4 citations · 4 across the 12 of their papers we have counts for
6 papers · 1 filter
Improving Medical VQA through Trajectory-Aware Process Supervision
Halil Ibrahim Gulluk, Olivier Gevaert
Reasoning capabilities are crucial for reliable medical visual question answering (VQA); however, existing datasets rarely include reasoning explanations. We address this by genera…
SemEnrich: Self-Supervised Semantic Enrichment of Radiology Reports for Vision-Language Learning
Halil Ibrahim Gulluk, Olivier Gevaert
Medical vision-language datasets are often limited in size and biased toward negative findings, as clinicians report abnormalities mostly but might omit some positive/neutral findi…
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data
Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng +3
Language-supervised vision models have recently attracted great attention in computer vision. A common approach to build such models is to use contrastive learning on paired data a…
Globally Optimal Training of Neural Networks with Threshold Activation Functions
Tolga Ergen, Halil Ibrahim Gulluk, Jonathan Lacotte +1
Threshold activation functions are highly preferable in neural networks due to their efficiency in hardware implementations. Moreover, their mode of operation is more interpretable…
Towards Sample-efficient Overparameterized Meta-learning
Yue Sun, Adhyyan Narang, Halil Ibrahim Gulluk +2
An overarching goal in machine learning is to build a generalizable model with few samples. To this end, overparameterization has been the subject of immense interest to explain th…
Sample Efficient Subspace-based Representations for Nonlinear Meta-Learning
Halil Ibrahim Gulluk, Yue Sun, Samet Oymak +1
Constructing good representations is critical for learning complex tasks in a sample efficient manner. In the context of meta-learning, representations can be constructed from comm…