Results 41 to 50 of about 1,721,209 (309)
A binary neural k-nearest neighbour technique [PDF]
, 2005 K-Nearest Neighbour (k-NN) is a widely used technique for classifying and clustering data. K-NN is effective but is often criticised for its polynomial run-time growth as k-NN calculates the distance to every other record in the data set for each record ...
Austin, J., Hodge, V.J.
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HF-SNN: High-Frequency Spiking Neural Network
IEEE Access, 2021 As the third generation of neural networks, spiking neural network (SNN) motivated by neurophysiology enjoys considerable advances due to integrating different information, such as time and space.
Jing Su, Jing Li
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Binary Domain Generalization for Sparsifying Binary Neural Networks
, 2023 Accepted as conference paper at ECML PKDD ...
Riccardo Schiavone +2 more
openaire +3 more sources
Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks [PDF]
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. While existing methods focus on large and complex models, the need for real-time detection demands greater efficiency.
Romeo Lanzino +4 more
semanticscholar +1 more source
Acta Scientiarum: Technology, 2011 The Law of Mass Action generally models the equilibrium data from ion exchange processes. This methodology is rigorous in terms of thermodynamics and takes into consideration the non-idealities in the solid and aqueous phases.
Rafael Luan Sehn Canevesi +5 more
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Fluids, 2023 The fluidization of certain biomasses used in thermal processes, such as sawdust, is particularly difficult due to their irregular shapes, varied sizes, and low densities, causing high minimum fluidization velocities (Umf).
Andres Reyes-Urrutia +5 more
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BiPer: Binary Neural Networks Using a Periodic Function [PDF]
Computer Vision and Pattern RecognitionQuantized neural networks employ reduced precision representations for both weights and activations. This quantization process significantly reduces the memory requirements and computational complexity of the network.
Edwin Vargas +3 more
semanticscholar +1 more source
Physics Letters B, 2020 The groundbreaking discoveries of gravitational waves from binary black-hole mergers [1–3] and, most recently, coalescing neutron stars [4] started a new era of Multi-Messenger Astrophysics and revolutionized our understanding of the Cosmos.
Plamen G. Krastev
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IFS-Based Image Reconstruction of Binary Images with Functional Networks
Mathematics, 2022 This work addresses the IFS-based image reconstruction problem for binary images. Given a binary image as the input, the goal is to obtain all the parameters of an iterated function system whose attractor approximates the input image accurately; the ...
Akemi Gálvez +6 more
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On the role of synaptic stochasticity in training low-precision neural networks [PDF]
, 2018 Stochasticity and limited precision of synaptic weights in neural network models are key aspects of both biological and hardware modeling of learning processes.
Baldassi, Carlo +6 more
core +4 more sources