Search Results for author:
Found 22 papers, 5 papers with code
Cross-Lingual Transformers for Neural Automatic Post-Editing
Finally, to address the over-correction problem, we select the final output among the PE outputs and the original MT sentence based on a sentence-level quality estimation.
Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation
In this paper, we describe the Bering Lab’s submission to the WMT 2020 Shared Task on Quality Estimation (QE).
Consensus-aware Contrastive Learning for Group Recommendation
Group recommendation aims to provide personalized item suggestions to a group of users by reflecting their collective preferences.
Learning to Contextualize Web Pages for Enhanced Decision Making by LLM Agents
In this work, we introduce LCoW, a framework for Learning language models to Contextualize complex Web pages into a more comprehensible form, thereby enhancing decision making by LLM agents.
Debiasing Classifiers by Amplifying Bias with Latent Diffusion and Large Language Models
Neural networks struggle with image classification when biases are learned and misleads correlations, affecting their generalization and performance.
Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting
To overcome these limitations, we introduce a fast and effective Spectral Attention mechanism, which preserves temporal correlations among samples and facilitates the handling of long-range information while maintaining the base model structure.
A Comprehensive Survey of Time Series Forecasting: Architectural Diversity and Open Challenges
By comparing and re-examining various deep learning models, we uncover new perspectives and presents the latest trends in time series forecasting, including the emergence of hybrid models, diffusion models, Mamba models, and foundation models.
DiffInject: Revisiting Debias via Synthetic Data Generation using Diffusion-based Style Injection
Dataset bias is a significant challenge in machine learning, where specific attributes, such as texture or color of the images are unintentionally learned resulting in detrimental performance.
MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation
Thereafter, we generate various candidate SQL queries based on the refined schema and diverse prompts.
Gradient Alignment with Prototype Feature for Fully Test-time Adaptation
In context of Test-time Adaptation(TTA), we propose a regularizer, dubbed Gradient Alignment with Prototype feature (GAP), which alleviates the inappropriate guidance from entropy minimization loss from misclassified pseudo label.
Read-only Prompt Optimization for Vision-Language Few-shot Learning
RPO leverages masked attention to prevent the internal representation shift in the pre-trained model.
Ranked #9 on
Prompt Engineering
on Caltech-101
Hierarchical Contrastive Learning with Multiple Augmentation for Sequential Recommendation
Thus, it is still possible to extend existing approaches to boost the effects of augmentation methods by using progressed structures with the combinations of multiple augmentation methods.
A Custom IC Layout Generation Engine Based on Dynamic Templates and Grids
The layout generation framework is applied to various design examples and generates DRC/LVS clean layouts automatically in multiple CMOS technologies.
A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction
Here we present the first large-scale benchmark of Korean legal AI datasets, LBOX OPEN, that consists of one legal corpus, two classification tasks, two legal judgement prediction (LJP) tasks, and one summarization task.
Learning multiple gaits of quadruped robot using hierarchical reinforcement learning
There is a growing interest in learning a velocity command tracking controller of quadruped robot using reinforcement learning due to its robustness and scalability.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Intent-based Product Collections for E-commerce using Pretrained Language Models
In this paper, we use a pretrained language model (PLM) that leverages textual attributes of web-scale products to make intent-based product collections.
IntelliCAT: Intelligent Machine Translation Post-Editing with Quality Estimation and Translation Suggestion
We present IntelliCAT, an interactive translation interface with neural models that streamline the post-editing process on machine translation output.
KLUE: Korean Language Understanding Evaluation
We introduce Korean Language Understanding Evaluation (KLUE) benchmark.
One-Shot Learning for Text-to-SQL Generation
We show that our model outperforms state-of-the-art approaches for various text-to-SQL datasets in two aspects: 1) the SQL generation accuracy for the trained templates, and 2) the adaptability to the unseen SQL templates based on a single example without any additional training.
Clause-Wise and Recursive Decoding for Complex and Cross-Domain Text-to-SQL Generation
We focus on the Spider dataset, a complex and cross-domain text-to-SQL task, which includes complex queries over multiple tables.
CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms
Many hyperparameter optimization (HyperOpt) methods assume restricted computing resources and mainly focus on enhancing performance.
