A comprehensive survey on pretrained foundation models: A history from bert to chatgpt
Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks with different data modalities. A PFM (eg, BERT, ChatGPT, and GPT-4) is …
downstream tasks with different data modalities. A PFM (eg, BERT, ChatGPT, and GPT-4) is …
Graph representation learning: a survey
Research on graph representation learning has received great attention in recent years
since most data in real-world applications come in the form of graphs. High-dimensional …
since most data in real-world applications come in the form of graphs. High-dimensional …
Analysis of dimensionality reduction techniques on big data
Due to digitization, a huge volume of data is being generated across several sectors such as
healthcare, production, sales, IoT devices, Web, organizations. Machine learning algorithms …
healthcare, production, sales, IoT devices, Web, organizations. Machine learning algorithms …
Overview and comparative study of dimensionality reduction techniques for high dimensional data
S Ayesha, MK Hanif, R Talib - Information Fusion, 2020 - Elsevier
The recent developments in the modern data collection tools, techniques, and storage
capabilities are leading towards huge volume of data. The dimensions of data indicate the …
capabilities are leading towards huge volume of data. The dimensions of data indicate the …
Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning
Recent critical commentaries unfavorably compare deep learning (DL) with standard
machine learning (SML) approaches for brain imaging data analysis. However, their …
machine learning (SML) approaches for brain imaging data analysis. However, their …
Linear discriminant analysis: A detailed tutorial
Linear Discriminant Analysis (LDA) is a very common technique for dimensionality reduction
problems as a preprocessing step for machine learning and pattern classification …
problems as a preprocessing step for machine learning and pattern classification …
Asymmetric transitivity preserving graph embedding
Graph embedding algorithms embed a graph into a vector space where the structure and
the inherent properties of the graph are preserved. The existing graph embedding methods …
the inherent properties of the graph are preserved. The existing graph embedding methods …
Improving the performance of individually calibrated SSVEP-BCI by task-discriminant component analysis
A brain-computer interface (BCI) provides a direct communication channel between a brain
and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has …
and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has …
Artificial intelligence and internet of things in screening and management of autism spectrum disorder
Autism is a disability that obstructs the process of a person's development. Autistic
individuals find it extremely difficult to cope with the world's pace, can not communicate …
individuals find it extremely difficult to cope with the world's pace, can not communicate …
Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset
Background/foreground separation is the first step in video surveillance system to detect
moving objects. Recent research on problem formulations based on decomposition into low …
moving objects. Recent research on problem formulations based on decomposition into low …