密集优化器:一种以信息率为导向的结构性搜索方法,用于密集型神经网络设计.
概括
密度优化器自动设计高性能密度卷积网络. 这种新的方法优化了网络架构,提高了计算机视觉任务的准确性和效率.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习架构 深度学习架构
- 机器学习优化优化
背景情况:
- 密集卷积网络 (DenseNets) 提供高效和紧的架构.
- 对于DenseNets的手动设计,在道调整和再利用方面面临着挑战.
- 自动化DenseNet架构搜索对于进一步提高性能至关重要.
研究的目的:
- 为高性能密集型网络提出一个自动化架构搜索方法.
- 为高效的网络设计开发一种新的优化框架.
- 提高计算机视觉模型的精度和降低计算成本.
主要方法:
- 开发了"密集优化器",用于密集网络的架构搜索方法.
- 将密集的网络视为等级信息系统,最大限度地提高信息.
- 利用功率定律来限制分布,并制定了一个优化问题.
- 实现了一个分支和边界算法,集成权力法原理和搜索空间缩放.
主要成果:
- 在各种计算机视觉基准数据集上验证了"密度优化器".
- 在搜索的DenseNet-OPT模型中,在CIFAR-100上获得了84.3%的top-1精度,5.97%的改进.
- 在单个CPU上仅用4小时的计算时间,证明了高质量的搜索结果.
结论:
- "密度优化器"成功地自动化了高性能密度卷积网络的设计.
- 与手工设计的对应物相比,该方法显著提高了准确性.
- 有效的搜索过程使其适用于现实世界的应用程序.
相关概念视频
Protein Networks
4.1K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.1K
Neural Circuits
1.6K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.6K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
103
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
103
Neuron Structure
225.5K
Overview
225.5K


