Results 21 to 30 of about 459,246 (134)
A Computationally Efficient Limited Memory CMA-ES for Large Scale Optimization
, 2014 We propose a computationally efficient limited memory Covariance Matrix Adaptation Evolution Strategy for large scale optimization, which we call the LM-CMA-ES.
Hansen N. +3 more
core +1 more source
Exploiting persymmetry for low-rank Space Time Adaptive Processing [PDF]
Signal Processing, 2014 Publication in the conference proceedings of EUSIPCO, Bucharest, Romania ...
Ginolhac, Guillaume +3 more
openaire +3 more sources
Robust language recognition via adaptive language factor extraction [PDF]
, 2014 This paper presents a technique to adapt an acoustically based language classifier to the background conditions and speaker accents. This adaptation improves language classification on a broad spectrum of TV broadcasts. The core of the system consists of
Demuynck, Kris +2 more
core +1 more source
An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-Identification
, 2018 In recent years, a variety of proposed methods based on deep convolutional neural networks (CNNs) have improved the state of the art for large-scale person re-identification (ReID). While a large number of optimizations and network improvements have been
Jamieson, Michael +2 more
core +1 more source
WaRA: Wavelet Low Rank Adaptation
Parameter-efficient fine-tuning (PEFT) has gained widespread adoption across various applications. Among PEFT techniques, Low-Rank Adaptation (LoRA) and its extensions have emerged as particularly effective, allowing efficient model adaptation while significantly reducing computational overhead. However, existing approaches typically rely on global low-
Heidari, Moein +4 more
openaire +2 more sources
A Bayesian Interpretation of Adaptive Low-Rank Adaptation
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Motivated by the sensitivity-based importance score of the adaptive low-rank adaptation (AdaLoRA), we utilize more theoretically supported metrics, including the signal-to-noise ratio (SNR), along with the Improved Variational Online Newton (IVON) optimizer, for adaptive parameter budget allocation.
Chen, Haolin, Garner, Philip N.
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Adaptive Low-Rank Kernel Subspace Clustering
, 2017 In this paper, we present a kernel subspace clustering method that can handle non-linear models. In contrast to recent kernel subspace clustering methods which use predefined kernels, we propose to learn a low-rank kernel matrix, with which mapped data in feature space are not only low-rank but also self-expressive.
Ji, Pan +4 more
openaire +2 more sources
The Expressive Power of Low-Rank Adaptation
, 2023 40 pages, 5 ...
Zeng, Yuchen, Lee, Kangwook
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Norm-Bounded Low-Rank Adaptation
In this work, we propose norm-bounded low-rank adaptation (NB-LoRA) for parameter-efficient fine tuning. NB-LoRA is a novel parameterization of low-rank weight adaptations that admits explicit bounds on each singular value of the adaptation matrix, which can thereby satisfy any prescribed unitarily invariant norm bound, including the Schatten norms (e ...
Wang, Ruigang +2 more
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