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变量张量神经网络用于深度学习
Saeed S Jahromi1,2,3, Román Orús4,5,6
1Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran.
Scientific reports
|August 16, 2024
概括
我们通过将张量网络 (TN) 与深度神经网络 (NN) 集成,引入可扩展的张量神经网络 (TNN). 这种方法克服了可扩展性的局限性,使得深度学习模型的高效训练具有广泛的参数.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算物理 计算物理
背景情况:
- 深度神经网络 (NN) 面临着不断增加的神经元数量的可扩展性挑战,限制了网络深度.
- 现有的NN架构与庞大的参数空间作斗争,阻碍了复杂任务的性能.
研究的目的:
- 开发一个可扩展的神经网络架构,克服深度和参数限制.
- 通过将张量网络 (TN) 集成到 NN 框架中,引入一种新的张量神经网络 (TNN).
- 为了实现深度学习模型的高效训练,具有大量的参数.
主要方法:
- 在神经网络 (NN) 架构中集成张量网络 (TN).
- 为TNNs开发一种由DMRG启发的变异性训练技术.
- 使用局部梯度下降方法进行张量梯度计算,允许混合密度和张量层.
主要成果:
- 展示了一个可扩展的张量神经网络 (TNN) 架构.
- 在一个大的参数空间上实现了高效的训练.
- 提供了回归,分类和图像识别 (MNIST) 的基准结果,以验证TNN的准确性和效率.
结论:
- 提议的TNN架构有效地解决了深度学习中的可扩展性限制.
- 变化训练算法能够有效处理大型参数空间,并提供对模型纠的见解.
- 超级神经网络为开发更深入,更高效的神经网络提供了一个有希望的方向.
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