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cs.LG2022
SHiFT: An Efficient, Flexible Search Engine for Transfer Learning
Cedric Renggli, Xiaozhe Yao, Luka Kolar +3
Transfer learning can be seen as a data- and compute-efficient alternative to training models from scratch. The emergence of rich model repositories, such as TensorFlow Hub, enable…
cs.LG2020
Automatic Feasibility Study via Data Quality Analysis for ML: A Case-Study on Label Noise
Cedric Renggli, Luka Rimanic, Luka Kolar +2
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…
cs.LG2020★ 4 cited
Iterative Correction of Sensor Degradation and a Bayesian Multi-Sensor Data Fusion Method
Luka Kolar, Rok Šikonja, Lenart Treven
We present a novel method for inferring ground-truth signal from multiple degraded signals, affected by different amounts of sensor exposure. The algorithm learns a multiplicative…