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Found 11 papers, 2 papers with code
Matching The Statements: A Simple and Accurate Model for Key Point Analysis
Key Point Analysis (KPA) is one of the most essential tasks in building an Opinion Summarization system, which is capable of generating key points for a collection of arguments toward a particular topic.
Multi-level Community-awareness Graph Neural Networks for Neural Machine Translation
Intuitively, the MC-GNN layer substitutes a self-attention layer at the encoder side of a transformer-based machine translation model.
Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A~Case~Study~at~HCMUT
In today's rapidly evolving landscape of Artificial Intelligence, large language models (LLMs) have emerged as a vibrant research topic.
A Deep Learning-Based System for Automatic Case Summarization
This paper presents a deep learning-based system for efficient automatic case summarization.
Classifying Whole Slide Images: What Matters?
Recently there have been many algorithms proposed for the classification of very high resolution whole slide images (WSIs).
Semi-supervised Neural Machine Translation with Consistency Regularization for Low-Resource Languages
We also introduce a SentenceBERT-based filter to enhance the quality of augmenting data by retaining semantically similar sentence pairs.
A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting
Precisely forecasting wind speed is essential for wind power producers and grid operators.
Spatial-temporal Multi-Task Learning for Within-field Cotton Yield Prediction
Understanding and accurately predicting within-field spatial variability of crop yield play a key role in site-specific management of crop inputs such as irrigation water and fertilizer for optimized crop production.
Predicting Opioid Relapse Using Social Media Data
In this paper, we introduce a Generative Adversarial Networks (GAN) model to predict the addiction relapses based on sentiment images and social influences.
Forecasting People's Needs in Hurricane Events from Social Network
Our proposed sequence to sequence method forecast people's needs more successfully than either of the other models.
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning
To improve the efficiency of the emergency response in the immediate aftermath of a disaster, we propose a heuristic multi-agent reinforcement learning scheduling algorithm, named as ResQ, which can effectively schedule the rapid deployment of volunteers to rescue victims in dynamic settings.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
