Violations of local duality in the heavy quark sector
11
Citation
19
Reference
10
Related Paper
Citation Trend
Keywords:
Duality (order theory)
Operator product expansion
Operator (biology)
Robustness
Cite
Citations (17)
Dominance (genetics)
Cite
Citations (27)
In this article, we take one step toward understanding the learning behavior of deep residual networks, and supporting the observation that deep residual networks behave like ensembles. We propose a new convolutional neural network architecture which builds upon the success of residual networks by explicitly exploiting the interpretation of very deep networks as an ensemble. The proposed multi-residual network increases the number of residual functions in the residual blocks. Our architecture generates models that are wider, rather than deeper, which significantly improves accuracy. We show that our model achieves an error rate of 3.73% and 19.45% on CIFAR-10 and CIFAR-100 respectively, that outperforms almost all of the existing models. We also demonstrate that our model outperforms very deep residual networks by 0.22% (top-1 error) on the full ImageNet 2012 classification dataset. Additionally, inspired by the parallel structure of multi-residual networks, a model parallelism technique has been investigated. The model parallelism method distributes the computation of residual blocks among the processors, yielding up to 15% computational complexity improvement.
Residual neural network
Cite
Citations (36)
In this article, we take one step toward understanding the learning behavior of deep residual networks, and supporting the hypothesis that deep residual networks are exponential ensembles by construction. We examine the effective range of ensembles by introducing multi-residual networks that significantly improve classification accuracy of residual networks. The multi-residual networks increase the number of residual functions in the residual blocks. This is shown to improve the accuracy of the residual network when the network is deeper than a threshold. Based on a series of empirical studies on CIFAR-10 and CIFAR-100 datasets, the proposed multi-residual network yield $6\%$ and $10\%$ improvement with respect to the residual networks with identity mappings. Comparing with other state-of-the-art models, the proposed multi-residual network obtains a test error rate of $3.92\%$ on CIFAR-10 that outperforms all existing models.
Residual neural network
Cite
Citations (17)
A residual-networks family with hundreds or even thousands of layers dominates major image recognition tasks, but building a network by simply stacking residual blocks inevitably limits its optimization ability. This paper proposes a novel residual-network architecture, Residual networks of Residual networks (RoR), to dig the optimization ability of residual networks. RoR substitutes optimizing residual mapping of residual mapping for optimizing original residual mapping. In particular, RoR adds level-wise shortcut connections upon original residual networks to promote the learning capability of residual networks. More importantly, RoR can be applied to various kinds of residual networks (ResNets, Pre-ResNets and WRN) and significantly boost their performance. Our experiments demonstrate the effectiveness and versatility of RoR, where it achieves the best performance in all residual-network-like structures. Our RoR-3-WRN58-4+SD models achieve new state-of-the-art results on CIFAR-10, CIFAR-100 and SVHN, with test errors 3.77%, 19.73% and 1.59%, respectively. RoR-3 models also achieve state-of-the-art results compared to ResNets on ImageNet data set.
Residual neural network
Cite
Citations (316)
Residual Neural Networks [1] won first place in all five main tracks of the ImageNet and COCO 2015 competitions. This kind of network involves the creation of pluggable modules such that the output contains a residual from the input. The residual in that paper is the identity function. We propose to include residuals from all lower layers, suitably normalized, to create the residual. This way, all previous layers contribute equally to the output of a layer. We show that our approach is an improvement on [1] for the CIFAR-10 dataset.
Deep Neural Networks
Cite
Citations (0)
Coupling constant
Cite
Citations (1)
Masses of the lowest lying hadrons and the behavior of constituent quarks are discussed on the analysis of their electromagnetic properties, based on QCD. We find that light quarks (u and d) in the hadrons move with the velocity ≃0.8c; s quark, with ≲0.7c; c quark, with nearly 0.3c. Then we know that the confinement force, no matter what kind of confinement one prefers, does not mainly contribute to the hadron masses. It is also conjectured that |νe-e|/|u-d|≃|νµ-µ|/|c-s|≃|ντ-τ|/|t-b| and mt≃23GeV on the assumption of the existence of the next layer.
Exotic hadron
Color confinement
Strong interaction
Cite
Citations (2)
SIGNAL (programming language)
Feature (linguistics)
Cite
Citations (0)