arXiv research

MKPN predicts varying-sized kernels for burst image denoising.

A new multi-kernel RBFNN design improves performance and speed.

Paper improves generalization bounds for multi-kernel learning with mixed datasets.

Study uses multi-kernel Hawkes models to analyze high-frequency price dynamics.

We develop a multi-kernel based regression method for graph signal processing where the target signal is assumed to be smooth over a graph. In multi-kernel regression, an effective kernel function is expressed as a linear combination of many basis kernel functions. We estimate the linear weights to learn the effective …

Paper introduces a new multi-kernel algorithm for better gradient approximation.

New method preserves privacy while improving machine learning accuracy.

SimpleMKKM improves multi-kernel clustering efficiency.

Kernel-based methods exhibit well-documented performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. Especially when the latter is not available, multi-kernel learning has gained popularity thanks to its flexibility in ch…

MKCapsnet improves schizophrenia identification using multi-kernels and dropout.

A new kernel function centers at different points improves robust learning.

In this paper, we provide a Banach-space formulation of supervised learning with generalized total-variation (gTV) regularization. We identify the class of kernel functions that are admissible in this framework. Then, we propose a variation of supervised learning in a continuous-domain hybrid search space with gTV regu…

Enhanced LSTM with multiple kernels and attention improves video action recognition.

Inference of space-time varying signals on graphs emerges naturally in a plethora of network science related applications. A frequently encountered challenge pertains to reconstructing such dynamic processes, given their values over a subset of vertices and time instants. The present paper develops a graph-aware kernel…

Multi-kernel learning has been well explored in the recent past and has exhibited promising outcomes for multi-class classification and regression tasks. In this paper, we present a multiple kernel learning approach for the One-class Classification (OCC) task and employ it for anomaly detection. Recently, the basic mul…

Signal processing tasks as fundamental as sampling, reconstruction, minimum mean-square error interpolation and prediction can be viewed under the prism of reproducing kernel Hilbert spaces. Endowing this vantage point with contemporary advances in sparsity-aware modeling and processing, promotes the nonparametric basi…

Image understanding using deep convolutional network has reached human-level performance, yet a closely related problem of video understanding especially, action recognition has not reached the requisite level of maturity. We combine multi-kernels based support-vector-machines (SVM) with a multi-stream deep convolution…

In recent work, it was shown that combining multi-kernel based support vector machines (SVMs) can lead to near state-of-the-art performance on an action recognition dataset (HMDB-51 dataset). This was 0.4\% lower than frameworks that used hand-crafted features in addition to the deep convolutional feature extractors. I…

The medical research facilitates to acquire a diverse type of data from the same individual for particular cancer. Recent studies show that utilizing such diverse data results in more accurate predictions. The major challenge faced is how to utilize such diverse data sets in an effective way. In this paper, we introduc…

The smart grid vision entails advanced information technology and data analytics to enhance the efficiency, sustainability, and economics of the power grid infrastructure. Aligned to this end, modern statistical learning tools are leveraged here for electricity market inference. Day-ahead price forecasting is cast as a…

This paper describes a method for learning low-dimensional approximations of nonlinear dynamical systems, based on neural-network approximations of the underlying Koopman operator. Extended Dynamic Mode Decomposition (EDMD) provides a useful data-driven approximation of the Koopman operator for analyzing dynamical syst…

A deep neural network for spatial time series forecasting.

New framework reduces LLM complexity by directly finetuning in Boolean domain.

ARM improves multivariate time series forecasting by better capturing series-wise relationships.

Kernel k-Means algorithm improves clustering of non-linear data.

Paper proposes Langevin dynamics for adaptive IRL of stochastic gradient algorithms.

A number of applications in engineering, social sciences, physics, and biology involve inference over networks. In this context, graph signals are widely encountered as descriptors of vertex attributes or features in graph-structured data. Estimating such signals in all vertices given noisy observations of their values…

Study identifies five AD subtypes using graph diffusion and similarity learning.

Fuzzy prediction sets generalize binary predictions to include elements at varying confidence levels.

Paper defines predictive multiplicity and measures its severity in classification problems.

A novel continual prediction model outperforms traditional one-time models in predicting AKI.

This paper re-examines conformal e-prediction and its advantages over conformal prediction.

Two new methods improve efficiency of conformal predictive systems.

Self-calibrating conformal prediction improves interval efficiency and offers a practical alternative.

Study uses deep learning to predict asset prices, finds complex target processes lead to meaningless predictions.

The paper emphasizes the importance of joint predictions over marginal predictions for decision-making.

Behavior modification improves prediction accuracy by nudging user behavior.

New adaptive conformal predictive systems developed.

Predictions can shape outcomes, study helps predict these effects.

Proposes feature conformal prediction for broader application in semantic feature spaces.

AutoCP automates the construction of accurate prediction intervals.

Proposes a method to apply conformal prediction to probabilistic time series forecasting models.

ICP improves prediction intervals for continuous outcomes at lower computational cost.

Optimizes predictions for specific tasks using parametrized decision analysis.

New measures for prediction validity and consonant plausibility introduced.

RFpredInterval package builds prediction intervals for random forests and boosted forests.

FPPI selectively uses predictions to improve inference efficiency.

COP improves online conformal prediction by incorporating data patterns, leading to tighter prediction sets.