2 citations · 2 across the 3 of their papers we have counts for
5 papers
Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need
Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles +1
Recent Continual Learning (CL) methods have combined pretrained Transformers with prompt tuning, a parameter-efficient fine-tuning (PEFT) technique. We argue that the choice of pro…
Renate: A Library for Real-World Continual Learning
Martin Wistuba, Martin Ferianc, Lukas Balles +2
Continual learning enables the incremental training of machine learning models on non-stationary data streams.While academic interest in the topic is high, there is little indicati…
Variational Boosted Soft Trees
Tristan Cinquin, Tammo Rukat, Philipp Schmidt +2
Gradient boosting machines (GBMs) based on decision trees consistently demonstrate state-of-the-art results on regression and classification tasks with tabular data, often outperfo…
Continual Learning with Transformers for Image Classification
Beyza Ermis, Giovanni Zappella, Martin Wistuba +2
In many real-world scenarios, data to train machine learning models become available over time. However, neural network models struggle to continually learn new concepts without fo…
Bank Card Usage Prediction Exploiting Geolocation Information
Martin Wistuba, Nghia Duong-Trung, Nicolas Schilling +1
We describe the solution of team ISMLL for the ECML-PKDD 2016 Discovery Challenge on Bank Card Usage for both tasks. Our solution is based on three pillars. Gradient boosted decisi…