Neural Information Processing 31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2–6, 2024, Proceedings, Part III /
The eleven-volume set LNCS 15286-15296 constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024. The 318 regular papers presented in the proceedings set were carefully reviewed and selecte...
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| 格式: | 電子 電子書 |
| 語言: | 英语 |
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Singapore :
Springer Nature Singapore : Imprint: Springer,
2025.
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| 版: | 1st ed. 2025. |
| 叢編: | Lecture Notes in Computer Science,
15288 |
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| 在線閱讀: | https://doi.org/10.1007/978-981-96-6582-2 |
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| 245 | 1 | 0 | |a Neural Information Processing |h [electronic resource] : |b 31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2–6, 2024, Proceedings, Part III / |c edited by Mufti Mahmud, Maryam Doborjeh, Kevin Wong, Andrew Chi Sing Leung, Zohreh Doborjeh, M. Tanveer. |
| 250 | |a 1st ed. 2025. | ||
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| 490 | 1 | |a Lecture Notes in Computer Science, |x 1611-3349 ; |v 15288 | |
| 505 | 0 | |a FreeFlow: A Unified Viewpoint on Diffusion Probabilistic Models via Optimal Transport and Fluid Mechanics -- Optimizing CNNs with Gram Schmidt Non-Iterative Learning for Image Recognition -- Improving Multilingual Speech Recognition with Tucker-compressed Mixture of LoRAs -- MetaFix: Semi-Supervised Model Agnostic Meta-Learning using Consistency Regularization -- Towards Private and Fair Machine Learning: Group-Specific Differentially Private Stochastic Gradient Descent with Threshold Optimization -- LogMoE: Optimizing Mixture of Experts for Log Anomaly Detection via Knowledge Distillation -- Cross-Domain Few-Shot Learning with Equiangular Embedding and Dynamic Adversarial Augmentation -- ∞-Net: An Unsupervised Model for Online Graph Time-Series Denoising -- On Learnable Parameters of Optimal and Suboptimal Deep Learning Models -- Aero-engine Condition-Based Maintenance Planning Using Reinforcement Learning -- Multi-Timescale Processing with Heterogeneous Assembly Echo StateNetworks -- ADERec: Adaptive Data Augmentation Sequence Recommendation Based on Dual Network Architecture -- Pruning neural network parameters using recurrent neural networks -- MA-Mamba: Multi-Agent Reinforcement Learning with State Space Model -- Decentralized Extension for Centralized Multi-Agent Reinforcement Learning via Online Distillation -- Advancing RVFL networks: Robust classification with the HawkEye loss function -- An Enhanced MILP-based Verifier for Adversary Robustness of Neural Networks -- Hide-and-Seek GANs for Generation with Limited Data -- Unsupervised Robust Hypergraph Correlation Hashing for MultimediaRetrieval -- Emotional Atmosphere Soft Label for Emotion Recognition in Conversations -- CCATS: Moving Forward with Class-Conditional Time Series Generation -- M3ixTS: Mixing of Multi-patch and Multi-view For Time Series Forecasting -- CSTFormer: Cross Spatial-Temporal Learning Transformer withDynamic Sign Language Recognition through an Augmented Reality Environment -- MmFormer: A Novel Multi-Scale and Multi-Period Transformer Model for Irregular periodic Network Traffc Prediction -- Time Series Anomaly Detection via Temporal Dependencies and Multivariate Correlations Integrating -- Transformer-Based Long Time Series Forecasting with Decoupled Information Extraction and Information Complementarity. | |
| 520 | |a The eleven-volume set LNCS 15286-15296 constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024. The 318 regular papers presented in the proceedings set were carefully reviewed and selected from 1301 submissions. They focus on four main areas, namely: theory and algorithms; cognitive neurosciences; human-centered computing; and applications. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 1 | 4 | |a Automated Pattern Recognition. |
| 650 | 2 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Machine Learning. |
| 700 | 1 | |a Mahmud, Mufti. |e editor. |0 (orcid)0000-0002-2037-8348 |1 https://orcid.org/0000-0002-2037-8348 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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