Browsing by Subject "Deep Learning"
Now showing items 1-8 of 8
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Active Neural Learners for Text with Dual Supervision
Dual supervision for text classification and information retrieval, which involves training the machine with class labels augmented with text annotations that are indicative of the class, has been shown to provide significant ... -
Classification and Analysis of a Large MEG Dataset using Convolutional Neural Networks
Convolutional neural networks were used to classify and analyse a large magnetoencephalography (MEG) dataset. Networks were trained to classify between active and baseline intervals recorded during cued button pressing. ... -
Concept Embedding for Deep Neural Functional Analysis of Genes and Deep Neural Word Sense Disambiguation of Biomedical Text
As far as Gene Ontology (GO) is concerned, most of the existing gene functional similarity measures combine information content-based semantic similarity scores of single GO-term pairs to estimate gene functional similarity, ... -
Discriminative Shape Feature Pooling in Deep Convolutional Networks for Visual Classification
Unlike conventional handcrafted feature extractors, deep learning approach can extract generic image features without relying on explicit domain knowledge. Recently, there is a trend of combining handcrafted features with ... -
Learning Embeddings for Text and Images from Structure of the Data
In the big data era, most of the data generated every day are high dimensional such as text and image data. Learning compact representations from the input data can help in dealing with the high dimensionality and visualization ... -
Modelling Human Target Reaching using A novel predictive deep reinforcement learning technique
It is hypothesized that the brain builds an internal representation of the world and its body. Moreover, it is well established that human decision making and instrumental control uses multiple systems, some which are ... -
Multi-path Convolutional Neural Networks for Image Classification
Convolutional Neural Networks have demonstrated high performance in the ImageNet Large-Scale Visual Recognition Challenges contest. Nevertheless, the published results only show the overall performance for all image classes. ... -
UNSUPERVISED PARAPHRASE GENERATION FROM HIERARCHICAL LANGUAGE MODELS
Paraphrase generation is a challenging problem that requires a semantic representation of language. Language models implemented with deep neural networks (DNN) have the ability to transform text to a real valued vector ...
