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Found 3 papers, 3 papers with code
SHiFT: An Efficient, Flexible Search Engine for Transfer Learning
Transfer learning can be seen as a data- and compute-efficient alternative to training models from scratch.
Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise
In our experience of working with domain experts who are using today's AutoML systems, a common problem we encountered is what we call "unrealistic expectations" -- when users are facing a very challenging task with a noisy data acquisition process, while being expected to achieve startlingly high accuracy with machine learning (ML).
Iterative Correction of Sensor Degradation and a Bayesian Multi-Sensor Data Fusion Method
We present a novel method for inferring ground-truth signal from multiple degraded signals, affected by different amounts of sensor exposure.
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