Sequential Multi-Class Labeling in Crowdsourcing

Kang, Qiyu; Tay, Wee Peng

doi:10.1109/tkde.2018.2874003

Cited by 10 publications

(3 citation statements)

References 24 publications

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“…Errors may occur without leaving any trace in system logs [45]. The node labeling process itself could also be noisy, e.g., if the dataset is labeled using crowdsourcing [46], [47]. In our setup, it can be useful to perform correction without knowing the exact node identities where corruption occurs.…”

Section: A Imdb Dataset: Heterogeneous Graphsmentioning

confidence: 99%

Graph Signal Processing Over a Probability Space of Shift Operators

Tay

Ortega

2023

IEEE Trans. Signal Process.

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Graph signal processing (GSP) uses a shift operator to define a Fourier basis for the set of graph signals. The shift operator is often chosen to capture the graph topology. However, in many applications, the graph topology may be unknown a priori, its structure uncertain, or generated randomly from a predefined set for each observation. Each graph topology gives rise to a different shift operator. In this paper, we develop a GSP framework over a probability space of shift operators. We develop the corresponding notions of Fourier transform, MFC filters, and band-pass filters, which subsumes classical GSP theory as the special case where the probability space consists of a single shift operator. We show that an MFC filter under this framework is the expectation of random convolution filters in classical GSP, while the notion of bandlimitedness requires additional wiggle room from being simply a fixed point of a band-pass filter. We develop a mechanism that facilitates mapping from one space of shift operators to another, which allows our framework to be applied to a rich set of scenarios. We demonstrate how the theory can be applied by using both synthetic and real datasets.

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Section: A Imdb Dataset: Heterogeneous Graphsmentioning

confidence: 99%

Graph Signal Processing Over a Probability Space of Shift Operators

Tay

Ortega

2023

IEEE Trans. Signal Process.

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“…Thus, the WIMM model obtains b and α by solving the system of linear equations (20), and then ξ by the optimality condition (19), the final decision is…”

Section: B Maximum Impact Memory Model (Mimm)mentioning

confidence: 99%

“…Indeed, memory systems have been widely explored by researchers to enhance memorization capabilities in various domains. For instance, in the field of machine learning and artificial intelligence, memory mechanisms have been proposed to assist learners in remembering and revising learning tasks [15]- [17].Rafferty et al [18] presented an observable Partially Observable Markov Decision Process (POMDP) planning problem to address memory tasks [19], [20], while Settle and Meeder [21] developed a trainable memory retention model that optimizes revision schedules for effective memorization. In the realm of deep reinforcement learning, researchers have explored novel methods and optimal policies, elevating the efficiency and engagement of learners [22]- [24] .…”

Section: Introductionmentioning

confidence: 99%

School Mathematics Textbooks in China

Wang¹

2015

East China Normal University Scientific Reports

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Let µ and ν be two probability measures on R d , where µ(dx) = e −V (x) dx R d e −V (x) dx for some V ∈ C 1 (R d ). Explicit sufficient conditions on V and ν are presented such that µ * ν satisfies the log-Sobolev, Poincaré and super Poincaré inequalities. In particular, if V (x) = λ|x| 2 for some λ > 0 and ν(e λθ|•| 2 ) < ∞ for some θ > 1, then µ * ν satisfies the log-Sobolev inequality. This improves and extends the recent results on the log-Sobolev inequality derived in [20] for convolutions of the Gaussian measure and compactly supported probability measures. On the other hand, it is well known that the log-Sobolev inequality for µ * ν implies ν(e ε|•| 2 ) < ∞ for some ε > 0. RésuméDes conditions explicites suffisantes sur V et ν sont présentées telles que µ * ν satisfait des inégalités de Sobolev logarithmique, de Poincaré et de super-Poincaré. En particulier, si V (x) = λ|x| 2 pour quelque λ > 0 et ν(e λθ|•| 2 ) < ∞ avec θ > 1, alors µ * ν satisfait l'inégalité de Sobolev logarithmique. Cela améliore et étend des résultats récents sur l'inégalité de Sobolev logarithmique obtenus dans [20] pour des convolutions de la mesure de Gauss et des mesures de probabilité à support compact. D'autre part, il est bien connu que l'inégalité de Sobolev logarithmique pour µ * ν implique ν(e ε|•| 2 ) < ∞ pour quelque ε > 0.

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