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10/09/2020
A Vertex Cut based Framework for Load Balancing and Parallelism Optimization in Multi-core Systems
High-level applications, such as machine learning, are evolving from sim...
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07/20/2020
Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks
The terms multi-task learning and multitasking are easily confused. Mult...
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12/25/2019
Deep Graph Similarity Learning: A Survey
In many domains where data are represented as graphs, learning a similar...
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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...
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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...
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09/04/2018
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers
As deep learning methods form a critical part in commercially important ...
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07/16/2018
Scheduling Computation Graphs of Deep Learning Models on Manycore CPUs
For a deep learning model, efficient execution of its computation graph ...
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02/07/2018
Learning Role-based Graph Embeddings
Random walks are at the heart of many existing network embedding methods...
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11/17/2017
Segmenting Brain Tumors with Symmetry
We explore encoding brain symmetry into a neural network for a brain tum...
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10/25/2017
Inductive Representation Learning in Large Attributed Graphs
Graphs (networks) are ubiquitous and allow us to model entities (nodes) ...
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09/14/2017
A Framework for Generalizing Graph-based Representation Learning Methods
Random walks are at the heart of many existing deep learning algorithms ...
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10/04/2016
Revisiting Role Discovery in Networks: From Node to Edge Roles
Previous work in network analysis has focused on modeling the mixed-memb...
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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...
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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...
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08/16/2016
Enabling Factor Analysis on Thousand-Subject Neuroimaging Datasets
The scale of functional magnetic resonance image data is rapidly increas...
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06/13/2015
