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Found 16 papers, 1 papers with code
Harmonizing Attention: Training-free Texture-aware Geometry Transfer
Extracting geometry features from photographic images independently of surface texture and transferring them onto different materials remains a complex challenge.
Remarks on Loss Function of Threshold Method for Ordinal Regression Problem
Threshold methods are popular for ordinal regression problems, which are classification problems for data with a natural ordinal relation.
Parallel Algorithm for Optimal Threshold Labeling of Ordinal Regression Methods
For $K$-class OR tasks, threshold methods learn a one-dimensional transformation (1DT) of the explanatory variable so that 1DT values for observations of the explanatory variable preserve the order of label values $1,\ldots, K$ for corresponding observations of the target variable well, and then assign a label prediction to the learned 1DT through threshold labeling, namely, according to the rank of an interval to which the 1DT belongs among intervals on the real line separated by $(K-1)$ threshold parameters.
Universality of reservoir systems with recurrent neural networks
Approximation capability of reservoir systems whose reservoir is a recurrent neural network (RNN) is discussed.
Spatio-temporal reconstruction of substance dynamics using compressed sensing in multi-spectral magnetic resonance spectroscopic imaging
The objective of our study is to observe dynamics of multiple substances in vivo with high temporal resolution from multi-spectral magnetic resonance spectroscopic imaging (MRSI) data.
Convergence Analysis of Blurring Mean Shift
Blurring mean shift (BMS) algorithm, a variant of the mean shift algorithm, is a kernel-based iterative method for data clustering, where data points are clustered according to their convergent points via iterative blurring.
Negative-prompt Inversion: Fast Image Inversion for Editing with Text-guided Diffusion Models
In image editing employing diffusion models, it is crucial to preserve the reconstruction quality of the original image while changing its style.
Ranked #6 on
Text-based Image Editing
on PIE-Bench
Convergence Analysis of Mean Shift
The mean shift (MS) algorithm seeks a mode of the kernel density estimate (KDE).
Label Smoothing is Robustification against Model Misspecification
For example, in binary classification, instead of the one-hot target $(1, 0)^\top$ used in conventional logistic regression (LR), LR with LS (LSLR) uses the smoothed target $(1-\frac{\alpha}{2},\frac{\alpha}{2})^\top$ with a smoothing level $\alpha\in(0, 1)$, which causes squeezing of values of the logit.
Optimal Kernel for Kernel-Based Modal Statistical Methods
Kernel-based modal statistical methods include mode estimation, regression, and clustering.
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains
Since the supports may have various granularities depending on attributes (e. g., poverty rate and crime rate), modeling such data is not straightforward.
Kernel Selection for Modal Linear Regression: Optimal Kernel and IRLS Algorithm
Modal linear regression (MLR) is a method for obtaining a conditional mode predictor as a linear model.
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs
By deriving the posterior GP, we can predict the data value at any location point by considering the spatial correlations and the dependences between areal data sets, simultaneously.
Generative model based on minimizing exact empirical Wasserstein distance
Generative Adversarial Networks (GANs) are a very powerful framework for generative modeling.
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities
With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity.
Low-rank matrix reconstruction and clustering via approximate message passing
We study the problem of reconstructing low-rank matrices from their noisy observations.
