mlsanityData to 7 Oct 2026

Multitask Extension of Geometrically Aligned Transfer Encoder

arXiv 2405.01974 ↗First posted 3 May 2024cs.LG cs.AI q-bio.QMcited by 3

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Abstract (arXiv)

Molecular datasets often suffer from a lack of data. It is well-known that gathering data is difficult due to the complexity of experimentation or simulation involved. Here, we leverage mutual information across different tasks in molecular data to address this issue. We extend an algorithm that utilizes the geometric characteristics of the encoding space, known as the Geometrically Aligned Transfer Encoder (GATE), to a multi-task setup. Thus, we connect multiple molecular tasks by aligning the curved coordinates onto locally flat coordinates, ensuring the flow of information from source tasks to support performance on target data.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 3 May 2024.

Builds on

7 of 23 references.

References · 7

  1. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  2. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
  3. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  4. 2019Analyzing Learned Molecular Representations for Property Predictioncited by 373
  5. 2018Learning Multimodal Graph-to-Graph Translation for Molecular Optimizationcited by 87
  6. 2023Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Taskscited by 6
  7. 2022Grouping-matrix based Graph Pooling with Adaptive Number of Clusterscited by 5

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Citing papers per year: 2024: 1; 2025: 2.

3 papers.