Gradient Episodic Memory for Continual Learning: Difference between revisions
Jump to navigation
Jump to search
No edit summary |
No edit summary |
||
| Line 4: | Line 4: | ||
== 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/ | 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>. | ||
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 01:31, 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]. Gradient Episodic Memory (GEM) is a continual learning model that alleviates forgetting on previous acquired knowledge, while solving new problems more efficiently.