Results 41 to 50 of about 489,349 (320)
Frontiers in Synaptic Neuroscience, 2014 Long-term synaptic plasticity is fundamental to learning and network function. It has been studied under various induction protocols and depends on firing rates, membrane voltage, and precise timing of action potentials.These protocols show different ...
Daniel eKrieg, Jochen eTriesch
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An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features
Vision, 2019 The formation of structure in the visual system, that is, of the connections between cells within neural populations, is by and large an unsupervised learning process.
Laurent U. Perrinet
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Explicit Object Representation by Sparse Neural Codes [PDF]
, 2008 Neurons have been identified in the human medial temporal lobe (MTL) that display a strong selectivity for only a few stimuli (such as familiar individuals or landmark buildings) out of perhaps 100 presented to the test subject.
Waydo, Stephen J.
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Efficient Dictionary Learning with Sparseness-Enforcing Projections [PDF]
International Journal of Computer Vision, 2015 Learning dictionaries suitable for sparse coding instead of using engineered bases has proven effective in a variety of image processing tasks. This paper studies the optimization of dictionaries on image data where the representation is enforced to be ...
Markus Thom, M. Rapp, G. Palm
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2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), 2020 MP4 is currently the gold standard for video compression. Here we demonstrate that MP4 can be augmented by using a spatiotemporal sparse code optimized for the reconstruction of video portraits to up-sample data streams in which 75% of the pixels have been removed.
Daniel A. Wang +5 more
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Sparse-to-Sparse Training of Diffusion Models [PDF]
CoRRDiffusion models (DMs) are a powerful type of generative models that have achieved state-of-the-art results in various image synthesis tasks and have shown potential in other domains, such as natural language processing and temporal data modeling.
OLIVEIRA, Inês +2 more
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Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference
CoRR, 2019 Computations for the softmax function are significantly expensive when the number of output classes is large. In this paper, we present a novel softmax inference speedup method, Doubly Sparse Softmax (DS-Softmax), that leverages sparse mixture of sparse experts to efficiently retrieve top-k classes. Different from most existing methods that require and
Shun Liao +4 more
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A Soft Measurement Method for Carbon Content of Fly Ash Based on Sparseness Approach for LS-SVM
南方能源建设, 2019 [Introduction] The paper aims to establish a sparseness approach based sample distribution for LS-SVM models to solve the problem of excessive computation in the application of classical iterative shearing sparseness algorithm for the soft measurement ...
ZHANG Dahai +4 more
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SAC-NMF-Driven Graphical Feature Analysis and Applications
Machine Learning and Knowledge Extraction, 2020 Feature analysis is a fundamental research area in computer graphics; meanwhile, meaningful and part-aware feature bases are always demanding. This paper proposes a framework for conducting feature analysis on a three-dimensional (3D) model by ...
Nannan Li +3 more
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Augmented Collaborative Filtering for Sparseness Reduction in Personalized POI Recommendation
ACM Transactions on Intelligent Systems and Technology, 2017 As mobile device penetration increases, it has become pervasive for images to be associated with locations in the form of geotags. Geotags bridge the gap between the physical world and the cyberspace, giving rise to new opportunities to extract further ...
C. Cui +4 more
semanticscholar +1 more source