Sign up to save your podcasts
Or
Share 12.11.2025: Clustering-Guided Neural Networks, Hierarchical Structure-Property Alignment, and Rotation-Invariant Molecular Learning
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
12.11.2025: Clustering-Guided Neural Networks, Hierarchical Structure-Property Alignment, and Rotation-Invariant Molecular Learning
This podcast is brought to you by the Oliver Laboratory at Vanderbilt University.
----------------------------------------
0:00 Hierarchical Structure-Property Alignment for Data-Efficient Molecular Generation and Editing (https://arxiv.org/pdf/2511.08080.pdf)
4:32 Hierarchical Direction Perception via Atomic Dot-Product Operators for Rotation-Invariant Point Clouds Learning (https://arxiv.org/pdf/2511.08240.pdf)
9:27 Improving the accuracy and generalizability of molecular property regression models with a substructure-substitution-rule-informed framework (https://arxiv.org/pdf/2511.08314.pdf)
13:53 Clustering Guided Residual Neural Networks for Multi-Tx Localization in Molecular Communications (https://arxiv.org/pdf/2511.08513.pdf)
----------------------------------------
Source code: https://github.com/OliverLaboratory/arxivreader
Contact: oliverlaboratory.com
Source code: https://github.com/OliverLaboratory/arxivreader
View all episodes
By This podcast is brought to you by the Oliver Laboratory at Vanderbilt University.
----------------------------------------
0:00 Hierarchical Structure-Property Alignment for Data-Efficient Molecular Generation and Editing (https://arxiv.org/pdf/2511.08080.pdf)
4:32 Hierarchical Direction Perception via Atomic Dot-Product Operators for Rotation-Invariant Point Clouds Learning (https://arxiv.org/pdf/2511.08240.pdf)
9:27 Improving the accuracy and generalizability of molecular property regression models with a substructure-substitution-rule-informed framework (https://arxiv.org/pdf/2511.08314.pdf)
13:53 Clustering Guided Residual Neural Networks for Multi-Tx Localization in Molecular Communications (https://arxiv.org/pdf/2511.08513.pdf)
----------------------------------------
Source code: https://github.com/OliverLaboratory/arxivreader
Contact: oliverlaboratory.com
Source code: https://github.com/OliverLaboratory/arxivreader