scholarly journals Corpus and Models for Lemmatisation and POS-tagging of Classical French Theatre

2021 ◽  
Vol 2021 (Digital humanities in...) ◽  
Author(s):  
Jean-Baptiste Camps ◽  
Simon Gabay ◽  
Paul Fièvre ◽  
Thibault Clérice ◽  
Florian Cafiero
Keyword(s):  
Neural Networks ◽  
French Literature ◽  
State Of The Art ◽  
Preliminary Step ◽  
Training Models ◽  
Pos Tagging ◽  
Current State ◽  
French Theatre ◽  
And Training

This paper describes the process of building an annotated corpus and training models for classical French literature, with a focus on theatre, and particularly comedies in verse. It was originally developed as a preliminary step to the stylometric analyses presented in Cafiero and Camps [2019]. The use of a recent lemmatiser based on neural networks and a CRF tagger allows to achieve accuracies beyond the current state-of-the art on the in-domain test, and proves to be robust during out-of-domain tests, i.e.up to 20th c.novels.

scholarly journals An Efficient and General Framework for Aerial Point Cloud Classification in Urban Scenarios

2021 ◽  
Vol 13 (10) ◽  
pp. 1985
Author(s):  
Emre Özdemir ◽  
Fabio Remondino ◽  
Alessandro Golkar
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Computational Power ◽  
Specific Data ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Current State ◽  
Data Source ◽  
Point Cloud Classification ◽  
And Training

With recent advances in technologies, deep learning is being applied more and more to different tasks. In particular, point cloud processing and classification have been studied for a while now, with various methods developed. Some of the available classification approaches are based on specific data source, like LiDAR, while others are focused on specific scenarios, like indoor. A general major issue is the computational efficiency (in terms of power consumption, memory requirement, and training/inference time). In this study, we propose an efficient framework (named TONIC) that can work with any kind of aerial data source (LiDAR or photogrammetry) and does not require high computational power while achieving accuracy on par with the current state of the art methods. We also test our framework for its generalization ability, showing capabilities to learn from one dataset and predict on unseen aerial scenarios.

scholarly journals Soft Threshold Ternary Networks

2020 ◽  
Author(s):  
Weixiang Xu ◽  
Xiangyu He ◽  
Tianli Zhao ◽  
Qinghao Hu ◽  
Peisong Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Mobile Devices ◽  
State Of The Art ◽  
Performance Gap ◽  
Training Time ◽  
Current State ◽  
The Arts ◽  
Soft Threshold ◽  
And Storage ◽  
Selection Of

Large neural networks are difficult to deploy on mobile devices because of intensive computation and storage. To alleviate it, we study ternarization, a balance between efficiency and accuracy that quantizes both weights and activations into ternary values. In previous ternarized neural networks, a hard threshold Δ is introduced to determine quantization intervals. Although the selection of Δ greatly affects the training results, previous works estimate Δ via an approximation or treat it as a hyper-parameter, which is suboptimal. In this paper, we present the Soft Threshold Ternary Networks (STTN), which enables the model to automatically determine quantization intervals instead of depending on a hard threshold. Concretely, we replace the original ternary kernel with the addition of two binary kernels at training time, where ternary values are determined by the combination of two corresponding binary values. At inference time, we add up the two binary kernels to obtain a single ternary kernel. Our method dramatically outperforms current state-of-the-arts, lowering the performance gap between full-precision networks and extreme low bit networks. Experiments on ImageNet with AlexNet (Top-1 55.6%), ResNet-18 (Top-1 66.2%) achieves new state-of-the-art.

scholarly journals AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays

2021 ◽  
Vol 7 ◽  
pp. e495
Author(s):  
Saleh Albahli ◽  
Hafiz Tayyab Rauf ◽  
Abdulelah Algosaibi ◽  
Valentina Emilia Balas
Keyword(s):  
Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Synthetic Data ◽  
X Rays ◽  
Deep Convolutional Neural Networks ◽  
Current State ◽  
Pathology Classification ◽  
Wide Range ◽  
Multi Class Classification

Artificial intelligence (AI) has played a significant role in image analysis and feature extraction, applied to detect and diagnose a wide range of chest-related diseases. Although several researchers have used current state-of-the-art approaches and have produced impressive chest-related clinical outcomes, specific techniques may not contribute many advantages if one type of disease is detected without the rest being identified. Those who tried to identify multiple chest-related diseases were ineffective due to insufficient data and the available data not being balanced. This research provides a significant contribution to the healthcare industry and the research community by proposing a synthetic data augmentation in three deep Convolutional Neural Networks (CNNs) architectures for the detection of 14 chest-related diseases. The employed models are DenseNet121, InceptionResNetV2, and ResNet152V2; after training and validation, an average ROC-AUC score of 0.80 was obtained competitive as compared to the previous models that were trained for multi-class classification to detect anomalies in x-ray images. This research illustrates how the proposed model practices state-of-the-art deep neural networks to classify 14 chest-related diseases with better accuracy.

scholarly journals Detecting Emotions in English and Arabic Tweets

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 98 ◽  
Author(s):  
Tariq Ahmad ◽  
Allan Ramsay ◽  
Hanady Ahmed
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Learning Algorithms ◽  
General Purpose ◽  
Machine Learning Algorithms ◽  
Current State ◽  
Optimal Thresholds ◽  
Alternative Approach

Assigning sentiment labels to documents is, at first sight, a standard multi-label classification task. Many approaches have been used for this task, but the current state-of-the-art solutions use deep neural networks (DNNs). As such, it seems likely that standard machine learning algorithms, such as these, will provide an effective approach. We describe an alternative approach, involving the use of probabilities to construct a weighted lexicon of sentiment terms, then modifying the lexicon and calculating optimal thresholds for each class. We show that this approach outperforms the use of DNNs and other standard algorithms. We believe that DNNs are not a universal panacea and that paying attention to the nature of the data that you are trying to learn from can be more important than trying out ever more powerful general purpose machine learning algorithms.

scholarly journals PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Hylke E. Beck ◽  
Seth Westra ◽  
Jackson Tan ◽  
Florian Pappenberger ◽  
George J. Huffman ◽  
...  
Keyword(s):  
Neural Networks ◽  
Probability Distribution ◽  
State Of The Art ◽  
Intertropical Convergence Zone ◽  
Coefficient Of Determination ◽  
Current State ◽  
Peak Intensity ◽  
Global Land ◽  
The Neural Networks ◽  
Better Than

Abstract We introduce the Precipitation Probability DISTribution (PPDIST) dataset, a collection of global high-resolution (0.1°) observation-based climatologies (1979–2018) of the occurrence and peak intensity of precipitation (P) at daily and 3-hourly time-scales. The climatologies were produced using neural networks trained with daily P observations from 93,138 gauges and hourly P observations (resampled to 3-hourly) from 11,881 gauges worldwide. Mean validation coefficient of determination (R2) values ranged from 0.76 to 0.80 for the daily P occurrence indices, and from 0.44 to 0.84 for the daily peak P intensity indices. The neural networks performed significantly better than current state-of-the-art reanalysis (ERA5) and satellite (IMERG) products for all P indices. Using a 0.1 mm 3 h−1 threshold, P was estimated to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively. The highest P intensities were found over parts of Central America, India, and Southeast Asia, along the western equatorial coast of Africa, and in the intertropical convergence zone. The PPDIST dataset is available via www.gloh2o.org/ppdist.

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 Empirical Investigation of Optimization Algorithms in Neural Machine Translation

2017 ◽  
Vol 108 (1) ◽  
pp. 13-25 ◽  
Author(s):  
Parnia Bahar ◽  
Tamer Alkhouli ◽  
Jan-Thorsten Peter ◽  
Christopher Jan-Steffen Brix ◽  
Hermann Ney
Keyword(s):  
Neural Networks ◽  
Machine Translation ◽  
Optimization Problem ◽  
Empirical Investigation ◽  
State Of The Art ◽  
Optimization Techniques ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
And Training ◽  
Dimensional Optimization

AbstractTraining neural networks is a non-convex and a high-dimensional optimization problem. In this paper, we provide a comparative study of the most popular stochastic optimization techniques used to train neural networks. We evaluate the methods in terms of convergence speed, translation quality, and training stability. In addition, we investigate combinations that seek to improve optimization in terms of these aspects. We train state-of-the-art attention-based models and apply them to perform neural machine translation. We demonstrate our results on two tasks: WMT 2016 En→Ro and WMT 2015 De→En.

The secret is at the crossways: Hodotopic organization and nonlinear dynamics of brain neural networks

2013 ◽  
Vol 36 (6) ◽  
pp. 623-624 ◽  
Author(s):  
Tobias A. Mattei
Keyword(s):  
Nonlinear Dynamics ◽  
Neural Networks ◽  
Cognitive Neuroscience ◽  
State Of The Art ◽  
Current State

AbstractBy integrating the classic psychological principles of ancient art of memory (AAOM) with the most recent paradigms in cognitive neuroscience (i.e., the concepts of hodotopic organization and nonlinear dynamics of brain neural networks), Llewellyn provides an up-to-date model of the complex psychological relationships between memory, imagination, and dreams in accordance with current state-of-the-art principles in neuroscience.

Future Directions in Cognitive Engineering and Naturalistic Decision Making

1995 ◽  
Vol 39 (9) ◽  
pp. 450-453 ◽  
Author(s):  
Mica R. Endsley ◽  
Gary Klein ◽  
David D. Woods ◽  
Philip J. Smith ◽  
Stephen J. Selcon
Keyword(s):  
Decision Making ◽  
State Of The Art ◽  
Cognitive Engineering ◽  
Training Process ◽  
Naturalistic Decision Making ◽  
Future Directions ◽  
Current State ◽  
Near Future ◽  
And Training ◽  
Research Domain

Cognitive Engineering and Naturalistic Decision Making are presented as two related fields of endeavor that seek to understand how people process information and perform within complex systems and to develop ways of applying this knowledge within the design and training process This panel presents an overview of the current state of the art in this research domain and charts paths for needed developments in the field in the near future.

scholarly journals Training a neural network to learn other dimensionality reduction removes data size restrictions in bioinformatics and provides a new route to exploring data representations

2020 ◽  
Author(s):  
Alex Dexter ◽  
Spencer A. Thomas ◽  
Rory T. Steven ◽  
Kenneth N. Robinson ◽  
Adam J. Taylor ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Dimensionality Reduction ◽  
Computational Analysis ◽  
New Technologies ◽  
State Of The Art ◽  
Current State ◽  
Data Representations ◽  
Non Linear ◽  
Linear Dimensionality Reduction

AbstractHigh dimensionality omics and hyperspectral imaging datasets present difficult challenges for feature extraction and data mining due to huge numbers of features that cannot be simultaneously examined. The sample numbers and variables of these methods are constantly growing as new technologies are developed, and computational analysis needs to evolve to keep up with growing demand. Current state of the art algorithms can handle some routine datasets but struggle when datasets grow above a certain size. We present a training deep learning via neural networks on non-linear dimensionality reduction, in particular t-distributed stochastic neighbour embedding (t-SNE), to overcome prior limitations of these methods.One Sentence SummaryAnalysis of prohibitively large datasets by combining deep learning via neural networks with non-linear dimensionality reduction.

Sign in / Sign up

Export Citation Format

Share Document