Artificial neural networks and machine learning - ICANN 2024 Part 1
.-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...
Gespeichert in:
| Körperschaft: | |
|---|---|
| Weitere Verfasser: | , , , |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Cham
Springer
2024
|
| Schriftenreihe: | Lecture notes in computer science
15016 |
| Schlagworte: |
Angewandte Informatik
> COM094000
> COMPUTERS / Computer Science
> COMPUTERS / Computer Vision & Pattern Recognition
> COMPUTERS / Hardware / Network Hardware
> COMPUTERS / Neural Networks
> Computer vision
> Information technology: general issues
> Machine learning
> Maschinelles Lernen
> Maschinelles Sehen, Bildverstehen
> Network hardware
> Netzwerk-Hardware
> Neural networks & fuzzy systems
> Neuronale Netze und Fuzzysysteme
> Konferenzschrift
|
| Online Zugang: | Cover |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Tag hinzufügen 
| Zusammenfassung: | .-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. 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 |
|---|---|
| Beschreibung: | Literaturangaben |
| Beschreibung: | xxxiii, 480 Seiten Illustrationen, Diagramme |
| ISBN: | 9783031723315 978-3-031-72331-5 |