20 research outputs found
Why musical memory can be preserved in advanced Alzheimer's disease
Musical memory is relatively preserved in Alzheimer's disease and other dementias. In a 7 Tesla functional MRI study employing multi-voxel pattern analysis, Jacobsen et al. identify brain regions encoding long-term musical memory in young healthy controls, and show that these same regions display relatively little atrophy and hypometabolism in patients with Alzheimer's disease.See Clark and Warren (doi:10.1093/brain/awv148) for a scientific commentary on this article.
Musical memory is relatively preserved in Alzheimer's disease and other dementias. In a 7 Tesla functional MRI study employing multi-voxel pattern analysis, Jacobsen et al. identify brain regions encoding long-term musical memory in young healthy controls, and show that these same regions display relatively little atrophy and hypometabolism in patients with Alzheimer's disease.See Clark and Warren (doi:10.1093/awv148) for a scientific commentary on this article
Dynamic Steerable Blocks in Deep Residual Networks
Filters in convolutional networks are typically parameterized in a pixel
basis, that does not take prior knowledge about the visual world into account.
We investigate the generalized notion of frames designed with image properties
in mind, as alternatives to this parametrization. We show that frame-based
ResNets and Densenets can improve performance on Cifar-10+ consistently, while
having additional pleasant properties like steerability. By exploiting these
transformation properties explicitly, we arrive at dynamic steerable blocks.
They are an extension of residual blocks, that are able to seamlessly transform
filters under pre-defined transformations, conditioned on the input at training
and inference time. Dynamic steerable blocks learn the degree of invariance
from data and locally adapt filters, allowing them to apply a different
geometrical variant of the same filter to each location of the feature map.
When evaluated on the Berkeley Segmentation contour detection dataset, our
approach outperforms all competing approaches that do not utilize pre-training.
Our results highlight the benefits of image-based regularization to deep
networks