12 citations · 13 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 1 cited
Memento: Facilitating Effortless, Efficient, and Reliable ML Experiments
Zac Pullar-Strecker, Xinglong Chang, Liam Brydon +3
Running complex sets of machine learning experiments is challenging and time-consuming due to the lack of a unified framework. This leaves researchers forced to spend time implemen…
cs.LG2021★ 12 cited
Hitting the Target: Stopping Active Learning at the Cost-Based Optimum
Zac Pullar-Strecker, Katharina Dost, Eibe Frank +1
Active learning allows machine learning models to be trained using fewer labels while retaining similar performance to traditional supervised learning. An active learner selects th…