Evaluation of Improvements to the Location Corrections through Differential Networks (LOCD-IN) System

2019 ◽  
Author(s):  
Russell Gilabert ◽  
Evan Dill ◽  
Maarten Uijt de Haag
Keyword(s):  
Differential Networks

scholarly journals Testing differential networks with applications to the detection of gene-gene interactions

Biometrika ◽  
2015 ◽  
Vol 102 (2) ◽  
pp. 247-266 ◽  
Author(s):  
Yin Xia ◽  
Tianxi Cai ◽  
T. Tony Cai
Keyword(s):  
Gene Interactions ◽  
Differential Networks

scholarly journals Revealing Pathway Dynamics in Heart Diseases by Analyzing Multiple Differential Networks

2015 ◽  
Vol 11 (6) ◽  
pp. e1004332 ◽  
Author(s):  
Xiaoke Ma ◽  
Long Gao ◽  
Georgios Karamanlidis ◽  
Peng Gao ◽  
Chi Fung Lee ◽  
...  
Keyword(s):  
Heart Diseases ◽  
Differential Networks

Passive somatosensory discrimination tasks in healthy volunteers: Differential networks involved in familiar versus unfamiliar shape and length discrimination

NeuroImage ◽  
2005 ◽  
Vol 26 (2) ◽  
pp. 441-453 ◽  
Author(s):  
A VANDEWINCKEL ◽  
S SUNAERT ◽  
N WENDEROTH ◽  
R PEETERS ◽  
P VANHECKE ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Differential Networks

scholarly journals Differential Networks for Visual Question Answering

2019 ◽  
Vol 33 ◽  
pp. 8997-9004 ◽  
Author(s):  
Chenfei Wu ◽  
Jinlai Liu ◽  
Xiaojie Wang ◽  
Ruifan Li
Keyword(s):  
Question Answering ◽  
Image Feature ◽  
Visual Question Answering ◽  
Key Points ◽  
Potential Applications ◽  
Observation Noise ◽  
The Difference ◽  
Difference Operation ◽  
Novel Model ◽  
Differential Networks

The task of Visual Question Answering (VQA) has emerged in recent years for its potential applications. To address the VQA task, the model should fuse feature elements from both images and questions efficiently. Existing models fuse image feature element vi and question feature element qi directly, such as an element product viqi. Those solutions largely ignore the following two key points: 1) Whether vi and qi are in the same space. 2) How to reduce the observation noises in vi and qi. We argue that two differences between those two feature elements themselves, like (vi − vj) and (qi −qj), are more probably in the same space. And the difference operation would be beneficial to reduce observation noise. To achieve this, we first propose Differential Networks (DN), a novel plug-and-play module which enables differences between pair-wise feature elements. With the tool of DN, we then propose DN based Fusion (DF), a novel model for VQA task. We achieve state-of-the-art results on four publicly available datasets. Ablation studies also show the effectiveness of difference operations in DF model.

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