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Found 5 papers, 0 papers with code
Universal Graph Continual Learning
We address catastrophic forgetting issues in graph learning as incoming data transits from one to another graph distribution.
Improving Document Image Understanding with Reinforcement Finetuning
In this paper, we investigate the problem of improving the performance of Artificial Intelligence systems in understanding document images, especially in cases where training data is limited.
HYCEDIS: HYbrid Confidence Engine for Deep Document Intelligence System
Measuring the confidence of AI models is critical for safely deploying AI in real-world industrial systems.
Make The Most of Prior Data: A Solution for Interactive Text Summarization with Preference Feedback
For summarization, human preference is critical to tame outputs of the summarizer in favor of human interests, as ground-truth summaries are scarce and ambiguous.
Robust Deep Reinforcement Learning for Extractive Legal Summarization
Automatic summarization of legal texts is an important and still a challenging task since legal documents are often long and complicated with unusual structures and styles.
