Results 61 to 70 of about 1,871,059 (300)

Sparse representations for text categorization [PDF]

Interspeech 2010, 2010
Sparse representations (SRs) are often used to characterize a test signal using few support training examples, and allow the number of supports to be adapted to the specific signal being categorized. Given the good performance of SRs compared to other classifiers for both image classification and phonetic classification, in this paper, we extended the ...
Tara N. Sainath, Sameer Maskey, Dimitri Kanevsky, Bhuvana Ramabhadran, David Nahamoo, Julia Hirschberg   +5 more
openaire   +1 more source

Unlearning via Sparse Representations

CoRR, 2023
Machine \emph{unlearning}, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be costly and infeasible by existing techniques. We propose a nearly compute-free zero-shot unlearning technique based on a discrete representational bottleneck. We show that the proposed technique efficiently unlearns the forget set
Vedant Shah, Frederik Träuble, Ashish Malik, Hugo Larochelle, Michael Mozer, Sanjeev Arora, Yoshua Bengio, Anirudh Goyal   +7 more
openaire   +2 more sources

Design and analysis strategies for robust microbiome ageing research

FEBS Letters, EarlyView.
The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik, İsmail Güderer, Yi Wang, Tayyaba Alvi, Prasoon Pandey, Handan Melike Dönertaş   +5 more
wiley   +1 more source

Sparse Matrix Representation for Web Opinions [PDF]

, 2012
Due to the advancement of Web 2.0 technologies, a large volume of Web opinions is available on social media sites such as Web forums and Weblogs. These technologies provide a platform for Internet users around the world to communicate with each other and
Dileep kumar B R, Dr.L.Venkateswara Reddy   +1 more
core  

Convolutional sparse coding network for sparse seismic time-frequency representation

Artificial Intelligence in Geosciences
Seismic time-frequency (TF) transforms are essential tools in reservoir interpretation and signal processing, particularly for characterizing frequency variations in non-stationary seismic data.
Qiansheng Wei, Zishuai Li, Haonan Feng, Yueying Jiang, Yang Yang, Zhiguo Wang   +5 more
doaj   +1 more source

Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification

Remote Sensing, 2017
Sparse Representation has been widely applied to classification of hyperspectral images (HSIs). Besides spectral information, the spatial context in HSIs also plays an important role in the classification.
Fei Tong, Hengjian Tong, Junjun Jiang, Yun Zhang   +3 more
doaj   +1 more source

Investigating transcription factor dynamics in health and disease using FRAP

FEBS Letters, EarlyView.
FRAP analysis of GFP‐tagged transcription factors reveals how molecular mobility and target engagement change in response to drug treatment. By combining live‐cell imaging, quantitative model fitting, and statistical analysis, this approach uncovers transcription factor dynamics linked to disease mechanisms, providing a powerful framework for ...
Kannan Govindaraj, Carolina Vazquez Garzon, Jose Joaquin Velasco, Janine N. Post   +3 more
wiley   +1 more source

Image fusion based on group sparse representation

, 2016
Sparse representation based image fusion has been widely studied recently. However, it's not popular in some fields for the high time complexity. In this paper, a new image fusion method based on group sparse representation is proposed to overcome ...
Song, Zongxi, Zongxi Song, Gao, Wei, Fei Yin, Wei Gao, Yin, Fei   +5 more
core   +1 more source

Adaptive Iterated Shrinkage Thresholding-Based L_p-Norm Sparse Representation for Hyperspectral Imagery Target Detection

Remote Sensing, 2020
In recent years, with the development of compressed sensing theory, sparse representation methods have been concerned by many researchers. Sparse representation can approximate the original image information with less space storage. Sparse representation
Xiaobin Zhao, Wei Li, Mengmeng Zhang, Ran Tao, Pengge Ma   +4 more
doaj   +1 more source

Invisible but not inaccessible—Revealing transient oligomers formed by intrinsically disordered proteins with solution NMR and complementary methods

FEBS Letters, EarlyView.
Transient oligomers formed by intrinsically disordered proteins may be ‘invisible’ to direct detection yet remain accessible to solution NMR through equilibrium‐exchange measurements and pressure‐jump experiments. Complementary methods report on mass, stoichiometry, selected distance distributions, morphology, and internal packing.
Martin D. Gelenter, Ad Bax
wiley   +1 more source