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Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision Transformers
To overcome the challenge presented by the co-occurrence of multiple categories in natural images, we introduce BrainSAIL (Semantic Attribution and Image Localization), a method for isolating specific neurally-activating visual concepts in images.
Reanimating Images using Neural Representations of Dynamic Stimuli
Our approach leverages state-of-the-art video diffusion models to decouple static image representation from motion generation, enabling us to utilize fMRI brain activity for a deeper understanding of human responses to dynamic visual stimuli.
DeepChrome 2.0: Investigating and Improving Architectures, Visualizations, & Experiments
Results from cross-cell prediction experiments, where the model is trained and tested on datasets of varying sizes, cell-types, and correlations, suggest the relationship between histone modification signals and gene expression is independent of cell type.
Hangul Fonts Dataset: a Hierarchical and Compositional Dataset for Investigating Learned Representations
Thus, HFD enables the identification of shortcomings in existing methods, a critical first step toward developing new machine learning algorithms to extract hierarchical and compositional structure in the context of naturalistic variability.
