computer interaction -- Distributed systems and large-scale architectures -- Image processing, computer vision and multimedia -- Security of the information -- Formal methods, computational logic and theory of computation. |
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Sommario/riassunto |
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This book constitutes the refereed proceedings of the 12th Colombian Conference on Computing, CCC 2017, held in Cali, Colombia, in September 2017. The 56 revised full papers presented were carefully reviewed and selected from 186 submissions. The papers are organized in topical sections on information and knowledge management, software engineering and IT architectures, educational informatics, intelligent systems and robotics, human-computer interaction, distributed systems and large-scale architectures, image processing, computer vision and multimedia, security of the information, formal |
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methods, computational logic and theory of computation. |
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