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Found 6 papers, 1 papers with code
Improving Character-Aware Neural Language Model by Warming up Character Encoder under Skip-gram Architecture
Character-aware neural language models can capture the relationship between words by exploiting character-level information and are particularly effective for languages with rich morphology.
Abstractive Document Summarization with Word Embedding Reconstruction
Neural sequence-to-sequence (Seq2Seq) models and BERT have achieved substantial improvements in abstractive document summarization (ADS) without and with pre-training, respectively.
Integration of Large Language Models in Control of EHD Pumps for Precise Color Synthesis
This paper presents an innovative approach to integrating Large Language Models (LLMs) with Arduino-controlled Electrohydrodynamic (EHD) pumps for precise color synthesis in automation systems.
Robust Ellipse Fitting Based on Maximum Correntropy Criterion With Variable Center
For single ellipse fitting, we formulate a non-convex optimization problem to estimate the kernel bandwidth and center and divide it into two subproblems, each estimating one parameter.
One-class Text Classification with Multi-modal Deep Support Vector Data Description
This work presents multi-modal deep SVDD (mSVDD) for one-class text classification.
A Simple and Effective Usage of Word Clusters for CBOW Model
We propose a simple and effective method for incorporating word clusters into the Continuous Bag-of-Words (CBOW) model.
