Results 31 to 40 of about 249,885 (208)
The Quantization of Gravity: Quantization of the Hamilton Equations [PDF]
Universe, 2021 We quantize the Hamilton equations instead of the Hamilton condition. The resulting equation has the simple form −Δu=0 in a fiber bundle, where the Laplacian is the Laplacian of the Wheeler–DeWitt metric provided n≠4. Using then separation of variables, the solutions u can be expressed as products of temporal and spatial eigenfunctions, where the ...
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Journal of Physics: Condensed Matter, 2019 A dislocation, just like a phonon, is a type of atomic lattice displacement but subject to an extra topological constraint. However, unlike the phonon which has been quantized for decades, the dislocation has long remained classical. This article is a comprehensive review of the recent progress on quantized dislocations, aka the "dislon" theory.
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Quantization Dimension via Quantization Numbers [PDF]
Real Analysis Exchange, 2004 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kesseböhmer, Marc, Zhu, Sanguo
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Physical Review Letters, 2012 We propose a method for quantization of Lagrangians for which the Hamiltonian, as a function of momentum, is a branched function with cusps. Appropriate boundary conditions, which we identify, insure unitary time evolution. In special cases a dual (canonical) transformation maps the problem into a problem of quantum mechanics on singular spatial ...
Shapere, Alfred, Wilczek, Frank
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There is no “first” quantization [PDF]
Physics Letters A, 2003 The introduction of spinor and other massive fields by ``quantizing'' particles (corpuscles) is conceptually misleading. Only spatial fields must be postulated to form the fundamental objects to be quantized (that is, to define a formal basis for all quantum states), while apparent ``particles'' are a mere consequence of decoherence. This conclusion is
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HAQ: Hardware-Aware Automated Quantization With Mixed Precision [PDF]
Computer Vision and Pattern Recognition, 2018 Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference. Emergent DNN hardware accelerators begin to support mixed precision (1-8 bits) to further improve the computation efficiency, which raises a ...
Kuan Wang +4 more
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Journal of Physics A: Mathematical and General, 2001 20 pages, LaTex file, Substantially revised ...
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HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision [PDF]
IEEE International Conference on Computer Vision, 2019 Model size and inference speed/power have become a major challenge in the deployment of neural networks for many applications. A promising approach to address these problems is quantization.
Zhen Dong +4 more
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Quantized Feature Distillation for Network Quantization
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Neural network quantization aims to accelerate and trim full-precision neural network models by using low bit approximations. Methods adopting the quantization aware training (QAT) paradigm have recently seen a rapid growth, but are often conceptually complicated.
Zhu, Ke, He, Yin-Yin, Wu, Jianxin
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