24 papers · ranked by Valyu relevance
Zheng Zhang, Yong Xu, Jian Yang, Xuelong Li + 1 more
—Sparse representation has attracted much attention from researchers in fields of signal processing, image processing, computer vision and pattern recognition. Sparse representation also has a good reputation in both theoretical research and practical applications. Many different algorithms have been proposed for…
Maliheh Miri, Mohammad Taghi Sadeghi, Vahid Abootalebi
Sparse representation of signals has achieved satisfactory results in classification applications compared to the conventional methods. Microarray data, which are obtained from monitoring the expression levels of thousands of genes simultaneously, have very high dimensions in relation to the small number of samples.…
Nallig Leal, Eduardo Zurek, Esmeide Leal
Magnetic Resonance (MR) Imaging is a diagnostic technique that produces noisy images, which must be filtered before processing to prevent diagnostic errors. However, filtering the noise while keeping fine details is a difficult task. This paper presents a method, based on sparse representations and singular value…
Vincent Liu, Raksha Kumaraswamy, Lei Le, Martha White
We investigate sparse representations for control in reinforcement learning. While these representations are widely used in computer vision, their prevalence in reinforcement learning is limited to sparse coding where extracting representations for new data can be computationally intensive. Here, we begin by…
Mohammad Abbasi, Connor R. Sanderford, Narendiran Raghu, Mirjeta Pasha + 2 more
'Mirjeta Pasha' 'Benjamin B. Bartelle' 'Gennady S. Cymbalyuk'] Unsupervised learning methods are commonly used to detect features within transcriptomic data and ultimately derive meaningful representations of biology. Contributions of individual genes to any feature however becomes convolved with each learning step…
Esben Plenge, Stefan S. Klein, Wiro J. Niessen, Erik Meijering + 1 more
'Enrique Hernandez-Lemus'] Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels. In this…
Mohammad Mahdi Jahani Yekta
We show, based on the following three grounds, that the primary visual cortex (V1) is a biological direct-shortcut deep residual learning neural network (ResNet) for sparse visual processing: (1) We first highlight that Gabor-like sets of basis functions, which are similar to the receptive fields of simple cells in the…
Michael Beyeler, Emily Rounds, Kristofor D. Carlson, Nikil Dutt + 1 more
Supported by recent computational studies, sparse coding and dimensionality reduction are emerging as a ubiquitous coding strategy across brain regions and modalities, allowing neurons to achieve nonnegative sparse coding (NSC) by efficiently encoding high-dimensional stimulus spaces using a sparse and parts-based…
Fabio Massimo Zennaro, Ke Chen
In this paper we present a theoretical analysis to understand sparse filtering, a recent and effective algorithm for unsupervised learning. The aim of this research is not to show whether or how well sparse filtering works, but to understand why and when sparse filtering does work. We provide a thorough theoretical…
Costa, Valérie, Fel, Thomas + 6 more
Sparse autoencoders (SAEs) have recently become central tools for interpretability, leveraging dictionary learning principles to extract sparse, interpretable features from neural representations whose underlying structure is typically unknown. This paper evaluates SAEs in a controlled setting using MNIST, which…
Laurent Perrinet
``` @inbook{Perrinet15sparse, Author = {Perrinet, Laurent U.}, Chapter = {Sparse models for Computer Vision}, Citeulike-Article-Id = {13514904}, Editor = {Crist{\'{o}}bal, Gabriel and Keil, Matthias S. and Perrinet, Laurent U.}, Keywords = {bicv-sparse, sanz12jnp, vacher14}, month = nov, chapter = {14}, Priority = {0}…
Rishabh Raj, Dar Dahlen, Kyle Duyck, C. Ron Yu
The brain has a remarkable ability to recognize objects from noisy or corrupted sensory inputs. How this cognitive robustness is achieved computationally remains unknown. We present a coding paradigm, which encodes structural dependence among features of the input and transforms various forms of the same input into the…
Mohammad Abbasi, Connor Sanderford, Narendian Raghu, Benjamin B Bartelle
We have developed representation learning methods, specifically to address the constraints and advantages of complex spatial data. Sparse filtering (SFt), uses principles of sparsity and mutual information to build representations from both global and local features from a minimal list of samples. Critically, the…
Krishnakumar Balasubramanian, Kai Yu, Guy Lebanon
We propose and analyze a novel framework for learning sparse representations, based on two statistical techniques: kernel smoothing and marginal regression. The proposed approach provides a flexible framework for incorporating feature similarity or temporal information present in data sets, via non-parametric kernel…
Xian Wei, Hao Shen, Martin Kleinsteuber
—This work studies the problem of learning appropriate low dimensional image representations. We propose a generic algorithmic framework, which leverages two classic representation learning paradigms, i.e., sparse representation and the trace quotient criterion, to disentangle underlying factors of variation in high…
Yanbo Lian, Anthony N. Burkitt
Sparse coding, predictive coding and divisive normalization have each been found to be principles that underlie the function of neural circuits in many parts of the brain, supported by substantial experimental evidence. However, the connections between these related principles are still poorly understood. In this…
Sunil Kumar Prabhakar, Young-Gi Ju, Harikumar Rajaguru, Dong-Ok Won
In comparison to other biomedical signals, electroencephalography (EEG) signals are quite complex in nature, so it requires a versatile model for feature extraction and classification. The structural information that prevails in the originally featured matrix is usually lost when dealing with standard feature…
Sanjar Adilov
Machine learning models for molecular-property prediction typically work with molecular representations in the form of fingerprints, descriptors, or graphs. In case of fingerprints and descriptors, molecular representations usually comprise thousands of features, which causes the curse of dimensionality for many…
Esben Plenge, Stefan Klein, Wiro J. Niessen, Erik Meijering
The second author’s name is spelled incorrectly. The correct name is: Stefan Klein. The correct citation is: Plenge E, Klein S, Niessen WJ, Meijering E (2015) Multiple Sparse Representations Classification. PLoS ONE 10(7): e0131968. doi:[10.1371/journal.pone.0131968]()
Kelsey Hatzell, Yanjie Zheng
X-ray Computed Tomography (CT) is a non-invasive, non-destructive approach to imaging materials, material systems and engineered components in two- and three- dimensions. Acquisition of 3D images requires the collection of hundreds or thousands of through-thickness X-ray radiographic images from different angles. Such…
Anthony Onwuli, Keith T. Butler, Aron Walsh
High-dimensional representations of the elements have become common within the field of materials informatics to build useful, structure-agnostic models for the chemistry of materials. However, the characteristics of elements change when they adopt a given oxidation state, with distinct structural preferences and…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
Stamatia Zavitsanou, Zonghua Bo, Emanuele Casali, Matthew Langton + 1 more
Machine learning (ML) is currently transforming the field of chemistry by offering unparalleled efficiency in addressing complex challenges. Despite the progress made, a notable gap persists in the availability of user-friendly tools tailored to chemical problems involving small and sparse datasets. Here, we introduce…
Ping Yang, E. Adrian Henle, Cory M. Simon, Xiaoli Fern
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a data set of 382 molecules with toxicity labels from…