Results 281 to 290 of about 459,286 (299)

Benchmarking Compact VLMs for Clip-Level Surveillance Anomaly Detection Under Weak Supervision. [PDF]

J Imaging
Borodin K, Kondrashov K, Vasiliev N, Gladkova K, Larina I, Gorodnichev M, Mkrtchian G.   +6 more
europepmc   +1 more source

Laplacian-LoRA: Delaying Oversmoothing in Deep GCNs via Spectral Low-Rank Adaptation

Sai Vamsi Alisetti
openalex   +1 more source

Deep proteogenomic characterization of pancreatic solid pseudopapillary neoplasm reveals unique features distinct from other pancreatic tumors. [PDF]

Biomark Res
Tanaka A, Otani Y, Klimstra DS, Basturk O, Vyas MM, Wang JY, Roehrl MHA.   +6 more
europepmc   +1 more source

cMeta-INR: cohort-informed meta-learning-based implicit neural representation for deformable registration-driven real-time volumetric MRI estimation. [PDF]

Phys Med Biol
Qian X, Shao HC, Cai J, Zhang Y.
europepmc   +1 more source

Combining datasets with different ground truths using Low-Rank Adaptation to generalize image-based CNN models for photometric redshift prediction [PDF]

Vikram Seenivasan, Srinath Saikrishnan, Andrew Lizarraga, J. Miguel Soriano, Bernie Boscoe, T. Do   +5 more
openalex  

PRIMAL: Processing-In-Memory Based Low-Rank Adaptation for LLM Inference Accelerator [PDF]

Yue Jiet Chong, Yimin Wang, Zhen Wu, Xuanyao Fong   +3 more
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Parameter-Efficient Adaptation of Foundation Models via Low-Rank Factorization

Wei Liang, Mingxia Chen, Shufen Zhihao
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Low rank transform domain adaptive filtering

Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136), 2002
This paper introduces a least squares, matrix based framework for adaptive filtering that includes Normalized LMS, Affine Projection, and Recursive Least Squares as special cases. We then introduce other transform domain based methods and show how to create optimal low rank versions. We also discuss efficient implementation of our method.
Linebarger, Darel, Raghothaman, Balaji, Begušić, Dinko, Dowling, Eric, DeGroat, Ronald, Oh, Stephen   +5 more
openaire   +2 more sources

Adaptive Low Rank Matrix Completion

IEEE Transactions on Signal Processing, 2017
The low-rank matrix completion problem is fundamental to a number of tasks in data mining, machine learning, and signal processing. This paper considers the problem of adaptive matrix completion in time-varying scenarios. Given a sequence of incomplete and noise-corrupted matrices, the goal is to recover and track the underlying low rank matrices ...
Ruchi Tripathi, Boda Mohan, Ketan Rajawat   +2 more
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Low-rank representation with adaptive graph regularization

Neural Networks, 2018
Low-rank representation (LRR) has aroused much attention in the community of data mining. However, it has the following twoproblems which greatly limit its applications: (1) it cannot discover the intrinsic structure of data owing to the neglect of the local structure of data; (2) the obtained graph is not the optimal graph for clustering. To solve the
Jie Wen, Xiaozhao Fang, Yong Xu, Chunwei Tian, Lunke Fei   +4 more
openaire   +3 more sources