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Found 10 papers, 2 papers with code
Perceptual Structure in the Absence of Grounding for LLMs: The Impact of Abstractedness and Subjectivity in Color Language
The need for grounding in language understanding is an active research topic.
Exploring 360-Degree View of Customers for Lookalike Modeling
Lookalike models are based on the assumption that user similarity plays an important role towards product selling and enhancing the existing advertising campaigns from a very large user base.
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation
The smoothing is specially desired in the presence of homophilic graphs, such as the ones we find on recommender systems.
An Edit-centric Approach for Wikipedia Article Quality Assessment
We propose an edit-centric approach to assess Wikipedia article quality as a complementary alternative to current full document-based techniques.
Towards Federated Graph Learning for Collaborative Financial Crimes Detection
Advances in technology used in this domain, including machine learning based approaches, can improve upon the effectiveness of financial institutions' existing processes, however, a key challenge that most financial institutions continue to face is that they address financial crimes in isolation without any insight from other firms.
Content Aware Source Code Change Description Generation
We propose to study the generation of descriptions from source code changes by integrating the messages included on code commits and the intra-code documentation inside the source in the form of docstrings.
Villani at SemEval-2018 Task 8: Semantic Extraction from Cybersecurity Reports using Representation Learning
In this paper, we describe our proposal for the task of Semantic Extraction from Cybersecurity Reports.
Refining Raw Sentence Representations for Textual Entailment Recognition via Attention
Finally it combines the refined representations of both sentences into a single vector to be used for classification.
A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes
We propose a model to automatically describe changes introduced in the source code of a program using natural language.
