Gradient Episodic Memory for Continual Learning: Difference between revisions
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== Background and Introduction == | == Background and Introduction == | ||
Supervised learning consist of a training set <math>D_{tx}={(x_i,y_i)}^n_{i=1}</math>, where <math>x_i \in X</math> and <math>y_i \in Y</math>. | Supervised learning consist of a training set <math>D_{tx}={(x_i,y_i)}^n_{i=1}</math>, where <math>x_i \in X</math> and <math>y_i \in Y</math>. Empirical Risk Minimization (ERM) is one of the common supervised learning method used to minimize a loss function by having multiple passes over the training set. | ||
<center> | |||
<math> | |||
1/|D_{tr}|\textstyle \sum_{x_i,y_i) \in D_{tr}} \ell (f(x_i),y_i) | |||
</math> | |||
</center> | |||
where <math>\ell :math cal{Y} \times math cal{Y} ->[0, \infty)</math> | |||
Gradient Episodic Memory (GEM) is a continual learning model that alleviates forgetting on previous acquired knowledge, while solving new problems more efficiently. | Gradient Episodic Memory (GEM) is a continual learning model that alleviates forgetting on previous acquired knowledge, while solving new problems more efficiently. | ||
Revision as of 00:48, 17 November 2018
Group Member
Yu Xuan Lee, Tsen Yee Heng
Background and Introduction
Supervised learning consist of a training set [math]\displaystyle{ D_{tx}={(x_i,y_i)}^n_{i=1} }[/math], where [math]\displaystyle{ x_i \in X }[/math] and [math]\displaystyle{ y_i \in Y }[/math]. Empirical Risk Minimization (ERM) is one of the common supervised learning method used to minimize a loss function by having multiple passes over the training set.
[math]\displaystyle{ 1/|D_{tr}|\textstyle \sum_{x_i,y_i) \in D_{tr}} \ell (f(x_i),y_i) }[/math]
where [math]\displaystyle{ \ell :math cal{Y} \times math cal{Y} -\gt [0, \infty) }[/math]
Gradient Episodic Memory (GEM) is a continual learning model that alleviates forgetting on previous acquired knowledge, while solving new problems more efficiently.