1 citations · 1 across the 10 of their papers we have counts for
11 papers
WeightCLIP: Aligning Datasets and Models for Weight Space Learning
Aron Asefaw, Konstantinos Tzevelekakis, Damian Falk +2
Weight space learning aims to learn representations of neural network (NN) weights, enabling different downstream tasks. Existing approaches show promising performance, but lacking…
GeoSANE: Learning Geospatial Representations from Models, Not Data
Joelle Hanna, Damian Falk, Stella X. Yu +1
Recent advances in remote sensing have led to an increase in the number of available foundation models; each trained on different modalities, datasets, and objectives, yet capturin…
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…
A Model Zoo on Phase Transitions in Neural Networks
Konstantin Schürholt, Léo Meynent, Yefan Zhou +3
Using the weights of trained Neural Network (NN) models as data modality has recently gained traction as a research field - dubbed Weight Space Learning (WSL). Multiple recent work…
Structure Is Not Enough: Leveraging Behavior for Neural Network Weight Reconstruction
Léo Meynent, Ivan Melev, Konstantin Schürholt +2
The weights of neural networks (NNs) have recently gained prominence as a new data modality in machine learning, with applications ranging from accuracy and hyperparameter predicti…
Towards Scalable and Versatile Weight Space Learning
Konstantin Schürholt, Michael W. Mahoney, Damian Borth
Learning representations of well-trained neural network models holds the promise to provide an understanding of the inner workings of those models. However, previous work has eithe…