SuperExe2bat.57
a huge amount of research is conducted in the absence of real world problems and constraints.
public data.
Our motivation for this work is the following observation. People tend to annotate machine learning challenges with large quantities of data. However, acquiring a large amount of data can be expensive or even impossible.
For example, large datasets can be expensive to acquire and annotate.
As a result, people often rely on synthetic data, which is generated artificially. As we will show in, this does not actually transfer to the real world and can have negative effects on the performance of machine learning models.
Another situation where we sometimes see large quantities of data is deep learning challenges. However, in these challenges, the number of data points does not tend to be a large number. In this paper, we will address this problem by developing a dataset that allows machine learning researchers to evaluate their models on datasets with a limited number of data points.
We will introduce a dataset that has data points that are representative of real world problems and constraints. We will refer to this dataset as the IAML dataset.
Note that in our dataset, the number of data points in each set is small (128). However, there are thousands of data points in total.
These data points were generated using 2 different machine learning methods. We show that these data points are also representative of real world datasets. We will further explain the experimental setup in.
In this paper, we will focus on the following 4 topics.
is a dataset of over 3, data points.
In, we will show that the IAML dataset is representative of real-world data.
In, we will show that the IAML dataset is a large scale dataset.
In, we will evaluate some machine learning models and show that they perform well on the IAML dataset.
In, we will show how we generate data for a large number of data points.
. We will not consider. The datasets are available for download at
.
In this paper, we will discuss the datasets. We will introduce an evaluation method that we have developed for the datasets.
Finally, we will introduce the IAML dataset, which will be our main focus. We will also explain how the IAML dataset can be used to evaluate machine learning models.
In, we will briefly discuss the data collection process.
In, we will discuss
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