Search Results for author:
Found 5 papers, 3 papers with code
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution
Leveraging Hyena's new long-range capabilities, we present HyenaDNA, a genomic foundation model pretrained on the human reference genome with context lengths of up to 1 million tokens at the single nucleotide-level - an up to 500x increase over previous dense attention-based models.
S4ND: Modeling Images and Videos as Multidimensional Signals Using State Spaces
On ImageNet-1k, S4ND exceeds the performance of a Vision Transformer baseline by $1. 5\%$ when training with a $1$D sequence of patches, and matches ConvNeXt when modeling images in $2$D.
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction
Thus overall, this work not only yields insights into the computational mechanisms underlying the striking predictive capabilities of the retina, but also places the framework of deep networks as neuroscientific models on firmer theoretical foundations, by providing a new roadmap to go beyond comparing neural representations to extracting and understand computational mechanisms.
Revealing computational mechanisms of retinal prediction via model reduction
Thus overall, this work not only yields insights into the computational mechanisms underlying the striking predictive capabilities of the retina, but also places the framework of deep networks as neuroscientific models on firmer theoretical foundations, by providing a new roadmap to go beyond comparing neural representations to extracting and understand computational mechanisms.
Deep Learning Models of the Retinal Response to Natural Scenes
Here we demonstrate that deep convolutional neural networks (CNNs) capture retinal responses to natural scenes nearly to within the variability of a cell's response, and are markedly more accurate than linear-nonlinear (LN) models and Generalized Linear Models (GLMs).
