Source code classification using Neural Networks

2017 ◽  
Author(s):  
Shlok Gilda
Keyword(s):  
Neural Networks ◽  
Source Code

scholarly journals An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos

2020 ◽  
Vol 34 (01) ◽  
pp. 303-311 ◽  
Author(s):  
Sicheng Zhao ◽  
Yunsheng Ma ◽  
Yang Gu ◽  
Jufeng Yang ◽  
Tengfei Xing ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
State Of The Art ◽  
Source Code ◽  
Cross Entropy ◽  
Attention Network ◽  
Audio Features ◽  
End To End ◽  
3D Cnn ◽  
And Training

Emotion recognition in user-generated videos plays an important role in human-centered computing. Existing methods mainly employ traditional two-stage shallow pipeline, i.e. extracting visual and/or audio features and training classifiers. In this paper, we propose to recognize video emotions in an end-to-end manner based on convolutional neural networks (CNNs). Specifically, we develop a deep Visual-Audio Attention Network (VAANet), a novel architecture that integrates spatial, channel-wise, and temporal attentions into a visual 3D CNN and temporal attentions into an audio 2D CNN. Further, we design a special classification loss, i.e. polarity-consistent cross-entropy loss, based on the polarity-emotion hierarchy constraint to guide the attention generation. Extensive experiments conducted on the challenging VideoEmotion-8 and Ekman-6 datasets demonstrate that the proposed VAANet outperforms the state-of-the-art approaches for video emotion recognition. Our source code is released at: https://github.com/maysonma/VAANet.

scholarly journals Integrating Relation Constraints with Neural Relation Extractors

2020 ◽  
Vol 34 (05) ◽  
pp. 9442-9449
Author(s):  
Yuan Ye ◽  
Yansong Feng ◽  
Bingfeng Luo ◽  
Yuxuan Lai ◽  
Dongyan Zhao
Keyword(s):  
Neural Networks ◽  
Source Code ◽  
Relation Extraction ◽  
Rapid Progress ◽  
Post Processing ◽  
Unified Framework ◽  
Loss Term

Recent years have seen rapid progress in identifying predefined relationship between entity pairs using neural networks (NNs). However, such models often make predictions for each entity pair individually, thus often fail to solve the inconsistency among different predictions, which can be characterized by discrete relation constraints. These constraints are often defined over combinations of entity-relation-entity triples, since there often lack of explicitly well-defined type and cardinality requirements for the relations. In this paper, we propose a unified framework to integrate relation constraints with NNs by introducing a new loss term, Constraint Loss. Particularly, we develop two efficient methods to capture how well the local predictions from multiple instance pairs satisfy the relation constraints. Experiments on both English and Chinese datasets show that our approach can help NNs learn from discrete relation constraints to reduce inconsistency among local predictions, and outperform popular neural relation extraction (NRE) models even enhanced with extra post-processing. Our source code and datasets will be released at https://github.com/PKUYeYuan/Constraint-Loss-AAAI-2020.

scholarly journals Segmenting Microarrays with Deep Neural Networks

2015 ◽  
Author(s):  
Andrew L Jones
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Biological Material ◽  
Deep Neural Networks ◽  
Source Code ◽  
Segmentation Problem

Microarray images consist of thousands of spots, each of which corresponds to a different biological material. The microarray segmentation problem is to work out which pixels belong to which spots, even in presence of noise and corruption. We propose a solution based on deep neural networks, which achieves excellent results both on simulated and experimental data. We have made the source code for our solution available on Github under a permissive license.

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