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Found 4 papers, 1 papers with code
ELITE: Embedding-Less retrieval with Iterative Text Exploration
Large Language Models (LLMs) have achieved impressive progress in natural language processing, but their limited ability to retain long-term context constrains performance on document-level or multi-turn tasks.
Molecular design method based on novel molecular representation and variational auto-encoder
Based on the traditional VAE, a novel neural network model is presented, with the latest molecular representation, SELFIES, to improve the effect of generating new molecules.
LAC-Nav: Collision-Free Mutiagent Navigation Based on The Local Action Cells
Based on the realtime updated local action cells, we propose the LAC-Nav approach to navigate the agent with the properly selected velocity; and furthermore, we coupled the local action cell with an adaptive learning framework, in which the effect of selections are evaluated and used as the references for making decisions in the following updates.
PyramNet: Point Cloud Pyramid Attention Network and Graph Embedding Module for Classification and Segmentation
Specifically, GEM projects point cloud onto the graph and practices the covariance matrix to explore the relationship between points, so as to improve the local feature expression ability of the model.
