Artificial Neural Networks and Machine Learning – ICANN 2024 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part I /
The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024. The 294 full papers and 16 short papers included in these proceedin...
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| Бусад зохиолчид: | , , , |
| Формат: | Цахим Цахим ном |
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Springer Nature Switzerland : Imprint: Springer,
2024.
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| Хэвлэл: | 1st ed. 2024. |
| Цуврал: | Lecture Notes in Computer Science,
15016 |
| Нөхцлүүд: | |
| Онлайн хандалт: | https://doi.org/10.1007/978-3-031-72332-2 |
| Шошгууд: |
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| 245 | 1 | 0 | |a Artificial Neural Networks and Machine Learning – ICANN 2024 |h [electronic resource] : |b 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part I / |c edited by Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko. |
| 250 | |a 1st ed. 2024. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2024. | |
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| 490 | 1 | |a Lecture Notes in Computer Science, |x 1611-3349 ; |v 15016 | |
| 505 | 0 | |a -- Theory of Neural Networks and Machine Learning. -- Multi-label Robust Feature Selection via Subspace-Sparsity Learning. -- Nullspace-based metric for classification of dynamical systems and sensors. -- On the Bayesian Interpretation of Robust Regression Neural Networks. -- Probability-Generating Function Kernels for Spherical Data. -- Tailored Finite Point Operator Networks for Interface problems. -- Novel Methods in Machine Learning. -- A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class. -- Adaptive Compression of the Latent Space in Variational Autoencoders. -- Asymmetric Isomap for Dimensionality Reduction and Data Visualization. -- CALICO: Confident Active Learning with Integrated Calibration. -- Improved Multi-hop Reasoning through Sampling and Aggregating. -- Learning Solutions of Stochastic Optimization Problems with Bayesian Neural Networks. -- Revealing Unintentional Information Leakage in Low-Dimensional Facial Portrait Representations. -- Safe Data Resampling Method based on Counterfactuals Analysis. -- Test-Time Augmentation for Traveling Salesperson Problem. -- Novel Neural Architectures. -- Resonator-Gated RNNs. -- Towards a model of associative memory with learned distributed representations. -- Neural Architecture Search. -- Accelerated NAS via pretrained ensembles and multi-fidelity Bayesian Optimization. -- Feature Activation-Driven Zero-Shot NAS: A Contrastive Learning Framework. -- NAS-Bench-Compre: A Comprehensive Neural Architecture Search Benchmark with Customizable Components. -- NAVIGATOR-D3: Neural Architecture search using VarIational Graph Auto-encoder Toward Optimal aRchitecture Design for Diverse Datasets. -- ResBuilder: Automated Learning of Depth with Residual Structures -- Self-Organization. -- A Neuron Coverage-based Self-Organizing Approach for RBFNNs in Multi-Class Classification Tasks. -- Self-Organising Neural Discrete Representation Learning à la Kohonen. -- Neural Processes. -- Combined Global and Local Information Diffusion of Neural Processes. -- Topology of Neural Processes. -- Novel Architectures for Computer Vision. -- DEEPAM: Toward Deeper Attention Module in Residual Convolutional Neural Networks. -- Differentiable Largest Connected Component Layer for Image Mattin. -- Enhancing Generalization in Convolutional Neural Networks through Regularization with Edge and Line Features. -- Transformer Tracker based on Multi-level Residual Perception Structure. .-Multimodal Architectures. -- CAW: Confidence-based Adaptive Weighted Model for Multi-modal Entity Linking. -- Exploring Interpretable Semantic Alignment for Multimodal Machine Translation. -- Fairness in Machine Learning. -- CFP: A Reinforcement Learning Framework for Comprehensive Fairness-Performance Trade-off in Machine Learning. | |
| 520 | |a The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Computers. | |
| 650 | 0 | |a Application software. | |
| 650 | 0 | |a Computer networks . | |
| 650 | 1 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Computing Milieux. |
| 650 | 2 | 4 | |a Computer and Information Systems Applications. |
| 650 | 2 | 4 | |a Computer Communication Networks. |
| 700 | 1 | |a Wand, Michael. |e editor. |0 (orcid)0000-0003-0966-7824 |1 https://orcid.org/0000-0003-0966-7824 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Malinovská, Kristína. |e editor. |0 (orcid)0000-0001-7638-028X |1 https://orcid.org/0000-0001-7638-028X |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Schmidhuber, Jürgen. |e editor. |0 (orcid)0000-0002-1468-6758 |1 https://orcid.org/0000-0002-1468-6758 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Tetko, Igor V. |e editor. |0 (orcid)0000-0002-6855-0012 |1 https://orcid.org/0000-0002-6855-0012 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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