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Found 11 papers, 1 papers with code
Sensor Network Localization via Riemannian Conjugate Gradient and Rank Reduction: An Extended Version
The SNL is formulated as an Euclidean Distance Matrix Completion (EDMC) problem under the unit ball sample model.
Generalization Error Curves for Analytic Spectral Algorithms under Power-law Decay
The generalization error curve of certain kernel regression method aims at determining the exact order of generalization error with various source condition, noise level and choice of the regularization parameter rather than the minimax rate.
Optimal Rates of Kernel Ridge Regression under Source Condition in Large Dimensions
Motivated by the studies of neural networks (e. g., the neural tangent kernel theory), we perform a study on the large-dimensional behavior of kernel ridge regression (KRR) where the sample size $n \asymp d^{\gamma}$ for some $\gamma > 0$.
Mixed Distillation Helps Smaller Language Model Better Reasoning
While large language models (LLMs) have demonstrated exceptional performance in recent natural language processing (NLP) tasks, their deployment poses substantial challenges due to high computational and memory demands in real-world applications.
Optimal Rate of Kernel Regression in Large Dimensions
We perform a study on kernel regression for large-dimensional data (where the sample size $n$ is polynomially depending on the dimension $d$ of the samples, i. e., $n\asymp d^{\gamma}$ for some $\gamma >0$ ).
On the Optimality of Misspecified Kernel Ridge Regression
In the misspecified kernel ridge regression problem, researchers usually assume the underground true function $f_{\rho}^{*} \in [\mathcal{H}]^{s}$, a less-smooth interpolation space of a reproducing kernel Hilbert space (RKHS) $\mathcal{H}$ for some $s\in (0, 1)$.
On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains
In this paper, we provide a strategy to determine the eigenvalue decay rate (EDR) of a large class of kernel functions defined on a general domain rather than $\mathbb S^{d}$.
Kernel interpolation generalizes poorly
One of the most interesting problems in the recent renaissance of the studies in kernel regression might be whether the kernel interpolation can generalize well, since it may help us understand the `benign overfitting henomenon' reported in the literature on deep networks.
Development of a Hardware-in-the-loop Testbed for Laboratory Performance Verification of Flexible Building Equipment in Typical Commercial Buildings
The development of strategies that exploit these flexibilities could be facilitated by publicly available high-resolution datasets illustrating how control of HVAC systems in commercial buildings can be used in different climate zones to shape the energy use profile of a building for grid needs.
An Integrated Enhancement Solution for 24-hour Colorful Imaging
The current industry practice for 24-hour outdoor imaging is to use a silicon camera supplemented with near-infrared (NIR) illumination.
Instance-level Human Parsing via Part Grouping Network
Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing multiple instances in a single pass.
Ranked #6 on
Human Part Segmentation
on CIHP
