Examples of using Synthetic data in English and their translations into Chinese
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It's intended to help measure bias in AI comment classification systems, which Jigsaw and others have historically measured using synthetic data from template sentences.
They used conditional variational autoencoders, or"generative models," machine learning models which learn to generate synthetic data similar to that which it is trained on.
To train the core models, the team mostly used synthetic data from a simulated environment.
This 2D-only perspective inevitably limits their practical usages in many fields, such as synthetic data generation, robotic learning, visual reality, and the gaming industry.”.
One can also define a random forest dissimilarity measure between unlabeled data: the idea is to construct a random forest predictor that distinguishes the“observed” data from suitably generated synthetic data.
Synthetic data models will ease bottlenecks.
Synthetic data is useful in many situations.
See the definition of synthetic data in OECD.
Synthetic data are generated from a data model built on real data. .
An even more extreme idea is to train DI models with synthetic data.
The system learns the model and subsequently produces an entire database of synthetic data.
Synthetic data is information which is manufactured artificially rather than data generated from real world events.
One way of adding an extra level of sophisticated anonymisation to data is introducing synthetic data.
Reverie, that can help lower the cost of training data through tools for generating synthetic data.
In many cases, it is more efficient and cost-effective to create synthetic data rather than to collect real-world data. .
As data becomes more valuable, advances in synthetic data and other“lean” and“augmented” data learning techniques will accelerate.
One attempting to create more realistic data, the other attempting to get better at discerning between real and synthetic data.
Sparse inverse covariance estimation: example on synthetic data showing some recovery of a structure, and comparing to other covariance estimators.
The use of synthetic data[5]may be considered as one possible solution to minimise the amount of personal data processed by AI applications.
The system has a series of deep neural networks that perform perception, planning, and control, and these networks are trained entirely on synthetic data.