Artificial Neural Networks and Machine Learning – ICANN 2024

33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part I

Häftad, Engelska, 2024

Av Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko, Kristina Malinovska, Jurgen Schmidhuber

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and fairness in machine learning.Part II - computer vision: classification; and computer vision: 3D methods. Part III - computer vision: anomaly detection; and topics in computer vision. Part IV - brain-inspired computing;

Produktinformation

Utgivningsdatum2024-09-17
Mått155 x 235 x 28 mm
Vikt774 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor480
FörlagSpringer International Publishing AG
ISBN9783031723315

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.- 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.