Search Results for author:
Found 9 papers, 4 papers with code
TWT: Table with Written Text for Controlled Data-to-Text Generation
In this paper, we propose to generate text conditioned on the structured data (table) and a prefix (the written text) by leveraging the pre-trained models.
Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack
Neural style transfer (NST) generates new images by combining the style of one image with the content of another.
Retro-BLEU: Quantifying Chemical Plausibility of Retrosynthesis Routes through Reaction Template Sequence Analysis
Computer-assisted methods have emerged as valuable tools for retrosynthesis analysis.
A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models
The key contributions of this paper include a novel false positive attack method, two new loss functions, effective style transfer in handwriting styles, and superior performance in white-box false positive attacks compared to other white-box attack methods.
Leveraging Reaction-aware Substructures for Retrosynthesis Analysis
In this paper, we propose a substructure-level decoding model, where the substructures are reaction-aware and can be automatically extracted with a fully data-driven approach.
Part & Whole Extraction: Towards A Deep Understanding of Quantitative Facts for Percentages in Text
", our goal is to obtain a deep understanding of the percentage numbers ("30 percent" and "20%") by extracting their quantitative facts: part ("like watching football" and "prefer to watch NBA") and whole ("Americans).
Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL
One key component in text-to-SQL is to predict the comparison relations between columns and their values.
A Split-and-Recombine Approach for Follow-up Query Analysis
To leverage the advances in context-independent semantic parsing, we propose to perform follow-up query analysis, aiming to restate context-dependent natural language queries with contextual information.
