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Implicit Differentiation01:25

Implicit Differentiation

88
In classical mechanics, motion is often described through relationships between spatial coordinates and time. A car moving along a straight highway with constant acceleration serves as a simple case where velocity is an explicit function of time. This scenario results in a linear equation, enabling straightforward analysis using basic differentiation techniques.In contrast, a satellite in circular orbit follows a path defined by an implicit function. The position of the satellite is constrained...
88
Gradient and Del Operator01:14

Gradient and Del Operator

4.7K
In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...
4.7K
Implicit Differentiation: Problem Solving01:29

Implicit Differentiation: Problem Solving

82
Curves defined implicitly, where variables cannot be separated algebraically, require specialized techniques for analysis. The conchoid of Nicomedes exemplifies such a case. Its equation links x and y in a way that prevents isolation of one variable, making implicit differentiation essential to determine the slope and behavior at any point on the curve.The implicit form of the conchoid can be expressed as:To differentiate this equation, y is treated as a function of x, and the chain rule is...
82
Forced Transdifferentiation01:28

Forced Transdifferentiation

2.4K
Transdifferentiation, also known as lineage reprogramming, was first discovered by Selman and Kafatos in 1974 in silkmoths. They observed that the moths’ cuticle-producing cells transformed into salt-producing cells. Many such cases of natural transdifferentiation occur in organisms. In humans, pancreatic alpha cells can become beta cells. In newts, the loss of the eye’s lens causes the pigmented epithelial cells to transdifferentiate into the lens cells.
Artificial...
2.4K

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相关实验视频

Updated: Mar 3, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.1K

一种混合适应式预先条件的梯度方法,具有深度学习的动力.

Zhiyang Zhou1, Huisheng Zhang2, Zhaoyang Chen2

  • 1College of Artificial Intelligence, Dalian Maritime University, Dalian, 116026, China.

Neural networks : the official journal of the International Neural Network Society
|March 1, 2026
PubMed
概括

我们介绍了AdapGradm,这是一款用于深度学习的新型二阶优化器,与一阶效率相匹配. 它的混合版本,HAdapGradm,表现出比Adam更好的训练错误和概括.

关键词:
适应式预先条件的梯度方法收 收 收 收 收 收深度神经网络是一种深度神经网络.优化器优化器优化器优化器优化器

相关实验视频

Last Updated: Mar 3, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.1K

科学领域:

  • 机器学习 机器学习
  • 深度学习优化优化

背景情况:

  • 深度神经网络通常使用一阶随机优化.
  • 二级方法提供更快的融合,但在深度学习中面临计算开销.

研究的目的:

  • 介绍AdapGradm,这是一款用于深度学习的新型二级自适应优化器.
  • 提出HAdapGradm,这是一个混合优化器,可以在AdapGradm和SGD之间无过渡.
  • 评估这些新优化器的性能和融合.

主要方法:

  • 为了提高效率,AdapGradm使用了从一级衍生品获得的对角近似赫西安.
  • HAdapGradm将AdapGradm与SGD结合在一起,以实现灵活的优化.
  • 在温和条件下,收是严格确定的.

主要成果:

  • AdapGradm实现了与Adam等一级优化器可比的计算效率.
  • 与Adam和基线优化器相比,HAdapGradm表现出较低的训练错误.
  • HAdapGradm在图像分类和NLP任务中表现出卓越的概括能力.

结论:

  • AdapGradm和HAdapGradm为深度学习提供了高效的二级优化.
  • HAdapGradm为现有优化器提供了一种实用且有效的替代方案.
  • 提出的方法提升了深度学习培训的效率和性能.