4 citations · 4 across the 4 of their papers we have counts for
6 papers
SCRIBE: Scalable Contextual Representations of Interpretable Brain-Grounded Semantics
Camila Kolling, Omer Moussa, Emin Çelik +1
Understanding how the human brain constructs meaning from real-world language remains a fundamental challenge, as semantics depend on context that varies in natural settings. To en…
RABBiT: Rapidly adaptive BOLD foundation model via brain-tuning for accurate zero-shot and few-shot prediction of speech-elicited responses in the brain
Omer Moussa, Mariya Toneva
Language understanding in the brain is context-dependent, varying across experimental stimuli and individuals, which makes it difficult to build computational models that generaliz…
Brain-tuning Improves Generalizability and Efficiency of Brain Alignment in Speech Models
Omer Moussa, Mariya Toneva
Pretrained language models are remarkably effective in aligning with human brain responses elicited by natural language stimuli, positioning them as promising model organisms for s…
Brain-tuned Speech Models Better Reflect Speech Processing Stages in the Brain
Omer Moussa, Mariya Toneva
Pretrained self-supervised speech models excel in speech tasks but do not reflect the hierarchy of human speech processing, as they encode rich semantics in middle layers and poor…
Improving Semantic Understanding in Speech Language Models via Brain-tuning
Omer Moussa, Dietrich Klakow, Mariya Toneva
Speech language models align with human brain responses to natural language to an impressive degree. However, current models rely heavily on low-level speech features, indicating t…
The Right Losses for the Right Gains: Improving the Semantic Consistency of Deep Text-to-Image Generation with Distribution-Sensitive Losses
Mahmoud Ahmed, Omer Moussa, Ismail Shaheen +5
One of the major challenges in training deep neural networks for text-to-image generation is the significant linguistic discrepancy between ground-truth captions of each image in m…