Results 51 to 60 of about 573,498 (278)
IEEE AccessIn histopathological image analysis, the feature extraction task for classification proves to be demanding. This difficulty arises from the assortment of histological features appropriate for individual problems and the intricate presence of diverse ...
Usman Haider +5 more
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Ideguru, 2023 In the current era of disruption, the need to learn English is very important, therefore several digital applications have been created to make learning English easier, especially for students. U-Dictionary is a popular English learning application that
Feren Fedora, Moh. Ferdi Hasan
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Learning computationally efficient dictionaries and their implementation as fast transforms [PDF]
, 2015 Dictionary learning is a branch of signal processing and machine learning that aims at finding a frame (called dictionary) in which some training data admits a sparse representation. The sparser the representation, the better the dictionary.
Gribonval, Rémi, Magoarou, Luc Le
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Analysis Dictionary Learning: An Efficient and Discriminative Solution
, 2019 Discriminative Dictionary Learning (DL) methods have been widely advocated for image classification problems. To further sharpen their discriminative capabilities, most state-of-the-art DL methods have additional constraints included in the learning ...
Dai, Liyi +3 more
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Identifiability of Complete Dictionary Learning [PDF]
SIAM Journal on Mathematics of Data Science, 2019 Sparse component analysis (SCA), also known as complete dictionary learning, is the following problem: Given an input matrix $M$ and an integer $r$, find a dictionary $D$ with $r$ columns and a matrix $B$ with $k$-sparse columns (that is, each column of $B$ has at most $k$ non-zero entries) such that $M \approx DB$.
Cohen, Jérémy, Gillis, Nicolas
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Continual Learning for Multimodal Data Fusion of a Soft Gripper
Advanced Robotics Research, EarlyView.Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley +1 more source
Pooling-Invariant Image Feature Learning [PDF]
, 2013 Unsupervised dictionary learning has been a key component in state-of-the-art computer vision recognition architectures. While highly effective methods exist for patch-based dictionary learning, these methods may learn redundant features after the ...
Darrell, Trevor +2 more
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Advanced Robotics Research, EarlyView.This work presents a state‐adaptive Koopman linear quadratic regulator framework for real‐time manipulation of a deformable swab tool in robotic environmental sampling. By combining Koopman linearization, tactile sensing, and centroid‐based force regulation, the system maintains stable contact forces and high coverage across flat and inclined surfaces.
Siavash Mahmoudi +2 more
wiley +1 more source
Dependent Nonparametric Bayesian Group Dictionary Learning for online reconstruction of Dynamic MR images [PDF]
, 2015 In this paper, we introduce a dictionary learning based approach applied to the problem of real-time reconstruction of MR image sequences that are highly undersampled in k-space.
Kassim, Ashraf A. +2 more
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Slice-Based Online Convolutional Dictionary Learning
IEEE Transactions on Cybernetics, 2021 Convolutional dictionary learning (CDL) aims to learn a structured and shift-invariant dictionary to decompose signals into sparse representations. While yielding superior results compared to traditional sparse coding methods on various signal and image processing tasks, most CDL methods have difficulties handling large data, because they have to ...
Yijie Zeng, Jichao Chen, Guang-Bin Huang
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