Search Results for author:
Found 11 papers, 5 papers with code
Data-Adaptive Dimensional Analysis for Accurate Interpolation and Extrapolation in Computer Experiments
Dimensional analysis (DA) pays attention to fundamental physical dimensions such as length and mass when modelling scientific and engineering systems.
Distilling and Transferring Knowledge via cGAN-generated Samples for Image Classification and Regression
Knowledge distillation (KD) has been actively studied for image classification tasks in deep learning, aiming to improve the performance of a student based on the knowledge from a teacher.
Efficient Subsampling of Realistic Images From GANs Conditional on a Class or a Continuous Variable
When sampling from CcGANs, the superiority of cDR-RS is even more noticeable in terms of both effectiveness and efficiency.
Ranked #1 on
Image Generation
on RC-49
Continuous Conditional Generative Adversarial Networks: Novel Empirical Losses and Label Input Mechanisms
This work proposes the continuous conditional generative adversarial network (CcGAN), the first generative model for image generation conditional on continuous, scalar conditions (termed regression labels).
Ranked #2 on
Image Generation
on RC-49
Classification Beats Regression: Counting of Cells from Greyscale Microscopic Images based on Annotation-free Training Samples
Modern methods often formulate the counting of cells from microscopic images as a regression problem and more or less rely on expensive, manually annotated training images (e. g., dot annotations indicating the centroids of cells or segmentation masks identifying the contours of cells).
Subsampling Generative Adversarial Networks: Density Ratio Estimation in Feature Space with Softplus Loss
Our subsampling methods do not rely on the optimality of the discriminator and are suitable for all types of GANs.
Sequential Computer Experimental Design for Estimating an Extreme Probability or Quantile
A computer code can simulate a system's propagation of variation from random inputs to output measures of quality.
Bayesian Optimization Using Monotonicity Information and Its Application in Machine Learning Hyperparameter
We propose an algorithm for a family of optimization problems where the objective can be decomposed as a sum of functions with monotonicity properties.
Regression Phalanxes
Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems.
Ensembles of phalanxes across assessment metrics for robust ranking of homologous proteins
Our approach uses an ensemble of models in a classifier and an ensemble of assessment metrics.
Ensembling classification models based on phalanxes of variables with applications in drug discovery
Because the class of interest is rare in the training data, there is relatively little information in the known class response labels for model building.
