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The study demonstrates that changing the network architecture and introducing specialized objective functions can significantly speed up the training process and reduce the risk of overfitting. Scientists emphasize the importance of empirical tests on a variety of tasks to assess the resilience of methods to noise and data variability.
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The potential of such approaches is especially high for medicine, robotics, and environmental monitoring, where access to large data sets is limited or ethical restrictions prevent information collection. In the future, the team plans to integrate new techniques into commercial platforms.
