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Found 4 papers, 0 papers with code
Minimizing Factual Inconsistency and Hallucination in Large Language Models
Large Language Models (LLMs) are widely used in critical fields such as healthcare, education, and finance due to their remarkable proficiency in various language-related tasks.
Synergistic Fusion of Graph and Transformer Features for Enhanced Molecular Property Prediction
While recent advances in Graph Neural Networks (GNNs) and Transformers have shown to be effective and promising, they face the following limitations: Transformer self-attention does not explicitly consider the underlying molecule structure while GNN feature representation alone is not sufficient to capture granular and hidden interactions and characteristics that distinguish similar molecules.
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Molecular Property Prediction
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Knowledge Capture and Replay for Continual Learning
Deep neural networks have shown promise in several domains, and the learned data (task) specific information is implicitly stored in the network parameters.
Classify and Generate: Using Classification Latent Space Representations for Image Generations
Utilization of classification latent space information for downstream reconstruction and generation is an intriguing and a relatively unexplored area.
