Search Results for author:
Found 20 papers, 10 papers with code
How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey
Despite its technological breakthroughs, eXplainable Artificial Intelligence (XAI) research has limited success in producing the {\em effective explanations} needed by users.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Imputation using training labels and classification via label imputation
However, even though the label is usually available in the training data, the common practice of imputation usually only relies on the input and ignores the label.
Correlation visualization under missing values: a comparison between imputation and direct parameter estimation methods
Correlation matrix visualization is essential for understanding the relationships between variables in a dataset, but missing data can pose a significant challenge in estimating correlation coefficients.
Blockwise Principal Component Analysis for monotone missing data imputation and dimensionality reduction
To address this issue, we propose a Blockwise principal component analysis Imputation (BPI) framework for dimensionality reduction and imputation of monotone missing data.
Conditional expectation with regularization for missing data imputation
Missing data frequently occurs in datasets across various domains, such as medicine, sports, and finance.
VISEM-Tracking, a human spermatozoa tracking dataset
A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view.
Combining datasets to increase the number of samples and improve model fitting
For many use cases, combining information from different datasets can be of interest to improve a machine learning model's performance, especially when the number of samples from at least one of the datasets is small.
Principal Component Analysis based frameworks for efficient missing data imputation algorithms
Missing data is a commonly occurring problem in practice.
Unequal Covariance Awareness for Fisher Discriminant Analysis and Its Variants in Classification
Fisher Discriminant Analysis (FDA) is one of the essential tools for feature extraction and classification.
Parallel feature selection based on the trace ratio criterion
The experiments show that our method can produce a small set of features in a fraction of the amount of time by the other methods under comparison.
FinNet: Solving Time-Independent Differential Equations with Finite Difference Neural Network
Deep learning approaches for partial differential equations (PDEs) have received much attention in recent years due to their mesh-freeness and computational efficiency.
StressNAS: Affect State and Stress Detection Using Neural Architecture Search
Smartwatches have rapidly evolved towards capabilities to accurately capture physiological signals.
Dictionary-Guided Scene Text Recognition
Language prior plays an important role in the way humans perceive and recognize text in the wild.
DPER: Efficient Parameter Estimation for Randomly Missing Data
The missing data problem has been broadly studied in the last few decades and has various applications in different areas such as statistics or bioinformatics.
COVID-19 and Big Data: Multi-faceted Analysis for Spatio-temporal Understanding of the Pandemic with Social Media Conversations
We provide a multi-faceted analysis of critical properties exhibited by these conversations on social media regarding the novel coronavirus pandemic.
Learning To Count Everything
We also present a novel adaptation strategy to adapt our network to any novel visual category at test time, using only a few exemplar objects from the novel category.
Ranked #13 on
Object Counting
on FSC147
Structural and Functional Decomposition for Personality Image Captioning in a Communication Game
Personality image captioning (PIC) aims to describe an image with a natural language caption given a personality trait.
EPEM: Efficient Parameter Estimation for Multiple Class Monotone Missing Data
The problem of monotone missing data has been broadly studied during the last two decades and has many applications in different fields such as bioinformatics or statistics.
Faster feature selection with a Dropping Forward-Backward algorithm
In this era of big data, feature selection techniques, which have long been proven to simplify the model, makes the model more comprehensible, speed up the process of learning, have become more and more important.
ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks
Brain extraction (skull stripping) is a challenging problem in neuroimaging.
