Search Results for author:
Found 16 papers, 3 papers with code
Reflection-Bench: probing AI intelligence with reflection
The ability to adapt beliefs or behaviors in response to unexpected outcomes, reflection, is fundamental to intelligent systems' interaction with the world.
Robust Square Root Unscented Kalman filter of graph signals
In addition, in order to enhance the numerical stability of the unscented Kalman filter, the algorithm combines the double square root decomposition method to update the covariance matrix in the graph frequency domain.
ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models
Inspired by the awesome development of role-playing agents, we propose an ESC Evaluation framework (ESC-Eval), which uses a role-playing agent to interact with ESC models, followed by a manual evaluation of the interactive dialogues.
MLLMGuard: A Multi-dimensional Safety Evaluation Suite for Multimodal Large Language Models
In this paper, we present MLLMGuard, a multidimensional safety evaluation suite for MLLMs, including a bilingual image-text evaluation dataset, inference utilities, and a lightweight evaluator.
An Improved Robust Total Logistic Distance Metric algorithm for Generalized Gaussian Noise and Noisy Input
Although the known maximum total generalized correntropy (MTGC) and generalized maximum blakezisserman total correntropy (GMBZTC) algorithms can maintain good performance under the errors-in-variables (EIV) model disrupted by generalized Gaussian noise, their requirement for manual ad-justment of parameters is excessive, greatly increasing the practical difficulty of use.
OVEL: Large Language Model as Memory Manager for Online Video Entity Linking
In this paper, we propose a task called Online Video Entity Linking OVEL, aiming to establish connections between mentions in online videos and a knowledge base with high accuracy and timeliness.
Adaptive Unscented Kalman Filter under Minimum Error Entropy with Fiducial Points for Non-Gaussian Systems
By adding the correntropy to the error entropy, the proposed algorithm further enhances the ability of suppressing impulse noise and outliers.
Generalized Minimum Error with Fiducial Points Criterion for Robust Learning
In this paper, a generalized minimum error with fiducial points criterion (GMEEF) is presented by adopting the Generalized Gaussian Density (GGD) function as kernel.
L0-norm constraint normalized subband adaptive filtering algorithm: Performance development and AEC application
Limited by fixed step-size and sparsity penalty factor, the conventional sparsity-aware normalized subband adaptive filtering (NSAF) type algorithms suffer from trade-off requirements of high filtering accurateness and quicker convergence behavior for sparse system identification.
Robust Total Least Mean M-Estimate normalized subband filter Adaptive Algorithm for impulse noises and noisy inputs
In addition, this paper also conducts a detailed theoretical performance analysis of the TLMM-NSAF algorithm and obtains the stable step size range and theoretical steady-state mean squared deviation (MSD) of the algorithm.
Robust Multitask Diffusion Normalized M-estimate Subband Adaptive Filtering Algorithm Over Adaptive Networks
However, its performance is mainly limited by two aspects, i. e, the correlated input signal and impulsive noise interference.
Robustness of Maximum Correntropy Estimation Against Large Outliers
The maximum correntropy criterion (MCC) has recently been successfully applied in robust regression, classification and adaptive filtering, where the correntropy is maximized instead of minimizing the well-known mean square error (MSE) to improve the robustness with respect to outliers (or impulsive noises).
Kernel Risk-Sensitive Loss: Definition, Properties and Application to Robust Adaptive Filtering
Nonlinear similarity measures defined in kernel space, such as correntropy, can extract higher-order statistics of data and offer potentially significant performance improvement over their linear counterparts especially in non-Gaussian signal processing and machine learning.
Maximum Correntropy Kalman Filter
Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption.
Diffusion Maximum Correntropy Criterion Algorithms for Robust Distributed Estimation
Robust diffusion adaptive estimation algorithms based on the maximum correntropy criterion (MCC), including adaptation to combination MCC and combination to adaptation MCC, are developed to deal with the distributed estimation over network in impulsive (long-tailed) noise environments.
Generalized Correntropy for Robust Adaptive Filtering
In this work, we propose a generalized correntropy that adopts the generalized Gaussian density (GGD) function as the kernel (not necessarily a Mercer kernel), and present some important properties.
