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Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation
TLDR
We model the problem using an optimization framework where truths and source reliability are defined as two sets of unknown variables. Expand
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A Confidence-Aware Approach for Truth Discovery on Long-Tail Data
TLDR
We propose a confidence-aware truth discovery (CATD) method to automatically detect truths from conflicting data with long-tail phenomenon. Expand
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A Survey on Truth Discovery
TLDR
We focus on providing a comprehensive overview of truth discovery methods, and summarizing them from different aspects, and discuss the key challenges in truth discovery. Expand
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Joint Slot Filling and Intent Detection via Capsule Neural Networks
TLDR
We propose a capsule-based neural network model which accomplishes slot filling and intent detection via a dynamic routing-by-agreement schema. Expand
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FaitCrowd: Fine Grained Truth Discovery for Crowdsourced Data Aggregation
TLDR
We propose FaitCrowd, a fine grained truth discovery model for the task of aggregating conflicting data collected from multiple users/sources. Expand
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LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
TLDR
We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering. Expand
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Simple and Deep Graph Convolutional Networks
TLDR
We propose the deep GCNII, an extension of the vanilla GCN model with two simple yet effective techniques: {\em Initial residual} and {\em Identity mapping}. Expand
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Cloud-Enabled Privacy-Preserving Truth Discovery in Crowd Sensing Systems
TLDR
In this paper, we propose a novel cloud-enabled privacy-preserving truth discovery (PPTD) framework for crowd sensing systems, which can achieve the protection of not only users' sensory data but also their reliability scores derived by the truth discovery approaches. Expand
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Truth Discovery on Crowd Sensing of Correlated Entities
TLDR
We formulate the task of truth discovery on correlated entities as an optimization problem, in which both truths and user reliability are modeled as variables. Expand
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Representation Learning for Treatment Effect Estimation from Observational Data
TLDR
We propose a local similarity preserved individual treatment effect (SITE) estimation method based on deep representation learning. Expand
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