most citedChameleon: Learning Model Initializations Across Tasks With Different Schemas

5 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2022

Few-shot human motion prediction for heterogeneous sensors

Rafael Rego Drumond, Lukas Brinkmeyer, Lars Schmidt-Thieme

Human motion prediction is a complex task as it involves forecasting variables over time on a graph of connected sensors. This is especially true in the case of few-shot learning,…

cs.AI2022

End-to-End Image-Based Fashion Recommendation

Shereen Elsayed, Lukas Brinkmeyer, Lars Schmidt-Thieme

In fashion-based recommendation settings, incorporating the item image features is considered a crucial factor, and it has shown significant improvements to many traditional models…

cs.LG2022★ 1 cited

Few-Shot Forecasting of Time-Series with Heterogeneous Channels

Lukas Brinkmeyer, Rafael Rego Drumond, Johannes Burchert +1

Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set. Leveraging learning experienc…

cs.LG2019★ 1 cited

HIDRA: Head Initialization across Dynamic targets for Robust Architectures

Rafael Rego Drumond, Lukas Brinkmeyer, Josif Grabocka +1

The performance of gradient-based optimization strategies depends heavily on the initial weights of the parametric model. Recent works show that there exist weight initializations…

cs.LG2019★ 5 cited

Chameleon: Learning Model Initializations Across Tasks With Different Schemas

Lukas Brinkmeyer, Rafael Rego Drumond, Randolf Scholz +2

Parametric models, and particularly neural networks, require weight initialization as a starting point for gradient-based optimization. Recent work shows that a specific initial pa…