Modality-agnostic decoders leverage modality-invariant representations in human subjects' brain activity to predict stimuli irrespective of their modality (image, text, mental imagery).
The ability to predict brain activity from words before they occur can be explained by information shared between neighbouring words, without requiring next-word prediction by the brain.
Abstract: Accurate segmentation of pulmonary infection regions is critical for diagnosing respiratory diseases such as COVID-19 and pneumonia. Although recent deep learning approaches have achieved ...
Summary: Meta’s Fundamental AI Research team has unveiled TRIBE, a groundbreaking foundation model designed to predict how the human brain processes visual and auditory stimuli. Trained on massive ...
Spec-Bench is a comprehensive benchmark designed for assessing Speculative Decoding methods across diverse scenarios. Based on Spec-Bench, we aim to establish and maintain a unified evaluation ...
Abstract: In deep learning-based dehazing strategies, attention mechanisms are widely used to refine feature representations and improve overall performance. However, conventional contextual attention ...
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