Add to ORKGDensely connected convolutional neural networks are currently one of the best object recognition algorithms. Given the plasticity of neural networks, the DenseNet algorithm should perform similarly in NLP tasks. In its attempt to verify whether the DenseNet algorithm can yield equally impressive results on NLP tasks, this paper has modified the DenseNet algorithm and tested it on text classification. For this purpose, three differently sized datasets have each been encoded as Tf-IDf vectors and word vectors and then the DenseNet’s performance on these different feature sets was compared to more conventional methods including Naïve Bayes classifiers and other neural networks. The paper finds that DenseNets can perform on par with these algor...
The strength of long short-term memory neural networks (LSTMs) that have been applied is more locate...
Recently it has been shown that sparse neural networks perform better than dense networks with simil...
Deep neural networks (DNN) are the state-of-the-art machine learning models outperforming traditiona...
The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and con...
Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProce...
Convolutional Neural Networks (CNNs) and pre-trained word embeddings have revolutionized the field o...
The thesis explores different extensions of Deep Neural Networks in learning underlying natural lang...
In recent years, Deep Learning (DL) techniques have gained much at-tention from Artificial Intellige...
Convolutional neural networks have seen much success in computer vision and natural language process...
Convolutional neural networks have seen much success in computer vision and natural language process...
Densely connected convolutional networks (DenseNet) behave well in image processing. However, for re...
In the computer vision field, semantic segmentation represents a very interesting task. Convolutiona...
Abstract. Convolutional Neural Networks (CNNs) can provide accu-rate object classification. They can...
This master's thesis investigates how a state-of-the-art (SOTA) deep neural network (NN) model can b...
International audienceWe introduce a sparse scattering deep convolutional neural network, which prov...
The strength of long short-term memory neural networks (LSTMs) that have been applied is more locate...
Recently it has been shown that sparse neural networks perform better than dense networks with simil...
Deep neural networks (DNN) are the state-of-the-art machine learning models outperforming traditiona...
The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and con...
Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProce...
Convolutional Neural Networks (CNNs) and pre-trained word embeddings have revolutionized the field o...
The thesis explores different extensions of Deep Neural Networks in learning underlying natural lang...
In recent years, Deep Learning (DL) techniques have gained much at-tention from Artificial Intellige...
Convolutional neural networks have seen much success in computer vision and natural language process...
Convolutional neural networks have seen much success in computer vision and natural language process...
Densely connected convolutional networks (DenseNet) behave well in image processing. However, for re...
In the computer vision field, semantic segmentation represents a very interesting task. Convolutiona...
Abstract. Convolutional Neural Networks (CNNs) can provide accu-rate object classification. They can...
This master's thesis investigates how a state-of-the-art (SOTA) deep neural network (NN) model can b...
International audienceWe introduce a sparse scattering deep convolutional neural network, which prov...
The strength of long short-term memory neural networks (LSTMs) that have been applied is more locate...
Recently it has been shown that sparse neural networks perform better than dense networks with simil...
Deep neural networks (DNN) are the state-of-the-art machine learning models outperforming traditiona...