scholarly journals Adaptive Region Embedding for Text Classification

2019 ◽  
Vol 33 ◽  
pp. 7314-7321
Author(s):  
Liuyu Xiang ◽  
Xiaoming Jin ◽  
Lan Yi ◽  
Guiguang Ding
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Text Classification ◽  
State Of The Art ◽  
Context Information ◽  
Great Success ◽  
Recurrent Networks ◽  
Learning Models ◽  
Benchmark Datasets

Deep learning models such as convolutional neural networks and recurrent networks are widely applied in text classification. In spite of their great success, most deep learning models neglect the importance of modeling context information, which is crucial to understanding texts. In this work, we propose the Adaptive Region Embedding to learn context representation to improve text classification. Specifically, a metanetwork is learned to generate a context matrix for each region, and each word interacts with its corresponding context matrix to produce the regional representation for further classification. Compared to previous models that are designed to capture context information, our model contains less parameters and is more flexible. We extensively evaluate our method on 8 benchmark datasets for text classification. The experimental results prove that our method achieves state-of-the-art performances and effectively avoids word ambiguity.

scholarly journals Sentiment Classification Using Convolutional Neural Networks

2019 ◽  
Vol 9 (11) ◽  
pp. 2347 ◽  
Author(s):  
Hannah Kim ◽  
Young-Seob Jeong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Text Classification ◽  
State Of The Art ◽  
Sentiment Classification ◽  
Learning Models ◽  
Text Data ◽  
Textual Data ◽  
Better Than

As the number of textual data is exponentially increasing, it becomes more important to develop models to analyze the text data automatically. The texts may contain various labels such as gender, age, country, sentiment, and so forth. Using such labels may bring benefits to some industrial fields, so many studies of text classification have appeared. Recently, the Convolutional Neural Network (CNN) has been adopted for the task of text classification and has shown quite successful results. In this paper, we propose convolutional neural networks for the task of sentiment classification. Through experiments with three well-known datasets, we show that employing consecutive convolutional layers is effective for relatively longer texts, and our networks are better than other state-of-the-art deep learning models.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

scholarly journals Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

2021 ◽  
Vol 11 (5) ◽  
pp. 2284
Author(s):  
Asma Maqsood ◽  
Muhammad Shahid Farid ◽  
Muhammad Hassan Khan ◽  
Marcin Grzegorzek
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Red Blood Cells ◽  
Convolutional Neural Networks ◽  
Blood Cells ◽  
Superior Performance ◽  
Learning Models ◽  
Infected Female ◽  
The World ◽  
Augmentation Techniques

Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedious work for its detection. Recently, some automated methods have been proposed that exploit hand-crafted feature extraction techniques however, their accuracies are not reliable. Deep learning approaches modernize the world with their superior performance. Convolutional Neural Networks (CNN) are vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer healthcare experts. The contributions of this paper are two-fold. First, we evaluate the performance of different existing deep learning models for efficient malaria detection. Second, we propose a customized CNN model that outperforms all observed deep learning models. It exploits the bilateral filtering and image augmentation techniques for highlighting features of red blood cells before training the model. Due to image augmentation techniques, the customized CNN model is generalized and avoids over-fitting. All experimental evaluations are performed on the benchmark NIH Malaria Dataset, and the results reveal that the proposed algorithm is 96.82% accurate in detecting malaria from the microscopic blood smears.

Train Deep Learning Models using subsurface geological images datasets

2021 ◽  
Author(s):  
Ramy Abdallah ◽  
Clare E. Bond ◽  
Robert W.H. Butler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Models ◽  
Structural Interpretation ◽  
Performance Accuracy ◽  
Learning Techniques ◽  
Machine Learning Models

&lt;p&gt;Machine learning is being presented as a new solution for a wide range of geoscience problems. Primarily machine learning has been used for 3D seismic data processing, seismic facies analysis and well log data correlation. The rapid development in technology with open-source artificial intelligence libraries and the accessibility of affordable computer graphics processing units (GPU) makes the application of machine learning in geosciences increasingly tractable. However, the application of artificial intelligence in structural interpretation workflows of subsurface datasets is still ambiguous. This study aims to use machine learning techniques to classify images of folds and fold-thrust structures. Here we show that convolutional neural networks (CNNs) as supervised deep learning techniques provide excellent algorithms to discriminate between geological image datasets. Four different datasets of images have been used to train and test the machine learning models. These four datasets are a seismic character dataset with five classes (faults, folds, salt, flat layers and basement), folds types with three classes (buckle, chevron and conjugate), fault types with three classes (normal, reverse and thrust) and fold-thrust geometries with three classes (fault bend fold, fault propagation fold and detachment fold). These image datasets are used to investigate three machine learning models. One Feedforward linear neural network model and two convolutional neural networks models (Convolution 2d layer transforms sequential model and Residual block model (ResNet with 9, 34, and 50 layers)). Validation and testing datasets forms a critical part of testing the model&amp;#8217;s performance accuracy. The ResNet model records the highest performance accuracy score, of the machine learning models tested. Our CNN image classification model analysis provides a framework for applying machine learning to increase structural interpretation efficiency, and shows that CNN classification models can be applied effectively to geoscience problems. The study provides a starting point to apply unsupervised machine learning approaches to sub-surface structural interpretation workflows.&lt;/p&gt;

scholarly journals Visualizing Deep Networks by Optimizing with Integrated Gradients

2020 ◽  
Vol 34 (07) ◽  
pp. 11890-11898
Author(s):  
Zhongang Qi ◽  
Saeed Khorram ◽  
Li Fuxin
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
User Needs ◽  
Learning Models ◽  
Local Optima ◽  
Popular Approach ◽  
Deep Network ◽  
Deep Networks ◽  
Benchmark Datasets ◽  
Descent Directions

Understanding and interpreting the decisions made by deep learning models is valuable in many domains. In computer vision, computing heatmaps from a deep network is a popular approach for visualizing and understanding deep networks. However, heatmaps that do not correlate with the network may mislead human, hence the performance of heatmaps in providing a faithful explanation to the underlying deep network is crucial. In this paper, we propose I-GOS, which optimizes for a heatmap so that the classification scores on the masked image would maximally decrease. The main novelty of the approach is to compute descent directions based on the integrated gradients instead of the normal gradient, which avoids local optima and speeds up convergence. Compared with previous approaches, our method can flexibly compute heatmaps at any resolution for different user needs. Extensive experiments on several benchmark datasets show that the heatmaps produced by our approach are more correlated with the decision of the underlying deep network, in comparison with other state-of-the-art approaches.

scholarly journals Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks

2016 ◽  
Vol 21 (9) ◽  
pp. 998-1003 ◽  
Author(s):  
Oliver Dürr ◽  
Beate Sick
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Single Cell ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Misclassification Rate ◽  
Support Vector ◽  
Learning Methods ◽  
Phenotype Classification

Deep learning methods are currently outperforming traditional state-of-the-art computer vision algorithms in diverse applications and recently even surpassed human performance in object recognition. Here we demonstrate the potential of deep learning methods to high-content screening–based phenotype classification. We trained a deep learning classifier in the form of convolutional neural networks with approximately 40,000 publicly available single-cell images from samples treated with compounds from four classes known to lead to different phenotypes. The input data consisted of multichannel images. The construction of appropriate feature definitions was part of the training and carried out by the convolutional network, without the need for expert knowledge or handcrafted features. We compare our results against the recent state-of-the-art pipeline in which predefined features are extracted from each cell using specialized software and then fed into various machine learning algorithms (support vector machine, Fisher linear discriminant, random forest) for classification. The performance of all classification approaches is evaluated on an untouched test image set with known phenotype classes. Compared to the best reference machine learning algorithm, the misclassification rate is reduced from 8.9% to 6.6%.

scholarly journals Self-Ensembling Attention Networks: Addressing Domain Shift for Semantic Segmentation

2019 ◽  
Vol 33 ◽  
pp. 5581-5588 ◽  
Author(s):  
Yonghao Xu ◽  
Bo Du ◽  
Lefei Zhang ◽  
Qian Zhang ◽  
Guoli Wang ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Great Success ◽  
Learning Models ◽  
Target Domain ◽  
Attention Networks ◽  
Source Domain ◽  
Benchmark Datasets ◽  
Different Levels

Recent years have witnessed the great success of deep learning models in semantic segmentation. Nevertheless, these models may not generalize well to unseen image domains due to the phenomenon of domain shift. Since pixel-level annotations are laborious to collect, developing algorithms which can adapt labeled data from source domain to target domain is of great significance. To this end, we propose self-ensembling attention networks to reduce the domain gap between different datasets. To the best of our knowledge, the proposed method is the first attempt to introduce selfensembling model to domain adaptation for semantic segmentation, which provides a different view on how to learn domain-invariant features. Besides, since different regions in the image usually correspond to different levels of domain gap, we introduce the attention mechanism into the proposed framework to generate attention-aware features, which are further utilized to guide the calculation of consistency loss in the target domain. Experiments on two benchmark datasets demonstrate that the proposed framework can yield competitive performance compared with the state of the art methods.

scholarly journals A Study of The Convolutional Neural Networks Applications

2019 ◽  
Vol 3 (2) ◽  
pp. 31-40 ◽  
Author(s):  
Ahmed Shamsaldin ◽  
Polla Fattah ◽  
Tarik Rashid ◽  
Nawzad Al-Salihi
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Face Recognition ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Text Classification ◽  
Scene Labeling ◽  
Real World Problems

At present, deep learning is widely used in a broad range of arenas. A convolutional neural networks (CNN) is becoming the star of deep learning as it gives the best and most precise results when cracking real-world problems. In this work, a brief description of the applications of CNNs in two areas will be presented: First, in computer vision, generally, that is, scene labeling, face recognition, action recognition, and image classification; Second, in natural language processing, that is, the fields of speech recognition and text classification.

scholarly journals Nonlinear Chen-Lee Chaotic System Based Deep Convolutional Generative Adversarial Nets for Chatter Diagnosis

2021 ◽  
Author(s):  
Ping-Huan Kuo ◽  
Po-Chien Luan ◽  
Yung-Ruen Tseng ◽  
Her-Terng Yau
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Chaotic System ◽  
Original Data ◽  
Training Data ◽  
Original Training ◽  
Chatter Detection ◽  
Learning Models ◽  
Training Strategy

Abstract Chatter has a direct effect on the precision and life of machine tools and its detection is a crucial issue in all metal machining processes. Traditional methods focus on how to extract discriminative features to help identify chatter. Nowadays, deep learning models have shown an extraordinary ability to extract data features which are their necessary fuel. In this study deep learning models have been substituted for more traditional methods. Chatter data are rare and valuable because the collecting process is extremely difficult. To solve this practical problem an innovative training strategy has been proposed that is combined with a modified convolutional neural network and deep convolutional generative adversarial nets. This improves chatter detection and classification. Convolutional neural networks can be effective chatter classifiers, and adversarial networks can act as generators that produce more data. The convolutional neural networks were trained using original data as well as by forged data produced by the generator. Original training data were collected and preprocessed by the Chen-Lee chaotic system. The adversarial training process used these data to create the generator and the generator could produce enough data to compensate for the lack of training data. The experimental results were compared with without a data generator and data augmentation. The proposed method had an accuracy of 95.3% on leave-one-out cross-validation over ten runs and surpassed other methods and models. The forged data were also compared with original training data as well as data produced by augmentation. The distribution shows that forged data had similar quality and characteristics to the original data. The proposed training strategy provides a high-quality deep learning chatter detection model.

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