Topic
Class (philosophy)
About: Class (philosophy) is a research topic. Over the lifetime, 821 publications have been published within this topic receiving 28000 citations.
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TL;DR: In this paper , the authors derived necessary results for the existence, uniqueness and various form of Hyers-Ulam (H-U) type stability for the considered problem, using classical fixed point theorems due to Banach and Krasnoselskii's.
Abstract: In this research work, we establish some new results about piecewise equation involving Caputo Fabrizio derivative (CFD). The concerned class has been recently introduced and these results are fundamental for investigation of qualitative theory and numerical interpretation. We derive some necessary results for the existence, uniqueness and various form of Hyers-Ulam (H-U) type stability for the considered problem. For the required results, we need to utilize usual classical fixed point theorems due to Banach and Krasnoselskii's. Moreover, results devoted to H-U stability are derived by using classical tools of nonlinear functional analysis. Some pertinent test problems are given to demonstrate our results.
12 citations
TL;DR: In this article, a multi-triage model is proposed that resolves both tasks simultaneously via multi-task learning (MTL) via assigning developers and allocating issue types in the bug triage process.
12 citations
TL;DR: Zhang et al. as mentioned in this paper proposed an attention-based imbalanced image classification (DAIIC) approach to automatically pay more attention to the minority classes in a data-driven manner, where an attention network and a novel attention augmented logistic regression function are employed to encapsulate as many features, which belongs to minority classes, as possible into the discriminative feature learning process.
Abstract: Class imbalance is a common problem in real-world image classification problems, some classes are with abundant data, and the other classes are not. In this case, the representations of classifiers are likely to be biased toward the majority classes and it is challenging to learn proper features, leading to unpromising performance. To eliminate this biased feature representation, many algorithm-level methods learn to pay more attention to the minority classes explicitly according to the prior knowledge of the data distribution. In this article, an attention-based approach called deep attention-based imbalanced image classification (DAIIC) is proposed to automatically pay more attention to the minority classes in a data-driven manner. In the proposed method, an attention network and a novel attention augmented logistic regression function are employed to encapsulate as many features, which belongs to the minority classes, as possible into the discriminative feature learning process by assigning the attention for different classes jointly in both the prediction and feature spaces. With the proposed object function, DAIIC can automatically learn the misclassification costs for different classes. Then, the learned misclassification costs can be used to guide the training process to learn more discriminative features using the designed attention networks. Furthermore, the proposed method is applicable to various types of networks and data sets. Experimental results on both single-label and multilabel imbalanced image classification data sets show that the proposed method has good generalizability and outperforms several state-of-the-art methods for imbalanced image classification.
12 citations
TL;DR: A general definition of abstract strategies is presented which is extensional in the sense that a strategy is defined explicitly as a set of derivations of an abstract reduction system and a more intensional definition supporting the abstract view is presented.
Abstract: This paper is a contribution to the theoretical foundations of strategies. We first present a general definition of abstract strategies which is extensional in the sense that a strategy is defined explicitly as a set of derivations of an abstract reduction system. We then move to a more intensional definition supporting the abstract view but more operational in the sense that it describes a means for determining such a set. We characterize the class of extensional strategies that can be defined intensionally. We also give some hints towards a logical characterization of intensional strategies and propose a few challenging perspectives.
12 citations
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TL;DR: In this paper, the authors established the stability of solutions to the entropically regularized optimal transport problem with respect to the marginals and the cost function, and obtained the wellposedness of the solution in this geometric sense, even when all transports have infinite cost.
Abstract: We establish the stability of solutions to the entropically regularized optimal transport problem with respect to the marginals and the cost function. The result is based on the geometric notion of cyclical invariance and inspired by the use of $c$-cyclical monotonicity in classical optimal transport. As a consequence of stability, we obtain the wellposedness of the solution in this geometric sense, even when all transports have infinite cost. More generally, our results apply to a class of static Schrodinger bridge problems including entropic optimal transport.
12 citations