Search Results for author: Daniel Zeng

Found 8 papers, 1 papers with code

ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy

no code implementations4 Jul 2022 Daniel Zeng, Tailin Wu, Jure Leskovec

Here, we introduce ViRel, a method for unsupervised discovery and learning of Visual Relations with graph-level analogy.

Relation Classification

Boosting Multi-Label Image Classification with Complementary Parallel Self-Distillation

1 code implementation23 May 2022 Jiazhi Xu, Sheng Huang, Fengtao Zhou, Luwen Huangfu, Daniel Zeng, Bo Liu

Then, the MLIC models of fewer categories are trained with these sub-tasks in parallel for respectively learning the joint patterns and the category-specific patterns of labels.

Knowledge Distillation Multi-Label Image Classification

Aggregate effects of advertising decisions: a complex systems look at search engine advertising via an experimental study

no code implementations4 Mar 2022 Yanwu Yang, Xin Li, Bernard J. Jansen, Daniel Zeng

Originality: This is one of the first research works to explore collective group decisions and resulting phenomena in the complex context of search engine advertising via developing and validating a simulation framework that supports assessments of various advertising strategies and estimations of the impact of mechanisms on the search market.

Keyword Optimization in Sponsored Search Advertising: A Multi-Level Computational Framework

no code implementations28 Feb 2022 Yanwu Yang, Bernard J. Jansen, Yinghui Yang, Xunhua Guo, Daniel Zeng

This paper proposes a multi-level and closed-form computational framework for keyword optimization (MKOF) to support various keyword decisions.

Learning Parameters for a Generalized Vidale-Wolfe Response Model with Flexible Ad Elasticity and Word-of-Mouth

no code implementations28 Feb 2022 Yanwu Yang, Baozhu Feng, Daniel Zeng

The GVW model and its deep learning-based estimation method provide a basis to support big data-driven advertising analytics and decision makings; in the meanwhile, identified properties and experimental findings of this research illuminate critical managerial insights for advertisers in various advertising forms.

Knowledge-Enhanced Natural Language Inference Based on Knowledge Graphs

no code implementations COLING 2020 Zikang Wang, Linjing Li, Daniel Zeng

In this paper, we propose a novel Knowledge Graph-enhanced NLI (KGNLI) model to leverage the usage of background knowledge stored in knowledge graphs in the field of NLI.

Knowledge Graphs Natural Language Inference

Spread-gram: A spreading-activation schema of network structural learning

no code implementations30 Sep 2019 Jie Bai, Linjing Li, Daniel Zeng

Inspired by a cognitive model of human memory, we propose a network representation learning scheme.

Representation Learning

Evaluating the Usefulness of Sentiment Information for Focused Crawlers

no code implementations27 Sep 2013 Tianjun Fu, Ahmed Abbasi, Daniel Zeng, Hsinchun Chen

Despite the prevalence of sentiment-related content on the Web, there has been limited work on focused crawlers capable of effectively collecting such content.

Marketing

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