Results 21 to 30 of about 379,828 (307)
Asymmetric Deep Semantic Quantization for Image Retrieval
IEEE Access, 2019 Due to its fast retrieval and storage efficiency capabilities, hashing has been widely used in nearest neighbor retrieval tasks. By using deep learning-based techniques, hashing can outperform non-learning-based hashing technique in many applications ...
Zhan Yang +3 more
doaj +1 more source
In-Hindsight Quantization Range Estimation for Quantized Training [PDF]
arXiv, 2021 Quantization techniques applied to the inference of deep neural networks have enabled fast and efficient execution on resource-constraint devices. The success of quantization during inference has motivated the academic community to explore fully quantized training, i.e. quantizing back-propagation as well.
arxiv
Ternary Quantization: A Survey [PDF]
arXiv, 2023 Inference time, model size, and accuracy are critical for deploying deep neural network models. Numerous research efforts have been made to compress neural network models with faster inference and higher accuracy. Pruning and quantization are mainstream methods to this end.
arxiv
Succesful renormalization of a QCD-inspired Hamiltonian
, 2003 The long standing problem of non perturbative renormalization of a gauge field theoretical Hamiltonian is addressed and explicitly carried out within an (effective) light-cone Hamiltonian approach to QCD.
Anisovich +8 more
core +1 more source
Dirac versus reduced quantization and operator ordering [PDF]
, 1996 We show an equivalence between Dirac quantization and the reduced phase space quantization. The equivalence of the both quantization methods determines the operator ordering of the Hamiltonian.
Shimizu, Katsutaro
core +3 more sources
Analysis of Application of Cluster Descriptions in Space of Characteristic Image Features
Data, 2018 In this paper, we propose an investigation of the properties of structural image recognition methods in the cluster space of characteristic features. Recognition, which is based on key point descriptors like SIFT (Scale-invariant Feature Transform), SURF
Oleksii Gorokhovatskyi +2 more
doaj +1 more source
Spatial Shift Point-Wise Quantization
IEEE Access, 2020 Deep neural networks (DNN) have been applied to numerous artificial-intelligence applications because of their remarkable accuracy. However, computational requirements for deep neural networks are recently skyrocketing far beyond the Moore's Law.
Eunhui Kim, Kyong-Ha Lee
doaj +1 more source
IEEE Access, 2021 We consider a power constrained downlink communication scenario where energy efficiency, reliability, and latency take precedence over rate, as in some Internet of Things (IoT) applications.
Sherief Helwa, Naofal Al-Dhahir
doaj +1 more source
Constrained quantization for probability distributions [PDF]
arXiv, 2023 In this paper, for a Borel probability measure $P$ on a normed space $\mathbb R^k$, we extend the definitions of $n$th unconstrained quantization error, unconstrained quantization dimension, and unconstrained quantization coefficient, which traditionally in the literature are known as $n$th quantization error, quantization dimension, and quantization ...
arxiv
, 2002 We raise the problem of constructing quantum observables that have classical counterparts without quantization. Specifically we seek to define and motivate a solution to the quantum-classical correspondence problem independent from quantization and ...
AA Kirilov +15 more
core +1 more source