Atjaunināt sīkdatņu piekrišanu

Neural Information Processing: 30th International Conference, ICONIP 2023, Changsha, China, November 2023, 2023, Proceedings, Part VII 1st ed. 2024 [Mīkstie vāki]

Edited by , Edited by , Edited by , Edited by , Edited by
  • Formāts: Paperback / softback, 585 pages, height x width: 235x155 mm, weight: 920 g, 214 Illustrations, color; 12 Illustrations, black and white; XX, 585 p. 226 illus., 214 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 1961
  • Izdošanas datums: 25-Nov-2023
  • Izdevniecība: Springer Verlag, Singapore
  • ISBN-10: 9819981255
  • ISBN-13: 9789819981250
  • Mīkstie vāki
  • Cena: 91,53 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Standarta cena: 107,69 €
  • Ietaupiet 15%
  • Grāmatu piegādes laiks ir 3-4 nedēļas, ja grāmata ir uz vietas izdevniecības noliktavā. Ja izdevējam nepieciešams publicēt jaunu tirāžu, grāmatas piegāde var aizkavēties.
  • Daudzums:
  • Ielikt grozā
  • Piegādes laiks - 4-6 nedēļas
  • Pievienot vēlmju sarakstam
  • Formāts: Paperback / softback, 585 pages, height x width: 235x155 mm, weight: 920 g, 214 Illustrations, color; 12 Illustrations, black and white; XX, 585 p. 226 illus., 214 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 1961
  • Izdošanas datums: 25-Nov-2023
  • Izdevniecība: Springer Verlag, Singapore
  • ISBN-10: 9819981255
  • ISBN-13: 9789819981250
The nine-volume set constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023.  

The 1274 papers presented in the proceedings set were carefully reviewed and selected from 652 submissions. 

The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.
Theory and Algorithms.- A 3D UWB hybrid localization method based on
BSR and L-AOA.- Unsupervised Feature Selection Using Both Similar and
Dissimilar Structures.- STA-Net: Reconstruct Missing Temperature Data of
Meteorological Stations Using a Spatiotemporal Attention Neural
Network.- Embedding Entity and Relation for Knowledge Graph by Probability
Directed Graph.- Solving the inverse problem of laser with complex-valued
field by physics-informed neural networks.- Efficient Hierarchical
Reinforcement Learning via Mutual Information Constrained Subgoal
Discovery.- Accelerate Support Vector Clustering via Spectral Data
Compression.- A Novel Iterative Fusion Multi-Task Learning Framework for
Solving Dense Prediction.- Anti-Interference Zeroing Neural Network Model for
Time-Varying Tensor Square Root Finding.- CLF-AIAD: A Contrastive Learning
Framework for Acoustic Industrial Anomaly Detection.- Prediction and analysis
of acoustic displacement field using the method of neural network.- Graph
Multi-Dimensional Feature Network.- CBDN: A Chinese short-text classification
model based on Chinese BERT and fused deep neural networks.- Lead ASR Models
to Generalize Better Using Approximated Bias-Variance Tradeof.- Human-guided
Transfer Learning for Autonomous Robot.- Leveraging Two-scale Features to
Enhance Fine-grained Object Retrieval.- Predefined-time Synchronization of
Complex Networks with Disturbances by Using Sliding Mode
Control.- Reward-Dependent and Locally Modulated Hebbian Rule for Pattern
Classification.- Robust Iterative Hard Thresholding Algorithm for Fault
Tolerant RBF Network.- Cross-lingual Knowledge Distillation via Flow-based
Voice Conversion for Robust Polyglot Text-To-Speech.- A health evaluation
algorithm for edge nodes based on LSTM.- A Comprehensive Review of Arabic
Question Answering Datasets.- Solving Localized Wave Solutions of the
Nonlinear PDEs using Physics-Constraint Deep Learning Method.- Graph
Reinforcement Learning For Securing Critical Loads By
E-mobility.- Human-Object Interaction Detection with Channel Aware
Attention.- AAKD-Net:Attention-based Adversarial Knowledge Distillation
Network for Image Classification.- A High-Performance Tensorial Evolutionary
Computation for Solving Spatial Optimization Problems.- Towards better
evaluations of class activation mapping and interpretability of
CNNs.- Contrastive Learning-Based Music Recommendation Model.- A Memory
Optimization Method for Distributed Training.- Unsupervised Monocular Depth
Estimation with Semantic Reconstruction using Dual-Discriminator Generative
Adversarial Networks.- Generating Spatiotemporal Trajectories with GANs and
Conditional GANs.- Visual Navigation of Target-Driven Memory-Augmented
Reinforcement Learning.- Recursive Constrained Maximum Versoria Criterion
Algorithm for Adaptive Filtering.- Graph Pointer Network and Reinforcement
Learning for Thinnest Path Problem.- Multi-Neuron Information Fusion for
Direct Training Spiking Neural Networks.- Event-based Object Recognition
Using Feature Fusion and Spiking Neural Networks.- Circular FC: Fast Fourier
Transform Meets Fully Connected Layer For Convolutional Neural
Network.- Accurate Latency Prediction of Deep Learning Model Inference under
Dynamic Runtime Resource.- Robust LS-QSVM Implementation via Efficient Matrix
Factorization and Eigenvalue Estimation.- An Adaptive Auxiliary Training
Method of Autoencoders and its Application in Anomaly Detection.- Matrix
Contrastive Learning for Short Text Clustering.- Sharpness-aware Minimization
for Out-of-Distribution Generalization.- Rapid APT Detection in
Resource-Constrained IoT Devices Using Global Vision Federated Learning
(GV-FL).