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神经网络（Neural Networks）是世界上三个最古老的神经建模学会的档案期刊:国际神经网络学会(INNS)、欧洲神经网络学会(ENNS)和日本神经网络学会(JNNS)。神经网络提供了一个论坛，以发展和培育一个国际社会的学者和实践者感兴趣的所有方面的神经网络和相关方法的计算智能。神经网络欢迎高质量论文的提交，有助于全面的神经网络研究，从行为和大脑建模，学习算法，通过数学和计算分析，系统的工程和技术应用，大量使用神经网络的概念和技术。这一独特而广泛的范围促进了生物和技术研究之间的思想交流，并有助于促进对生物启发的计算智能感兴趣的跨学科社区的发展。因此，神经网络编委会代表的专家领域包括心理学，神经生物学，计算机科学，工程，数学，物理。该杂志发表文章、信件和评论以及给编辑的信件、社论、时事、软件调查和专利信息。文章发表在五个部分之一:认知科学，神经科学，学习系统，数学和计算分析、工程和应用。 官网地址：http://dblp.uni-trier.de/db/journals/nn/
The Self-Learning Agent with a Progressive Neural Network Integrated Transformer
The Self-Learning Agent with a Progressive Neural Network Integrated Transformer
Arxiv
1+阅读 · 4月3日
Improved universal approximation with neural networks studied via affine-invariant subspaces of $L_2(\mathbb{R}^n)$
Arxiv
0+阅读 · 4月3日
Deep learning for music generation. Four approaches and their comparative evaluation
Deep learning for music generation. Four approaches and their comparative evaluation
Arxiv
0+阅读 · 4月3日
Data-Driven Object Tracking: Integrating Modular Neural Networks into a Kalman Framework
Data-Driven Object Tracking: Integrating Modular Neural Networks into a Kalman Framework
Arxiv
0+阅读 · 4月3日
Prosperity: Accelerating Spiking Neural Networks via Product Sparsity
Prosperity: Accelerating Spiking Neural Networks via Product Sparsity
Arxiv
0+阅读 · 4月2日
MI-HGNN: Morphology-Informed Heterogeneous Graph Neural Network for Legged Robot Contact Perception
MI-HGNN: Morphology-Informed Heterogeneous Graph Neural Network for Legged Robot Contact Perception
Arxiv
0+阅读 · 4月3日
Atrial constitutive neural networks
Atrial constitutive neural networks
Arxiv
0+阅读 · 4月3日
Do Two AI Scientists Agree?
Do Two AI Scientists Agree?
Arxiv
0+阅读 · 4月3日
RBR4DNN: Requirements-based Testing of Neural Networks
RBR4DNN: Requirements-based Testing of Neural Networks
Arxiv
0+阅读 · 4月3日
Reservoir Computing: A New Paradigm for Neural Networks
Reservoir Computing: A New Paradigm for Neural Networks
Arxiv
0+阅读 · 4月3日
Representation and Regression Problems in Neural Networks: Relaxation, Generalization, and Numerics
Representation and Regression Problems in Neural Networks: Relaxation, Generalization, and Numerics
Arxiv
0+阅读 · 4月3日
Liquid Neural Networks: Next-Generation AI for Telecom from First Principles
Liquid Neural Networks: Next-Generation AI for Telecom from First Principles
Arxiv
0+阅读 · 4月3日
CARE: Confidence-Aware Regression Estimation of building density fine-tuning EO Foundation Models
CARE: Confidence-Aware Regression Estimation of building density fine-tuning EO Foundation Models
Arxiv
0+阅读 · 4月3日
When Can You Trust Your Explanations? A Robustness Analysis on Feature Importances
When Can You Trust Your Explanations? A Robustness Analysis on Feature Importances
Arxiv
0+阅读 · 4月3日
A time-stepping deep gradient flow method for option pricing in (rough) diffusion models
A time-stepping deep gradient flow method for option pricing in (rough) diffusion models
Arxiv
0+阅读 · 4月3日
参考链接
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人工智能
Elsevier
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