5 papers · 1 filter
Robust Molecular Property Prediction via Densifying Scarce Labeled Data
Jina Kim, Jeffrey Willette, Bruno Andreis +1
A widely recognized limitation of molecular prediction models is their reliance on structures observed in the training data, resulting in poor generalization to out-of-distribution…
Instruction-Guided Autoregressive Neural Network Parameter Generation
Soro Bedionita, Bruno Andreis, Song Chong +1
Learning to generate neural network parameters conditioned on task descriptions and architecture specifications is pivotal for advancing model adaptability and transfer learning. E…
Set-based Neural Network Encoding Without Weight Tying
Bruno Andreis, Soro Bedionita, Philip H. S. Torr +1
We propose a neural network weight encoding method for network property prediction that utilizes set-to-set and set-to-vector functions to efficiently encode neural network paramet…
Diffusion-Based Neural Network Weights Generation
Bedionita Soro, Bruno Andreis, Hayeon Lee +4
Transfer learning has gained significant attention in recent deep learning research due to its ability to accelerate convergence and enhance performance on new tasks. However, its…
Set-based Meta-Interpolation for Few-Task Meta-Learning
Seanie Lee, Bruno Andreis, Kenji Kawaguchi +2
Meta-learning approaches enable machine learning systems to adapt to new tasks given few examples by leveraging knowledge from related tasks. However, a large number of meta-traini…