Relevant bibliographies by topics / Quantization

Academic literature on the topic 'Quantization'

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Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Quantization"

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Borisov, Leonid A., and Yuriy N. Orlov. "On the generalization of moyal equation for an arbitrary linear quantization." Infinite Dimensional Analysis, Quantum Probability and Related Topics 24, no. 01 (March 2021): 2150003. http://dx.doi.org/10.1142/s021902572150003x.

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For an arbitrary linear combination of quantizations, the kernel of the inverse operator is constructed. An equation for the evolution of the Wigner function for an arbitrary linear quantization is derived and it is shown that only for Weyl quantization this equation does not contain a source of quasi-probability. Stationary solutions for the Wigner function of a harmonic oscillator are constructed, depending on the characteristic function of the quantization rule. In the general case of Hermitian linear quantization these solutions are real but not positive. We found the representation of Weyl quantization in the form of the limit of a sequence of linear Hermitian quantizations, such that for each element of this sequence the stationary solution of the Moyal equation is positive.
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Klauder, John R. "A New Proposal to Create a Valid Quantization of Einstein’s Gravity." Entropy 24, no. 10 (September 27, 2022): 1374. http://dx.doi.org/10.3390/e24101374.

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Canonical quantization has created many valid quantizations that require infinite-line coordinate variables. However, the half-harmonic oscillator, which is limited to the positive coordinate half, cannot receive a valid canonical quantization because of the reduced coordinate space. Instead, affine quantization, which is a new quantization procedure, has been deliberately designed to handle the quantization of problems with reduced coordinate spaces. Following examples of what affine quantization is, and what it can offer, a remarkably straightforward quantization of Einstein’s gravity is attained, in which a proper treatment of the positive definite metric field of gravity has been secured.
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ALI, S. TWAREQUE, and MIROSLAV ENGLIŠ. "QUANTIZATION METHODS: A GUIDE FOR PHYSICISTS AND ANALYSTS." Reviews in Mathematical Physics 17, no. 04 (May 2005): 391–490. http://dx.doi.org/10.1142/s0129055x05002376.

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This survey is an overview of some of the better known quantization techniques (for systems with finite numbers of degrees-of-freedom) including in particular canonical quantization and the related Dirac scheme, introduced in the early days of quantum mechanics, Segal and Borel quantizations, geometric quantization, various ramifications of deformation quantization, Berezin and Berezin–Toeplitz quantizations, prime quantization and coherent state quantization. We have attempted to give an account sufficiently in depth to convey the general picture, as well as to indicate the mutual relationships between various methods, their relative successes and shortcomings, mentioning also open problems in the area. Finally, even for approaches for which lack of space or expertise prevented us from treating them to the extent they would deserve, we have tried to provide ample references to the existing literature on the subject. In all cases, we have made an effort to keep the discussion accessible both to physicists and to mathematicians, including non-specialists in the field.
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Pavšič, Matej. "A novel view on successive quantizations, leading to increasingly more “miraculous” states." Modern Physics Letters A 34, no. 23 (July 30, 2019): 1950186. http://dx.doi.org/10.1142/s0217732319501861.

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A series of successive quantizations is considered, starting with the quantization of a non-relativistic or relativistic point particle: (1) quantization of a particle’s position, (2) quantization of wave function, (3) quantization of wave functional. The latter step implies that the wave packet profiles forming the states of quantum field theory are themselves quantized, which gives new physical states that are configurations of configurations. In the procedure of quantization, instead of the Schrödinger first-order equation in time derivative for complex wave function (or functional), the equivalent second-order equation for its real part was used. In such a way, at each level of quantization, the equation a quantum state satisfies is just like that of a harmonic oscillator, and wave function(al) is composed in terms of the pair of its canonically conjugated variables.
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Trusov, Anton, Elena Limonova, Dmitry Nikolaev, and Vladimir V. Arlazarov. "4.6-Bit Quantization for Fast and Accurate Neural Network Inference on CPUs." Mathematics 12, no. 5 (February 23, 2024): 651. http://dx.doi.org/10.3390/math12050651.

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Quantization is a widespread method for reducing the inference time of neural networks on mobile Central Processing Units (CPUs). Eight-bit quantized networks demonstrate similarly high quality as full precision models and perfectly fit the hardware architecture with one-byte coefficients and thirty-two-bit dot product accumulators. Lower precision quantizations usually suffer from noticeable quality loss and require specific computational algorithms to outperform eight-bit quantization. In this paper, we propose a novel 4.6-bit quantization scheme that allows for more efficient use of CPU resources. This scheme has more quantization bins than four-bit quantization and is more accurate while preserving the computational efficiency of the later (it runs only 4% slower). Our multiplication uses a combination of 16- and 32-bit accumulators and avoids multiplication depth limitation, which the previous 4-bit multiplication algorithm had. The experiments with different convolutional neural networks on CIFAR-10 and ImageNet datasets show that 4.6-bit quantized networks are 1.5–1.6 times faster than eight-bit networks on the ARMv8 CPU. Regarding the quality, the results of the 4.6-bit quantized network are close to the mean of four-bit and eight-bit networks of the same architecture. Therefore, 4.6-bit quantization may serve as an intermediate solution between fast and inaccurate low-bit network quantizations and accurate but relatively slow eight-bit ones.
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Klauder, John R. "A Valid Quantization of a Half-Harmonic Oscillator Field Theory." Axioms 11, no. 8 (July 24, 2022): 360. http://dx.doi.org/10.3390/axioms11080360.

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The usual full- and half-harmonic oscillators are turned into field theories, and that behavior is examined using canonical and affine quantization. The result leads to a valid affine quantization of the half harmonic oscillator field theory, which points toward further valid quantizations of more realistic field theory models.
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BATES, L., R. CUSHMAN, M. HAMILTON, and J. ŚNIATYCKI. "QUANTIZATION OF SINGULAR REDUCTION." Reviews in Mathematical Physics 21, no. 03 (April 2009): 315–71. http://dx.doi.org/10.1142/s0129055x09003633.

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This paper creates a theory of quantization of singularly reduced systems. We compare our results with those obtained by quantizing algebraically reduced systems. In the case of a Kähler polarization, we show that quantization of a singularly reduced system commutes with reduction, thus generalizing results of Sternberg and Guillemin. We illustrate our theory by treating an example of Arms, Gotay and Jennings where algebraic and singular reduction at the zero level of the momentum mapping differ. In spite of this, their quantizations agree.
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AURICH, R., and J. BOLTE. "QUANTIZATION RULES FOR STRONGLY CHAOTIC SYSTEMS." Modern Physics Letters B 06, no. 27 (November 20, 1992): 1691–719. http://dx.doi.org/10.1142/s0217984992001393.

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We discuss the quantization of strongly chaotic systems and apply several quantization rules to a model system given by the unconstrained motion of a particle on a compact surface of constant negative Gaussian curvature. We study the periodic-orbit theory for distinct symmetry classes corresponding to a parity operation which is always present when such a surface has genus two. Recently, several quantization rules based on periodic orbit theory have been introduced. We compare quantizations using the dynamical zeta function Z(s) with the quantization condition [Formula: see text] where a periodic-orbit expression for the spectral staircase N(E) is used. A general discussion of the efficiency of periodic-orbit quantization then allows us to compare the different methods. The system dependence of the efficiency, which is determined by the topological entropy τ and the mean level density [Formula: see text], is emphasized.
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Bergeron, Hervé, and Jean-Pierre Gazeau. "Variations à la Fourier-Weyl-Wigner on Quantizations of the Plane and the Half-Plane." Entropy 20, no. 10 (October 13, 2018): 787. http://dx.doi.org/10.3390/e20100787.

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Any quantization maps linearly function on a phase space to symmetric operators in a Hilbert space. Covariant integral quantization combines operator-valued measure with the symmetry group of the phase space. Covariant means that the quantization map intertwines classical (geometric operation) and quantum (unitary transformations) symmetries. Integral means that we use all resources of integral calculus, in order to implement the method when we apply it to singular functions, or distributions, for which the integral calculus is an essential ingredient. We first review this quantization scheme before revisiting the cases where symmetry covariance is described by the Weyl-Heisenberg group and the affine group respectively, and we emphasize the fundamental role played by Fourier transform in both cases. As an original outcome of our generalisations of the Wigner-Weyl transform, we show that many properties of the Weyl integral quantization, commonly viewed as optimal, are actually shared by a large family of integral quantizations.
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Losev, Ivan. "Isomorphisms of quantizations via quantization of resolutions." Advances in Mathematics 231, no. 3-4 (October 2012): 1216–70. http://dx.doi.org/10.1016/j.aim.2012.06.017.

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Dissertations / Theses on the topic "Quantization"

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Misra, Vinith. "Functional quantization." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/46021.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
Includes bibliographical references (p. 119-121).
Data is rarely obtained for its own sake; oftentimes, it is a function of the data that we care about. Traditional data compression and quantization techniques, designed to recreate or approximate the data itself, gloss over this point. Are performance gains possible if source coding accounts for the user's function? How about when the encoders cannot themselves compute the function? We introduce the notion of functional quantization and use the tools of high-resolution analysis to get to the bottom of this question. Specifically, we consider real-valued raw data Xn/1 and scalar quantization of each component Xi of this data. First, under the constraints of fixed-rate quantization and variable-rate quantization, we obtain asymptotically optimal quantizer point densities and bit allocations. Introducing the notions of functional typicality and functional entropy, we then obtain asymptotically optimal block quantization schemes for each component. Next, we address the issue of non-monotonic functions by developing a model for high-resolution non-regular quantization. When these results are applied to several examples we observe striking improvements in performance.Finally, we answer three questions by means of the functional quantization framework: (1) Is there any benefit to allowing encoders to communicate with one another? (2) If transform coding is to be performed, how does a functional distortion measure influence the optimal transform? (3) What is the rate loss associated with a suboptimal quantizer design? In the process, we demonstrate how functional quantization can be a useful and intuitive alternative to more general information-theoretic techniques.
by Vinith Misra.
M.Eng.
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Gardell, Fredrik. "Geometric Quantization." Thesis, Uppsala universitet, Teoretisk fysik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-296618.

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In this project we introduce the general idea of geometric quantization and demonstratehow to apply the process on a few examples. We discuss how to construct a line bundleover the symplectic manifold with Dirac’s quantization conditions and how to determine if we are able to quantize a system with the help of Weil’s integrability condition. To reducethe prequantum line bundle we employ real polarization such that the system does notbreak Heisenberg’s uncertainty principle anymore. From the prequantum bundle and thepolarization we construct the sought after Hilbert space.
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Hedlund, William. "Geometric Quantization." Thesis, Uppsala universitet, Teoretisk fysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-325649.

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We formulate a process of quantization of classical mechanics, from a symplecticperspective. The Dirac quantization axioms are stated, and a satisfactory prequantizationmap is constructed using a complex line bundle. Using polarization, it isdetermined which prequantum states and observables can be fully quantized. Themathematical concepts of symplectic geometry, fibre bundles, and distributions are exposedto the degree to which they occur in the quantization process. Quantizationsof a cotangent bundle and a sphere are described, using real and K¨ahler polarizations,respectively.
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Tangboondouangjit, Aram. "Sigma-Delta quantization number theoretic aspects of refining quantization error /." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3793.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2006.
Thesis research directed by: Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Li, Minyue. "Distribution Preserving Quantization." Doctoral thesis, KTH, Skolan för elektro- och systemteknik (EES), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-38482.

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In the lossy coding of perceptually relevant signals, such as sound and images, the ultimate goal is to achieve good perceived quality of the reconstructed signal, under a constraint on the bit-rate. Conventional methodologies focus either on a rate-distortion optimization or on the preservation of signal features. Technologies resulting from these two perspectives are efficient only for high-rate or low-rate scenarios. In this dissertation, a new objective is proposed: to seek the optimal rate-distortion trade-off under a constraint that statistical properties of the reconstruction are similar to those of the source. The new objective leads to a new quantization concept: distribution preserving quantization (DPQ). DPQ preserves the probability distribution of the source by stochastically switching among an ensemble of quantizers. At low rates, DPQ exhibits a synthesis nature, resembling existing coding methods that preserve signal features. Compared with rate-distortion optimized quantization, DPQ yields some rate-distortion performance for perceptual benefits. The rate-distortion optimization for DPQ facilitates mathematical analysis. The dissertation defines a distribution preserving rate-distortion function (DP-RDF), which serves as a lower bound on the rate of any DPQ method for a given distortion. For a large range of sources and distortion measures, the DP-RDF approaches the classic rate-distortion function with increasing rate. This suggests that, at high rates, an optimal DPQ can approach conventional quantization in terms of rate-distortion characteristics. After verifying the perceptual advantages of DPQ with a relatively simple realization, this dissertation focuses on a method called transformation-based DPQ, which is based on dithered quantization and a non-linear transformation. Asymptotically, with increasing dimensionality, a transformation-based DPQ achieves the DP-RDF for i.i.d. Gaussian sources and the mean squared error (MSE). This dissertation further proposes a DPQ scheme that asymptotically achieves the DP-RDF for stationary Gaussian processes and the MSE. For practical applications, this scheme can be reduced to dithered quantization with pre- and post-filtering. The simplified scheme preserves the power spectral density (PSD) of the source. The use of dithered quantization and non-linear transformations to construct DPQ is extended to multiple description coding, which leads to a multiple description DPQ (MD-DPQ) scheme. MD-DPQ preserves the source probability distribution for any packet loss scenario. The proposed schemes generally require efficient entropy coding. The dissertation also includes an entropy coding algorithm for lossy coding systems, which is referred to as sequential entropy coding of quantization indices with update recursion on probability (SECURE). The proposed lossy coding methods were subjected to evaluations in the context of audio coding. The experimental results confirm the benefits of the methods and, therewith, the effectiveness of the proposed new lossy coding objective.
QC 20110829
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Brown, Jonathan D. "N-Symplectic Quantization." NCSU, 2008. http://www.lib.ncsu.edu/theses/available/etd-02282008-135847/.

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A quantization scheme based on n-symplectic geometry is defined. Using this new definition a generalized Van Hove prequantization is given for the frame bundle of R^n, LR^n. The full set of operators of the generalized Van Hove prequantization is irreducible and essentially self adjoint. However, this prequantization is reducible when it is restricted to the Heisenberg algebra. Several full quantizations are also given for LR^n proving there is no Groenwold Van Hove type obstruction for quantizing LR^n. Using the covering theory of n-symplectic geometry we analyse why this quantization fails under symplectic quantization. Throughout the paper, emphasis is placed on comparison to the symplectic theory.
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Matschkal, Bernd. "Spherical logarithmic quantization." Aachen Shaker, 2007. http://d-nb.info/988124009/04.

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Dunne, G. V. "Methods of quantization." Thesis, Imperial College London, 1988. http://hdl.handle.net/10044/1/47039.

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Nazaikinskii, Vladimir E., Bert-Wolfgang Schulze, and Boris Sternin. "Quantization methods in differential equations : Chapter 2: Quantization of Lagrangian modules." Universität Potsdam, 1999. http://opus.kobv.de/ubp/volltexte/2008/2558/.

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In this chapter we use the wave packet transform described in Chapter 1 to quantize extended classical states represented by so-called Lagrangian sumbanifolds of the phase space. Functions on a Lagrangian manifold form a module over the ring of classical Hamiltonian functions on the phase space (with respect to pointwise multiplication). The quantization procedure intertwines this multiplication with the action of the corresponding quantum Hamiltonians; hence we speak of quantization of Lagrangian modules. The semiclassical states obtained by this quantization procedure provide asymptotic solutions to differential equations with a small parameter. Locally, such solutions can be represented by WKB elements. Global solutions are given by Maslov's canonical operator [2]; also see, e.g., [3] and the references therein. Here the canonical operator is obtained in the framework of the universal quantization procedure provided by the wave packet transform. This procedure was suggested in [4] (see also the references there) and further developed in [5]; our exposition is in the spirit of these papers. Some further bibliographical remarks can be found in the beginning of Chapter 1.
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Polastri, Costanza. "Quantization of angular momentum." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14610/.

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La tesi tratta una breve esposizione della teoria del momento angolare, con particolare enfasi sui costrutti matematici che sono necessari a comprenderla. Dapprima, nel Capitolo 1 si vede una panoramica dei concetti principali di geometria differenziale. Nel Capitolo 2 si approfondisce la teoria dei gruppi di Lie, con particolare attenzione alla mappa esponenziale e alle rappresentazioni matriciali dei gruppi SU(2) e SO(3). Il Capitolo 3 è dedicato alla parte di fisica: si espongono i concetti di operatore e osservabile, dopodiche' si analizza il gruppo di Galileo di trasformazioni spazio-temporali e si usano le simmetrie dello spazio-tempo per ricavare l'operatore momento angolare.
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Books on the topic "Quantization"

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F, Swaszek Peter, ed. Quantization. New York: Van Nostrand Reinhold, 1985.

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Masujima, Michio. Path integral quantization and stochastic quantization. 2nd ed. Berlin: Springer, 2008.

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Masujima, Michio, ed. Path Integral Quantization and Stochastic Quantization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-48162-1.

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Path integral quantization and stochastic quantization. 2nd ed. Berlin: Springer, 2008.

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Greiner, Walter, and Joachim Reinhardt. Field Quantization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61485-9.

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Namiki, Mikio. Stochastic Quantization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-540-47217-9.

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Namiki, Mikio. Stochastic quantization. Berlin: Springer-Verlag, 1992.

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Namiki, Mikio. Stochastic quantization. Berlin: Springer-Verlag, 1992.

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Woodhouse, Nicholas. Geometric quantization. 2nd ed. Oxford: Clarendon Press, 1991.

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Geometric quantization. 2nd ed. Oxford: Clarendon Press, 1992.

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Book chapters on the topic "Quantization"

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Taubman, David S., and Michael W. Marcellin. "Quantization." In JPEG2000 Image Compression Fundamentals, Standards and Practice, 87–142. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0799-4_3.

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Pelgrom, Marcel. "Quantization." In Analog-to-Digital Conversion, 91–124. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44971-5_4.

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Bongaarts, Peter. "Quantization." In Quantum Theory, 187–216. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09561-5_13.

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Zhang, Jian-zu, Roberto Floreanini, Steven Duplij, Steven Duplij, and Dmitri Gitman. "Quantization." In Concise Encyclopedia of Supersymmetry, 312. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/1-4020-4522-0_417.

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Bosi, Marina, and Richard E. Goldberg. "Quantization." In Introduction to Digital Audio Coding and Standards, 13–46. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0327-9_2.

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Gustafson, Stephen J., and Israel Michael Sigal. "Quantization." In Mathematical Concepts of Quantum Mechanics, 27–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21866-8_4.

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Schuller, Gerald. "Quantization." In Filter Banks and Audio Coding, 105–7. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51249-1_2.

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Woit, Peter. "Quantization." In Quantum Theory, Groups and Representations, 229–36. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64612-1_17.

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Shekhar, Shashi, and Hui Xiong. "Quantization." In Encyclopedia of GIS, 935. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1063.

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Gustafson, Stephen J., and Israel Michael Sigal. "Quantization." In Mathematical Concepts of Quantum Mechanics, 33–51. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59562-3_4.

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Conference papers on the topic "Quantization"

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Rios, P. de M., G. M. Tuynman, Piotr Kielanowski, Anatol Odzijewicz, Martin Schlichenmaier, and Theodore Voronov. "Weyl Quantization from geometric quantization." In GEOMETRIC METHODS IN PHYSICS. AIP, 2008. http://dx.doi.org/10.1063/1.3043868.

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Gao, Lianli, Xiaosu Zhu, Jingkuan Song, Zhou Zhao, and Heng Tao Shen. "Beyond Product Quantization: Deep Progressive Quantization for Image Retrieval." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/102.

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Product Quantization (PQ) has long been a mainstream for generating an exponentially large codebook at very low memory/time cost. Despite its success, PQ is still tricky for the decomposition of high-dimensional vector space, and the retraining of model is usually unavoidable when the code length changes. In this work, we propose a deep progressive quantization (DPQ) model, as an alternative to PQ, for large scale image retrieval. DPQ learns the quantization codes sequentially and approximates the original feature space progressively. Therefore, we can train the quantization codes with different code lengths simultaneously. Specifically, we first utilize the label information for guiding the learning of visual features, and then apply several quantization blocks to progressively approach the visual features. Each quantization block is designed to be a layer of a convolutional neural network, and the whole framework can be trained in an end-to-end manner. Experimental results on the benchmark datasets show that our model significantly outperforms the state-of-the-art for image retrieval. Our model is trained once for different code lengths and therefore requires less computation time. Additional ablation study demonstrates the effect of each component of our proposed model. Our code is released at https://github.com/cfm-uestc/DPQ.
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Kung, H. T., Bradley McDanel, and Sai Qian Zhang. "Term Quantization: Furthering Quantization at Run Time." In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2020. http://dx.doi.org/10.1109/sc41405.2020.00100.

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Qin, Haotong. "Hardware-friendly Deep Learning by Network Quantization and Binarization." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/687.

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Quantization is emerging as an efficient approach to promote hardware-friendly deep learning and run deep neural networks on resource-limited hardware. However, it still causes a significant decrease to the network in accuracy. We summarize challenges of quantization into two categories: Quantization for Diverse Architectures and Quantization on Complex Scenes. Our studies focus mainly on applying quantization on various architectures and scenes and pushing the limit of quantization to extremely compress and accelerate networks. The comprehensive research on quantization will achieve more powerful, more efficient, and more flexible hardware-friendly deep learning, and make it better suited to more real-world applications.
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Spencer, Mark F., and Douglas E. Thornton. "Quantization Error in Digital Holography." In Applications of Lasers for Sensing and Free Space Communications. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/lsc.2022.lstu3c.3.

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This paper uses the signal-to-noise ratio to analyze the effects of quantization error (aka digitization noise) in digital holography. Assuming a strong reference, the results show that quantization error depends on the pixel-well depth and the number of bits used for digitization.
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Yang, Jiwei, Xu Shen, Jun Xing, Xinmei Tian, Houqiang Li, Bing Deng, Jianqiang Huang, and Xian-sheng Hua. "Quantization Networks." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00748.

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Kumar, Ravi, Ronny Lempel, Roy Schwartz, and Sergei Vassilvitskii. "Rank quantization." In the sixth ACM international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2433396.2433416.

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Metz, Clément, Thibault Allenet, Johannes Thiele, Antoine Dupret, and Olivier Bichler. "Lattice Quantization." In 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2023. http://dx.doi.org/10.23919/date56975.2023.10137188.

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Eggers, Joachim J., and Bernd Girod. "Quantization watermarking." In Electronic Imaging, edited by Ping W. Wong and Edward J. Delp III. SPIE, 2000. http://dx.doi.org/10.1117/12.385006.

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KHRENNIKOV, ANDREI, and GAVRIEL SEGRE. "HYPERBOLIC QUANTIZATION." In Proceedings of the 26th Conference. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812770271_0028.

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Reports on the topic "Quantization"

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Mladenov, Ivaïlo M. Quantization on Curved Manifolds. GIQ, 2012. http://dx.doi.org/10.7546/giq-3-2002-64-104.

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Games, Richard A., Sean D. O'Neil, and Joseph J. Rushanan. Algebraic Integer Quantization and Conversion. Fort Belvoir, VA: Defense Technical Information Center, July 1988. http://dx.doi.org/10.21236/ada206664.

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Dehn, James T. Quantization by Cosmic Background Radiation. Fort Belvoir, VA: Defense Technical Information Center, May 1989. http://dx.doi.org/10.21236/ada208103.

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Hirshfeld, Allen. Current Aspects of Deformation Quantization. GIQ, 2012. http://dx.doi.org/10.7546/giq-3-2002-290-303.

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Grigorescu, Marius. Geometrical Framework of Quantization Problem. GIQ, 2012. http://dx.doi.org/10.7546/jgsp-23-2011-1-27.

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Cushman, Richard, and Jędrzej Śniatycki. On Bohr-Sommerfeld-Heisenberg Quantization. Jgsp, 2014. http://dx.doi.org/10.7546/jgsp-35-2014-11-19.

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Kanatchikov, Igor V. Ehrenfest Theorem in Precanonical Quantization. Jgsp, 2015. http://dx.doi.org/10.7546/jgsp-37-2015-43-66.

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Venkatraman, Mahesh, Heesung Kwon, and Nasser M. Nasrabadi. Video Compression using Vector Quantization. Fort Belvoir, VA: Defense Technical Information Center, May 1998. http://dx.doi.org/10.21236/ada344253.

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Langnau, Alex. Perturbation theory in light-cone quantization. Office of Scientific and Technical Information (OSTI), January 1992. http://dx.doi.org/10.2172/10134550.

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Hyer, Thomas. Hadronic wavefunctions in light-cone quantization. Office of Scientific and Technical Information (OSTI), May 1994. http://dx.doi.org/10.2172/10162396.

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