research
∙
05/12/2021
Slower is Better: Revisiting the Forgetting Mechanism in LSTM for Slower Information Decay
Sequential information contains short- to long-range dependencies; howev...
share
research
∙
09/27/2020
Multi-timescale representation learning in LSTM Language Models
Although neural language models are effective at capturing statistics of...
research
∙
08/30/2019
A single-layer RNN can approximate stacked and bidirectional RNNs, and topologies in between
To enhance the expressiveness and representational capacity of recurrent...
research
∙
09/11/2018
Clinically Deployed Distributed Magnetic Resonance Imaging Reconstruction: Application to Pediatric Knee Imaging
Magnetic resonance imaging is capable of producing volumetric images wit...
research
∙
05/30/2017
Efficient, sparse representation of manifold distance matrices for classical scaling
Geodesic distance matrices can reveal shape properties that are largely ...
research
∙
05/30/2017
Sparse and low-rank approximations of large symmetric matrices using biharmonic interpolation
Symmetric matrices are widely used in machine learning problems such as ...
research
∙
09/29/2016
A Searchlight Factor Model Approach for Locating Shared Information in Multi-Subject fMRI Analysis
There is a growing interest in joint multi-subject fMRI analysis. The ch...
research
∙
08/17/2016
A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation
Finding the most effective way to aggregate multi-subject fMRI data is a...
research
∙
08/16/2016
Enabling Factor Analysis on Thousand-Subject Neuroimaging Datasets
The scale of functional magnetic resonance image data is rapidly increas...
research
∙
07/01/2016