Search Results for author:
Found 6 papers, 4 papers with code
Mind Reader: Reconstructing complex images from brain activities
Instead of training models from scratch to find a latent space shared by the three modalities, we encode fMRI signals into this pre-aligned latent space.
SplitMixer: Fat Trimmed From MLP-like Models
We show, both theoretically and experimentally, that SplitMixer performs on par with the state-of-the-art MLP-like models while having a significantly lower number of parameters and FLOPS.
Deep Representations for Time-varying Brain Datasets
Finding an appropriate representation of dynamic activities in the brain is crucial for many downstream applications.
Learning Interpretable Models for Coupled Networks Under Domain Constraints
This not only leads to a cleaner way of applying network constraints but also provides a more scalable solution when network connectivity is sparse.
White Noise Analysis of Neural Networks
A white noise analysis of modern deep neural networks is presented to unveil their biases at the whole network level or the single neuron level.
Where's YOUR focus: Personalized Attention
In this work, we present the novel idea of personalized attention prediction and develop Personalized Attention Network (PANet), a convolutional network that predicts saliency in images with personal preference.
