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Found 7 papers, 1 papers with code
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.
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.
MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Network
Cardiac arrhythmia, a condition characterized by irregular heartbeats, often serves as an early indication of various heart ailments.
TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model
Clinical trials are indispensable for medical research and the development of new treatments.
Unraveling Babel: Exploring Multilingual Activation Patterns of LLMs and Their Applications
Recently, large language models (LLMs) have achieved tremendous breakthroughs in the field of NLP, but still lack understanding of their internal activities when processing different languages.
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.
