Active Learning for experimental exploration: Unterschied zwischen den Versionen
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|kurzfassung= | |kurzfassung=A ranking is the result of running an experiment, a set of encoders is applied to an | ||
experimental condition (dataset, model, tuning, scoring) and are then ranked according to | |||
their performance. | |||
To draw conclusions about the performance of the encoders for a set of experimental | |||
conditions, one can aggregate the rankings into a consensus ranking. (i.e. taking the median | |||
rank) | |||
The goal of the thesis is to explore the space of consensus rankings and find all possible | |||
consensus rankings. | |||
However, running an experiment is a very time-consuming task. Therefore we utilize Active | |||
Learning, to avoid running unnecessary experiments. In Active Learning, the learner can | |||
choose the data it is trained on and achieves greater accuracy with fewer labeled data. | |||
}} | }} | ||
Aktuelle Version vom 8. Mai 2023, 17:53 Uhr
| Vortragende(r) | Steven Lorenz | |
|---|---|---|
| Vortragstyp | Proposal | |
| Betreuer(in) | Federico Matteucci | |
| Termin | Fr 12. Mai 2023 | |
| Vortragsmodus | in Präsenz | |
| Kurzfassung | A ranking is the result of running an experiment, a set of encoders is applied to an
experimental condition (dataset, model, tuning, scoring) and are then ranked according to their performance. To draw conclusions about the performance of the encoders for a set of experimental conditions, one can aggregate the rankings into a consensus ranking. (i.e. taking the median rank) The goal of the thesis is to explore the space of consensus rankings and find all possible consensus rankings. However, running an experiment is a very time-consuming task. Therefore we utilize Active Learning, to avoid running unnecessary experiments. In Active Learning, the learner can choose the data it is trained on and achieves greater accuracy with fewer labeled data. | |