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Found 6 papers, 1 papers with code
Semantic-Preserving Abstractive Text Summarization with Siamese Generative Adversarial Net
We propose a novel siamese generative adversarial net for abstractive text summarization (SSPGAN), which can preserve the main semantics of the source text.
CoCGAN: Contrastive Learning for Adversarial Category Text Generation
The discriminator of CoCGAN discriminates the authenticity of given samples and optimizes a contrastive learning objective to capture both more flexible data-to-class relations and data-to-data relations among training samples.
TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model
Existing research often focuses on leveraging electronic health records (EHRs) to support clinical trial outcome prediction.
Unraveling Babel: Exploring Multilingual Activation Patterns within Large Language Models
Recently, large language models (LLMs) have achieved tremendous breakthroughs in the field of language processing, yet their mechanisms in processing multiple languages remain agnostic.
PaGraph: Scaling GNN Training on Large Graphs via Computation-aware Caching and Partitioning
Emerging graph neural networks (GNNs) have extended the successes of deep learning techniques against datasets like images and texts to more complex graph-structured data.
Walking with Perception: Efficient Random Walk Sampling via Common Neighbor Awareness
Random walk is widely applied to sample large-scale graphs due to its simplicity of implementation and solid theoretical foundations of bias analysis.
