[PDF] Top 20 Deep Learning Structural and Historical Features for Anti-Patterns Detection
Has 10000 "Deep Learning Structural and Historical Features for Anti-Patterns Detection" found on our website. Below are the top 20 most common "Deep Learning Structural and Historical Features for Anti-Patterns Detection".
Deep Learning Structural and Historical Features for Anti-Patterns Detection
... (3.1) For a method, the “entity set” contains the entities accessed by the method, and for an attribute, it contains the methods accessing the ...on structural information to predict whether ... Voir le document complet
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A Novel Deep Learning Approach for Liver MRI Classification and HCC Detection
... HCC detection is a very challenging task, and has been treated in several research ...level and structure features maps from high and low-magnifications images respectively by ... Voir le document complet
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Deep Learning for Seismic Data Processing and Interpretation
... machine learning, a branch of artifi- cial intelligence, to solve pattern recognition ...the detection function, this class of algorithms is based on the use of a number of free parameters that will learn ... Voir le document complet
183
Deep learning and reinforcement learning methods for grounded goal-oriented dialogue
... VQA. For the trained MODERN model, we extract image features just before the attention mechanism of MODERN, which we will compare with extracted raw ResNet-50 features and finetune ResNet- 50 ... Voir le document complet
164
Transfer Learning for Handwriting Recognition on Historical Documents
... Figure 2: Example of a financial daily record for the Italian Comedy with identification fields. In this part of the project, our study focuses on the title field. This can be explained by the large collec- tion ... Voir le document complet
9
Automated structural damage detection using one class machine learning
... Previous research which has focused on the goal of developing automated, 'smart sensing' SHM technology, as described above, has adopted a data-based approach to da[r] ... Voir le document complet
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Preterm Newborn Presence Detection in Incubator and Open Bed Using Deep Transfer Learning
... units for monitoring the state of preterm newborns since it is contact-less and ...method for automatic detection of preterm newborn presence in incubator and open ...model for ... Voir le document complet
12
Stride detection for pedestrian trajectory reconstruction: a machine learning approach based on geometric patterns
... III.B for each element of our database, 2695 features are ...statistical learning algorithms have been tested notably random forests which are known to perform well in large dimensions, Support ... Voir le document complet
7
Training Set Class Distribution Analysis for Deep Learning Model - Application to Cancer Detection
... eep learning models specifically CNNs have been used successfully in many tasks including medical image ...obtain for new applications or new ...cancer detection task from histopathological images ... Voir le document complet
7
Training Set Class Distribution Analysis for Deep Learning Model - Application to Cancer Detection
... segmentation and classification, Deep learning, Class-biased ...of deep learning models in visual recognition [1] [2] and specifically CNN, drove researchers to explore their use ... Voir le document complet
6
How to Deal with Multi-source Data for Tree Detection Based on Deep Learning
... tion and detection of urban trees in multi-source aerial data composed of synchronized optical, near infrared and Digi- tal Surface Model (DSM) measurements of urban ...tion and not a pixel ... Voir le document complet
6
Natural vs Balanced Distribution in Deep Learning on Whole Slide Images for Cancer Detection
... challenging for a ...data, and others suggesting algorithm-level modifications [12, 30], such as applying a weighted cost ...data-level and algorithm-level changes [4, 36, ...data and model ... Voir le document complet
9
Machine learning and extremes for anomaly detection
... Anomaly Detection algorithms actions to be taken, especially in situations where human expertise is required to check each observation is ...machine learning perspective, anomaly detection can be ... Voir le document complet
221
FPGA based accelerator for visual features detection
... car. For such applications, strong processing times constraints have to be faced, to cope with po- tential high vehicle ...processor and a hardware accelerator (both included in the Xilinx Zynq FPGA) to ... Voir le document complet
7
Algorithms for structural and dynamical polychronous groups detection
... looking for neurons that might be excited enough to fire in turn, because they recieve more than a certain amount of spikes, N bSpikesN ...timing and record the propagation of the neural activity, until it ... Voir le document complet
12
A deep learning based on sparse auto-encoder with MCSA for broken rotor bar fault detection and diagnosis
... Index Terms—Deep Learning, Sparse Auto-Encoder Machine Current Signature Analysis, Fault Diagnosis and Detection, Broken Rotor Bar, Induction Motor... I NTRODUCTION Three phase IMs are w[r] ... Voir le document complet
6
Deep learning for distributed circuit design
... Further, by leveraging neural networks' differentiability, we can use our model to solve the inverse problem - i.e., given desirable EM specifications, we propagate th[r] ... Voir le document complet
55
Effective and annotation efficient deep learning for image understanding
... hand, for the residual based approaches it is easier to learn to predict zero residuals in the case of correct initial labels, but it is more difficult for them to refine “hard” mistakes that deviate a lot ... Voir le document complet
236
Active learning and input space analysis for deep networks
... tasks for text, recent comparisons have con rmed the advantage of CNNs over RNNs when the task at hand is mostly a keyphrase recognition task [Yin ...linguistics patterns to analyze their contrast: ... Voir le document complet
195
Deep learning for clinical mammography screening
... vision and deep learning, all the screening mammograms today are still read ...manually. Deep Learning techniques have revolutionized various fields such as object recog- nition, speech ... Voir le document complet
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