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Found 13 papers, 2 papers with code
Evaluating the Usefulness of Sentiment Information for Focused Crawlers
Despite the prevalence of sentiment-related content on the Web, there has been limited work on focused crawlers capable of effectively collecting such content.
Spread-gram: A spreading-activation schema of network structural learning
Inspired by a cognitive model of human memory, we propose a network representation learning scheme.
Knowledge-Enhanced Natural Language Inference Based on Knowledge Graphs
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.
Learning Parameters for a Generalized Vidale-Wolfe Response Model with Flexible Ad Elasticity and Word-of-Mouth
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.
Keyword Optimization in Sponsored Search Advertising: A Multi-Level Computational Framework
This paper proposes a multi-level and closed-form computational framework for keyword optimization (MKOF) to support various keyword decisions.
Aggregate effects of advertising decisions: a complex systems look at search engine advertising via an experimental study
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.
Boosting Multi-Label Image Classification with Complementary Parallel Self-Distillation
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.
ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy
Here, we introduce ViRel, a method for unsupervised discovery and learning of Visual Relations with graph-level analogy.
PRODIGY: Enabling In-context Learning Over Graphs
In-context learning is the ability of a pretrained model to adapt to novel and diverse downstream tasks by conditioning on prompt examples, without optimizing any parameters.
Wasserstein Diversity-Enriched Regularizer for Hierarchical Reinforcement Learning
Hierarchical reinforcement learning composites subpolicies in different hierarchies to accomplish complex tasks. Automated subpolicies discovery, which does not depend on domain knowledge, is a promising approach to generating subpolicies. However, the degradation problem is a challenge that existing methods can hardly deal with due to the lack of consideration of diversity or the employment of weak regularizers.
LDM$^2$: A Large Decision Model Imitating Human Cognition with Dynamic Memory Enhancement
With the rapid development of large language models (LLMs), it is highly demanded that LLMs can be adopted to make decisions to enable the artificial general intelligence.
YAYI-UIE: A Chat-Enhanced Instruction Tuning Framework for Universal Information Extraction
The difficulty of the information extraction task lies in dealing with the task-specific label schemas and heterogeneous data structures.
Short-Form Videos and Mental Health: A Knowledge-Guided Neural Topic Model
To prevent widespread consequences, platforms are eager to predict these videos' impact on viewers' mental health.
