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Transfer Learning for Fine-grained Classification Using Semi-supervised Learning and Visual Transformers
This task is particularly challenging in scenarios where data is scarce.
Design of Dynamic Experiments: A Data-Driven Methodology for the Optimization of Time-Varying Processes
ABSTRACT: A new methodology for the design of experiments is presented that provides a way to optimize the operation of a variety of batch and semibatch or fed-batch processes without the use of a knowledge-driven or fundamental model describing the inner workings of the process, when one or more time-varying decision variables must be selected.
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