7 papers
Meta-Learning Transformers to Improve In-Context Generalization
Lorenzo Braccaioli, Anna Vettoruzzo, Prabhant Singh +3
In-context learning enables transformer models to generalize to new tasks based solely on input prompts, without any need for weight updates. However, existing training paradigms t…
Automated Machine Learning for Unsupervised Tabular Tasks
Prabhant Singh, Pieter Gijsbers, Elif Ceren Gok Yildirim +2
In this work, we present LOTUS (Learning to Learn with Optimal Transport for Unsupervised Scenarios), a simple yet effective method to perform model selection for multiple unsuperv…
How NOT to benchmark your SITE metric: Beyond Static Leaderboards and Towards Realistic Evaluation
Prabhant Singh, Sibylle Hess, Joaquin Vanschoren
Transferability estimation metrics are used to find a high-performing pre-trained model for a given target task without fine-tuning models and without access to the source dataset.…
Analysis of Transferability Estimation Metrics for Surgical Phase Recognition
Prabhant Singh, Yiping Li, Yasmina Al Khalil
Fine-tuning pre-trained models has become a cornerstone of modern machine learning, allowing practitioners to achieve high performance with limited labeled data. In surgical video…
On Supernet Transfer Learning for Effective Task Adaptation
Prabhant Singh, Joaquin Vanschoren
Neural Architecture Search (NAS) methods have been shown to outperform hand-designed models and help to democratize AI. However, NAS methods often start from scratch with each new…
Occam's model: Selecting simpler representations for better transferability estimation
Prabhant Singh, Sibylle Hess, Joaquin Vanschoren
Fine-tuning models that have been pre-trained on large datasets has become a cornerstone of modern machine learning workflows. With the widespread availability of online model repo…