scholarly journals A tutorial survey of architectures, algorithms, and applications for deep learning

2014 ◽  
Vol 3 ◽  
Author(s):  
Li Deng
Keyword(s):  
Deep Learning ◽  
Information Processing ◽  
Language Processing ◽  
Feature Learning ◽  
Representation Learning ◽  
Machine Learning Techniques ◽  
Major Work ◽  
Future Directions ◽  
Recent Developments ◽  
History Of

In this invited paper, my overview material on the same topic as presented in the plenary overview session of APSIPA-2011 and the tutorial material presented in the same conference [1] are expanded and updated to include more recent developments in deep learning. The previous and the updated materials cover both theory and applications, and analyze its future directions. The goal of this tutorial survey is to introduce the emerging area of deep learning or hierarchical learning to the APSIPA community. Deep learning refers to a class of machine learning techniques, developed largely since 2006, where many stages of non-linear information processing in hierarchical architectures are exploited for pattern classification and for feature learning. In the more recent literature, it is also connected to representation learning, which involves a hierarchy of features or concepts where higher-level concepts are defined from lower-level ones and where the same lower-level concepts help to define higher-level ones. In this tutorial survey, a brief history of deep learning research is discussed first. Then, a classificatory scheme is developed to analyze and summarize major work reported in the recent deep learning literature. Using this scheme, I provide a taxonomy-oriented survey on the existing deep architectures and algorithms in the literature, and categorize them into three classes: generative, discriminative, and hybrid. Three representative deep architectures – deep autoencoders, deep stacking networks with their generalization to the temporal domain (recurrent networks), and deep neural networks (pretrained with deep belief networks) – one in each of the three classes, are presented in more detail. Next, selected applications of deep learning are reviewed in broad areas of signal and information processing including audio/speech, image/vision, multimodality, language modeling, natural language processing, and information retrieval. Finally, future directions of deep learning are discussed and analyzed.

scholarly journals The Past, Present and Future of Cannabis sativa Tissue Culture

Plants ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 185
Author(s):  
Adrian S. Monthony ◽  
Serena R. Page ◽  
Mohsen Hesami ◽  
Andrew Maxwell P. Jones
Keyword(s):  
Tissue Culture ◽  
Disease Virus ◽  
Cannabis Sativa ◽  
Clonal Plants ◽  
Future Directions ◽  
The Past ◽  
Recent Developments ◽  
History Of ◽  
Drug Type

The recent legalization of Cannabis sativa L. in many regions has revealed a need for effective propagation and biotechnologies for the species. Micropropagation affords researchers and producers methods to rapidly propagate insect-/disease-/virus-free clonal plants and store germplasm and forms the basis for other biotechnologies. Despite this need, research in the area is limited due to the long history of prohibitions and restrictions. Existing literature has multiple limitations: many publications use hemp as a proxy for drug-type Cannabis when it is well established that there is significant genotype specificity; studies using drug-type cultivars are predominantly optimized using a single cultivar; most protocols have not been replicated by independent groups, and some attempts demonstrate a lack of reproducibility across genotypes. Due to culture decline and other problems, the multiplication phase of micropropagation (Stage 2) has not been fully developed in many reports. This review will provide a brief background on the history and botany of Cannabis as well as a comprehensive and critical summary of Cannabis tissue culture. Special attention will be paid to current challenges faced by researchers, the limitations of existing Cannabis micropropagation studies, and recent developments and future directions of Cannabis tissue culture technologies.

scholarly journals Medical Text Classification Using Hybrid Deep Learning Models with Multihead Attention

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Sunil Kumar Prabhakar ◽  
Dong-Ok Won
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Text Classification ◽  
Patient Information ◽  
Classification Accuracy ◽  
Learning Model ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Medical Text ◽  
Deep Learning Model

To unlock information present in clinical description, automatic medical text classification is highly useful in the arena of natural language processing (NLP). For medical text classification tasks, machine learning techniques seem to be quite effective; however, it requires extensive effort from human side, so that the labeled training data can be created. For clinical and translational research, a huge quantity of detailed patient information, such as disease status, lab tests, medication history, side effects, and treatment outcomes, has been collected in an electronic format, and it serves as a valuable data source for further analysis. Therefore, a huge quantity of detailed patient information is present in the medical text, and it is quite a huge challenge to process it efficiently. In this work, a medical text classification paradigm, using two novel deep learning architectures, is proposed to mitigate the human efforts. The first approach is that a quad channel hybrid long short-term memory (QC-LSTM) deep learning model is implemented utilizing four channels, and the second approach is that a hybrid bidirectional gated recurrent unit (BiGRU) deep learning model with multihead attention is developed and implemented successfully. The proposed methodology is validated on two medical text datasets, and a comprehensive analysis is conducted. The best results in terms of classification accuracy of 96.72% is obtained with the proposed QC-LSTM deep learning model, and a classification accuracy of 95.76% is obtained with the proposed hybrid BiGRU deep learning model.

scholarly journals Advancements in Deep Learning Theory and Applications: Perspective in 2020 and beyond

2020 ◽  
Author(s):  
Md Nazmus Saadat ◽  
Muhammad Shuaib
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Language Processing ◽  
Future Research ◽  
Learning In Context ◽  
Postgraduate Students ◽  
History Of ◽  
Learning Platforms ◽  
Future Research Directions ◽  
Learning Machine

The aim of this chapter is to introduce newcomers to deep learning, deep learning platforms, algorithms, applications, and open-source datasets. This chapter will give you a broad overview of the term deep learning, in context to deep learning machine learning, and Artificial Intelligence (AI) is also introduced. In Introduction, there is a brief overview of the research achievements of deep learning. After Introduction, a brief history of deep learning has been also discussed. The history started from a famous scientist called Allen Turing (1951) to 2020. In the start of a chapter after Introduction, there are some commonly used terminologies, which are used in deep learning. The main focus is on the most recent applications, the most commonly used algorithms, modern platforms, and relevant open-source databases or datasets available online. While discussing the most recent applications and platforms of deep learning, their scope in future is also discussed. Future research directions are discussed in applications and platforms. The natural language processing and auto-pilot vehicles were considered the state-of-the-art application, and these applications still need a good portion of further research. Any reader from undergraduate and postgraduate students, data scientist, and researchers would be benefitted from this.

scholarly journals CCD Time-Series Photometry of Astronomical Sources

1995 ◽  
Vol 167 ◽  
pp. 167-172
Author(s):  
Steve B. Howell
Keyword(s):  
Time Series ◽  
Photometric Data ◽  
Two Dimensional ◽  
Future Directions ◽  
Recent Developments ◽  
History Of

CCDs are essentially the only instrument available today for photometry at most observatories; they are also becoming more readily available to amateurs as well. Thus, obtaining good photometric data with these two-dimensional devices is something we all need to understand. The history of and recent developments in CCD time-series photometry will be reviewed with some comments on future directions.

scholarly journals Foreign language attrition

2012 ◽  
Vol 1 (1) ◽  
pp. 102-124 ◽  
Author(s):  
Monika S. Schmid ◽  
Teodora Mehotcheva
Keyword(s):  
Foreign Language ◽  
Theoretical Models ◽  
Foreign Languages ◽  
Language Attrition ◽  
Present Contribution ◽  
Future Directions ◽  
Second Languages ◽  
History Of Research ◽  
Recent Developments ◽  
History Of

The present contribution discusses recent developments and future directions in the attrition of instructed foreign languages, arguing for a distinction between this type of attrition and attrition involving second languages acquired implicitly in an immersion setting. An overview of the history of research in the field and the most prominent findings is provided, followed by a discussion of theoretical models and methodologically problematic issues. We conclude by outlining some future directions for the field.

scholarly journals Artificial Intelligence for Microscopy: What You Should Know

2019 ◽  
Author(s):  
Lucas von Chamier ◽  
Romain F. Laine ◽  
Ricardo Henriques
Keyword(s):  
Artificial Intelligence ◽  
Image Segmentation ◽  
Deep Learning ◽  
Information Content ◽  
Biomedical Research ◽  
Cell Biology ◽  
Future Directions ◽  
New Horizons ◽  
The Future ◽  
Recent Developments

Artificial Intelligence based on Deep Learning is opening new horizons in Biomedical research and promises to revolutionize the Microscopy field. Slowly, it now transitions from the hands of experts in Computer Sciences to researchers in Cell Biology. Here, we introduce recent developments in Deep Learning applied to Microscopy, in a manner accessible to non-experts. We overview its concepts, capabilities and limitations, presenting applications in image segmentation, classification and restoration. We discuss how Deep Learning shows an outstanding potential to push the limits of Microscopy, enhancing resolution, signal and information content in acquired data. Its pitfalls are carefully discussed, as well as the future directions expected in this field.

scholarly journals Same Representation, Different Attentions: Shareable Sentence Representation Learning from Multiple Tasks

2018 ◽  
Author(s):  
Renjie Zheng ◽  
Junkun Chen ◽  
Xipeng Qiu
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Text Classification ◽  
Representation Learning ◽  
Training Data ◽  
Specific Information ◽  
Distributed Representation ◽  
Source Codes ◽  
Multiple Tasks ◽  
Classification Tasks

Distributed representation plays an important role in deep learning based natural language processing. However, the representation of a sentence often varies in different tasks, which is usually learned from scratch and suffers from the limited amounts of training data. In this paper, we claim that a good sentence representation should be invariant and can benefit the various subsequent tasks. To achieve this purpose, we propose a new scheme of information sharing for multi-task learning. More specifically, all tasks share the same sentence representation and each task can select the task-specific information from the shared sentence representation with attention mechanisms. The query vector of each task's attention could be either static parameters or generated dynamically. We conduct extensive experiments on 16 different text classification tasks, which demonstrate the benefits of our architecture. Source codes of this paper are available on Github.

scholarly journals Sentiment analysis and predictors of optimism: The AUA Presidential addresses, 1902-2019

2021 ◽  
Author(s):  
Akhil Saji
Keyword(s):  
Language Processing ◽  
Independent Predictor ◽  
Presidential Address ◽  
Machine Learning Techniques ◽  
Significant Independent Predictor ◽  
Searchable Database ◽  
Learning Techniques ◽  
History Of ◽  
Factual Content ◽  
American Urological Association

Objectives The annual addresses of the President of the American Urological Association (AUA) may articulate and reflect the contemporary goals, values, and concerns of contemporary AUA membership. There is no organized archive of such addresses. We aimed to create a searchable database of all AUA Presidents and their addresses to determine variables associated with speech sentiment including positivity, negativity, and emotional tone through the 117 years of the AUA’s history. Methods We queried AUA archives, journals, recorded tape, and personal records, to create a database of all existing AUA Presidential addresses and biographic data. We applied natural language processing and machine learning techniques to evaluate the addresses for overall sentiment with validation using analog analyses (i.e reading and annotation). Multivariable logistic regression was performed to identify significant predictors of Presidential address sentiment. Results Between 1902-2019, a total of 113 AUA meetings were held. A total of 85 of 113 (75.22%) presidential addresses were transcribed and archived in the database representing 254,124 words by male presidents with a median (IQR) age of 61.43 (53.1-66.5) years. AUA Presidents during the second half of the history of the AUA (1960-2019) were significantly older at time of inauguration and gave more positive speeches in the active voice than presidents during the first half (1902-1959) (p < .05). The only significant independent predictor of the degree of positivity in an AUA President’s annual address was speaker age (95% CI 1.007-1.119). Conclusions We created the first digital, searchable database of all AUA Presidential speeches from 1902-2019 and aim to add additional addresses prospectively. Artificial intelligence analyses mirrored the findings of human reading and demonstrated that from 1902-2019 AUA Presidential addresses became more positive and optimistic with increasing speaker age but without consistent predictors of a speech’s emotional or factual content.

Deep Learning Approaches for Textual Sentiment Analysis

2020 ◽  
pp. 171-182 ◽  
Author(s):  
Tamanna Sharma ◽  
Anu Bajaj ◽  
Om Prakash Sangwan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Computational Technique ◽  
Learning Techniques

Sentiment analysis is computational measurement of attitude, opinions, and emotions (like positive/negative) with the help of text mining and natural language processing of words and phrases. Incorporation of machine learning techniques with natural language processing helps in analysing and predicting the sentiments in more precise manner. But sometimes, machine learning techniques are incapable in predicting sentiments due to unavailability of labelled data. To overcome this problem, an advanced computational technique called deep learning comes into play. This chapter highlights latest studies regarding use of deep learning techniques like convolutional neural network, recurrent neural network, etc. in sentiment analysis.

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