• No results found

[PDF] Top 20 Deep Learning Architectures for Novel Problems

Has 10000 "Deep Learning Architectures for Novel Problems" found on our website. Below are the top 20 most common "Deep Learning Architectures for Novel Problems".

Deep Learning Architectures for Novel Problems

Deep Learning Architectures for Novel Problems

... We use Graph-CNNs to address this problem and use the standard benchmark datasets − NCI1 and D&D, to compare classification performance. NCI1 is a bal- anced graph dataset of chemical compounds that are screened for ... See full document

66

Continual Learning with Deep Architectures

Continual Learning with Deep Architectures

... This is why, for better performance and general applicability a number of more advanced CL strategies are needed. In this section, the CWR, CWR+ and AR1 strategies have been evaluated on the iCIFAR-100 and the CORe50 ... See full document

150

Deep learning architectures for Computer Vision

Deep learning architectures for Computer Vision

... To retrain from scratch a deep neural network like VGG, the amount of images used [7] is above one million. In many different cases the dataset used is not big enough to meet this requirement, so two new ... See full document

40

New architectures for very deep learning

New architectures for very deep learning

... Chapter 4 Local Winner-Take-All Networks Consider a layer of units in an MLP being trained to classify input patterns as being of one of the ten Arabic digits (0–9). The units are expected to learn to identify certain ... See full document

138

New hybrid kernel architectures for deep learning

New hybrid kernel architectures for deep learning

... and deep belief ...make deep learning architectures a good choice for transfer ...traditional deep learning approach ...deeper architectures and providing training ... See full document

66

Biomedical Data Classification with Improvised Deep Learning Architectures

Biomedical Data Classification with Improvised Deep Learning Architectures

... machine learning/deep learning ...combinational deep learning architectures. We use deep neural networks with 3D volumetric structural magnetic resonance images of ... See full document

111

Learning to Match Aerial Images with Deep Attentive Architectures

Learning to Match Aerial Images with Deep Attentive Architectures

... Our main contributions are as follows. First, we demon- strate that deep CNNs offer a solution for ultra-wide base- line matching. Inspired by recent efforts in patch matching [14, 43, 31] we build a ... See full document

9

The Science of Learning: Transferring Learning to Novel Problems

The Science of Learning: Transferring Learning to Novel Problems

... new problems, they base their thinking on what they have experienced ...hinder learning and transfer (Bransford, Brown, & Cocking, ...(2011), learning is mediated by what is already ... See full document

6

Comparing different deep learning architectures for classification of chest radiographs

Comparing different deep learning architectures for classification of chest radiographs

... network architectures, mainly a DenseNet-121, were used to classify the CheXpert data set 6,9,22 ...different architectures instead of the optimization of one specific ...certain architectures more ... See full document

16

Deep Transfer Learning for Art Classification Problems

Deep Transfer Learning for Art Classification Problems

... classification problems. We use four neural architectures that have obtained strong results on the ImageNet challenge in recent years and we investigate their performances when it comes to attributing the ... See full document

16

Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics

Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics

... semantics. Deep visual representations, learned using convolutional neural networks, have been shown to achieve particularly high ...compare deep visual representation learning techniques, exper- ... See full document

10

Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics

Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics

... categories, learning rich feature representations for a wide range of ...appropriate learning procedure to force the networks to classify images of a different domain produced by the CWT block into 24 ... See full document

13

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

... Abstract—Deep learning has taken over - both in problems beyond the realm of traditional, hand-crafted machine learning paradigms as well as in capturing the imagination of the practi- tioner ... See full document

23

Analysis of Deep Learning Architectures for Cross-corpus Speech Emotion Recognition

Analysis of Deep Learning Architectures for Cross-corpus Speech Emotion Recognition

... We then evaluate the generalisation capability of the models trained only on IEMOCAP. We re-train these models to output three emotions according to the mapping in Table 4 and report performance on the out-of-domain ... See full document

5

What Is Not Where: the Challenge of Integrating Spatial Representations Into Deep Learning Architectures

What Is Not Where: the Challenge of Integrating Spatial Representations Into Deep Learning Architectures

... with deep learn- ing and argued that in its present setup they fail to ground the meaning of spatial descriptions in the image but nonetheless achieve a good perfor- mance in generating spatial language which is ... See full document

13

Analysis of deep learning architectures for turbulence mitigation in long-range imagery

Analysis of deep learning architectures for turbulence mitigation in long-range imagery

... of deep learning for image processing has now become commonplace, with neural networks being able to outperform traditional methods in many ...various deep learning architectures on the ... See full document

18

Synthesising visual speech using dynamic visemes and deep learning architectures

Synthesising visual speech using dynamic visemes and deep learning architectures

... feedforward deep neural network (DNN) and many-to-one and many-to-many recurrent neural networks (RNNs) using long short-term memory (LSTM) are ...a deep learning ... See full document

51

Classification for DGA-Based Malicious Domain Names with Deep Learning Architectures

Classification for DGA-Based Malicious Domain Names with Deep Learning Architectures

... of deep learning networks in a broad range of applications, this article transfers five advanced learned ImageNet models from Alex Net, VGG, Squeeze Net, Inception, Res Net to classify DGA domains and ... See full document

5

Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs.

Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs.

... all deep learning architectures, transfer learning increased performance in detecting GON and decreased the training time needed for model ...transfer learning helps the model learn ... See full document

14

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures

... Deep learning for root systems The prevailing methodology when working with images in deep learning is the ...machine learning via their ability to learn not only solutions to prob- ... See full document

16

Show all 10000 documents...