Task Understanding from Confushing Multitask Data: Difference between revisions
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=Experiment= | =Experiment= | ||
==Setup== | ==Setup== | ||
3 data sets are used to compare CSL to existing methods, 1 function regression task and 2 image classification tasks. | 3 data sets are used to compare CSL to existing methods, 1 function regression task and 2 image classification tasks. | ||
The function regression data comes in the form of <math>(x_i,y_i),i=1,...,m</math> pairs. However, unlike typical regression problems, there are multiple <math>f_j(x),j=1,...,n</math> mapping functions, so the goal is to recover both the mapping functions <math>f_j</math> as well as determine which mapping function corresponds to each of the <math>m</math> observations. | The function regression data comes in the form of <math>(x_i,y_i),i=1,...,m</math> pairs. However, unlike typical regression problems, there are multiple <math>f_j(x),j=1,...,n</math> mapping functions, so the goal is to recover both the mapping functions <math>f_j</math> as well as determine which mapping function corresponds to each of the <math>m</math> observations. | ||
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The second image classification data set is the Kaggle Fashion Product experiment. This data set has more observations than the "colored-MNIST" data and consists of pictures labelled with either the “Gender”, “Category”, and “Color” of the clothing item. | The second image classification data set is the Kaggle Fashion Product experiment. This data set has more observations than the "colored-MNIST" data and consists of pictures labelled with either the “Gender”, “Category”, and “Color” of the clothing item. | ||
==Metrics of Confusing Supervised Learning== | |||
==Results== | |||
==Data== | ==Data== | ||
Revision as of 16:48, 15 November 2020
Task Understanding from Confusing Multi-task Data
Presented By aslkdfj;awekrf
1. Introduction
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Hello
[math]\displaystyle{ \begin{align*} e & = \pi = \sqrt{g} \end{align*} }[/math]
2. Related Work
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[math]\displaystyle{ \begin{align*} e & = \text{Hellow}\\ & = \dfrac{123}{4}\\ \end{align*} }[/math]
[math]\displaystyle{
\begin{align*}
h+1 & = \dfrac{abc}{\text{def}}\\
& = \dfrac{123}{4}\\
\end{align*}
}[/math]
Experiment
Setup
3 data sets are used to compare CSL to existing methods, 1 function regression task and 2 image classification tasks.
The function regression data comes in the form of [math]\displaystyle{ (x_i,y_i),i=1,...,m }[/math] pairs. However, unlike typical regression problems, there are multiple [math]\displaystyle{ f_j(x),j=1,...,n }[/math] mapping functions, so the goal is to recover both the mapping functions [math]\displaystyle{ f_j }[/math] as well as determine which mapping function corresponds to each of the [math]\displaystyle{ m }[/math] observations.
The first image classification data set consists of the MNIST digit data that has been colored. Each observation in this modified set consists of a colored image ([math]\displaystyle{ x_i }[/math]) and either the color, or the digit it represents ([math]\displaystyle{ y_i }[/math]). The goal is to recover the classification task ("color" or "digit") for each observation and construct the 2 classifiers for both tasks.
The second image classification data set is the Kaggle Fashion Product experiment. This data set has more observations than the "colored-MNIST" data and consists of pictures labelled with either the “Gender”, “Category”, and “Color” of the clothing item.