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Found 21 papers, 5 papers with code
The phase diagram of kernel interpolation in large dimensions
The generalization ability of kernel interpolation in large dimensions (i. e., $n \asymp d^{\gamma}$ for some $\gamma>0$) might be one of the most interesting problems in the recent renaissance of kernel regression, since it may help us understand the 'benign overfitting phenomenon' reported in the neural networks literature.
Multi-target Detection for Reconfigurable Holographic Surfaces Enabled Radar
Multi-target detection is one of the primary tasks in radar-based localization and sensing, typically built on phased array antennas.
Unified Near-field and Far-field Localization with Holographic MIMO
In this paper, a unified NF and FF localization method is proposed for the RIS-enabled localization system to overcome the above issue.
Reconfigurable Holographic Surface Aided Wireless Simultaneous Localization and Mapping
As a crucial facilitator of future autonomous driving applications, wireless simultaneous localization and mapping (SLAM) has drawn growing attention recently.
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 $\gamma > 0$.
Understanding Deep Gradient Leakage via Inversion Influence Functions
Deep Gradient Leakage (DGL) is a highly effective attack that recovers private training images from gradient vectors.
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 $\gamma >0$ ).
Safe and Robust Watermark Injection with a Single OoD Image
Training a high-performance deep neural network requires large amounts of data and computational resources.
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 $s\in (0, 1)$.
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.
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection
Developing a mechanism for battling financial crimes is an impending task that requires in-depth collaboration from multiple institutions, and yet such collaboration imposed significant technical challenges due to the privacy and security requirements of distributed financial data.
Reconfigurable Holographic Surface: A New Paradigm to Implement Holographic Radio
Ultra-massive multiple-input multiple-output (MIMO) is one of the key enablers in the forthcoming 6G networks to provide high-speed data services by exploiting spatial diversity.
Holographic Integrated Sensing and Communications: Principles, Technology, and Implementation
We first introduce the hardware structure and working principle of the RHS and then propose a novel holographic beamforming scheme for ISAC.
MetaRadar: Multi-target Detection for Reconfigurable Intelligent Surface Aided Radar Systems
To address this issue, we propose to use the reconfigurable intelligent surface (RIS) to improve the detection accuracy of radar systems due to its capability to customize channel conditions by adjusting its phase shifts, which is referred to as MetaRadar.
From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization
In the setting of federated optimization, where a global model is aggregated periodically, step asynchronism occurs when participants conduct model training by efficiently utilizing their computational resources.
Joint Progressive and Coarse-to-fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusion
Registration of brain MRI images requires to solve a deformation field, which is extremely difficult in aligning intricate brain tissues, e. g., subcortical nuclei, etc.
Wireless Indoor Simultaneous Localization and Mapping Using Reconfigurable Intelligent Surface
In this paper, we propose a novel SLAM system assisted by a reconfigurable intelligent surface (RIS) to address this issue.
MetaLocalization: Reconfigurable Intelligent Surface Aided Multi-user Wireless Indoor Localization
As the RIS is able to customize the radio channels by adjusting the phase shifts of the signals reflected at the surface, the localization accuracy in the RIS aided scheme can be improved by choosing the proper phase shifts with significant differences of RSS values among adjacent locations.
Multilingual Approach to Joint Speech and Accent Recognition with DNN-HMM Framework
Experimental results on an 8-accent English speech recognition show both methods can yield WERs close to the conventional ASR systems that completely ignore the accent, as well as desired AR accuracy.
MetaRadar: Indoor Localization by Reconfigurable Metamaterials
However, in MetaRadar, it is challenging to build radio maps for all the radio environments generated by metamaterial units and select suitable maps from all the possible maps to realize a high accuracy localization.
Towards Ubiquitous Positioning by Leveraging Reconfigurable Intelligent Surface
The received signal strength (RSS) based technique is widely utilized for ubiquitous positioning due to its advantage of simple implementability.
