Browsing by Subject "Deep Learning"
Now showing items 21-31 of 31
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Local Methods for Document-Level Natural Language Processing
(2023-12-14)Given the recent rise in popularity of methods based on Deep Neural Networks inside Natural Language Processing (NLP), important progress has been made in a variety of tasks that was not possible before, given their ... -
A Machine Learning Framework for Host Based Intrusion Detection using System Call Abstraction
(2020-04-13)The number of cyber threats is increasing faster than the number of defensive strategies deployed to tackle those threats. An automated Intrusion Detection System (IDS) has the capability to detect, classify, and predict ... -
Modelling Human Target Reaching using A novel predictive deep reinforcement learning technique
(2018-04-03)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
(2015)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. ... -
Music Composer Recognition from MIDI Representation using Deep Learning and N-gram Based Methods
(2022-10-07)In order to answer conceptually basic queries like “Who created this piece?” the discipline of computational musicology frequently requires the analysis of detailed characteristics. Melodic lines, rhythmic patterns, ... -
PERSONALIZED TOPIC MODELLING OF DOMAIN-SPECIFIC DOCUMENT COLLECTIONS
(2023-04-10)Topic modelling refers to the discovery of abstract topics in a document collection. The abstract topics are often described by a statistical model that models the probabilistic relationship between topics, documents and ... -
Stock Movement Prediction with Deep Learning, Finance Tweets Sentiment, Technical Indicators, and Candlestick Charting
(2020-03-31)Stock prediction has been a popular research topic. Due to its stochastic nature, predicting the future stock market remains a difficult problem. This thesis studies the application of Deep Neural Networks (DNNS) in ... -
Structural Embedding of Constituency Trees in the Attention-based Model for Machine Comprehension
(2023-08-18)Incorporating hierarchical structures for various Natural Language Processing (NLP) tasks, which involves training the model with syntactic information of constituency trees, has been shown to be very effective. Constituency ... -
Techniques to Overcome Data Scarcity in Deep Learning for Passive Acoustic Monitoring of Marine Mammals
(2023-10-20)Passive Acoustic Monitoring (PAM) is a useful technique for monitoring marine mammals. However, the large volume of data collected through PAM systems make automated algorithms for detecting and classifying sounds essential. ... -
A TRANSFORMER-BASED GRAPH NEURAL NETWORK AGGREGATION FRAMEWORK FOR 5G RADIO LINK FAILURE PREDICTION
(2023-08-31)The prediction of Radio Link Failures (RLF) in Radio Access Networks (RANs) is crucial to ensure smooth communication and meet the demanding requirements of high data rates, low latency, and improved performance in 5G ... -
UNSUPERVISED PARAPHRASE GENERATION FROM HIERARCHICAL LANGUAGE MODELS
(2018-12-14)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 ...