1 citations · 1 across the 6 of their papers we have counts for
8 papers
Learning Model Representations Using Publicly Available Model Hubs
Damian Falk, Konstantin Schürholt, Konstantinos Tzevelekakis +2
The weights of neural networks have emerged as a novel data modality, giving rise to the field of weight space learning. A central challenge in this area is that learning meaningfu…
Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights
Zhiyuan Liang, Dongwen Tang, Yuhao Zhou +11
Modern Parameter-Efficient Fine-Tuning (PEFT) methods such as low-rank adaptation (LoRA) reduce the cost of customizing large language models (LLMs), yet still require a separate o…
Dense Air Pollution Estimation from Sparse in-situ Measurements and Satellite Data
Ruben Gonzalez Avilés, Linus Scheibenreif, Damian Borth
This paper addresses the critical environmental challenge of estimating ambient Nitrogen Dioxide (NO) concentrations, a key issue in public health and environmental policy. Exi…
Hyperspectral Vision Transformers for Greenhouse Gas Estimations from Space
Ruben Gonzalez Avilés, Linus Scheibenreif, Nassim Ait Ali Braham +8
Hyperspectral imaging provides detailed spectral information and holds significant potential for monitoring of greenhouse gases (GHGs). However, its application is constrained by l…
A Model Zoo of Vision Transformers
Damian Falk, Léo Meynent, Florence Pfammatter +2
The availability of large, structured populations of neural networks - called 'model zoos' - has led to the development of a multitude of downstream tasks ranging from model analys…
The Impact of Model Zoo Size and Composition on Weight Space Learning
Damian Falk, Konstantin Schürholt, Damian Borth
Re-using trained neural network models is a common strategy to reduce training cost and transfer knowledge. Weight space learning - using the weights of trained models as data moda…