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ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
To incorporate 3D information completely and efficiently, we propose a novel message passing scheme that operates within 1-hop neighborhood.
SpelsNet: Surface Primitive Elements Segmentation by B-Rep Graph Structure Supervision
Shah and Mรคntylรค [1995] from an unstructured 3D scan ( e.g .
Shadow Knowledge Distillation: Bridging Offline and Online Knowledge Transfer Lujun Li
KD aims to transfer knowledge from a high-capacity large model ( i.e., teacher) to a low-capacity
022ca1bed6b574b962c48a2856eb207b-Paper-Conference.pdf
GPU-hours to train) for the benefit of the research community.
Decoupled Self-supervised Learning for Graphs
We address this problem by developing a decou-pled self-supervised learning (DSSL) framework for graph neural networks.
Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives
Recently, various new methods have been proposed to adapt closed LLMs to private data without leaking private information to third parties and/or the LLM provider.
Efficient Knowledge Distillation from Model Checkpoints
In this paper, we observe that an intermediate model, i.e., a checkpoint in the middle of the training procedure, often serves as a better teacher compared to the fully converged model, although the former has much lower accuracy.
Large language models transition from integrating across position-yoked, exponential windows to structure-yoked, power-law windows
Prior work suggests that human brain responses to language exhibit hierarchically organized "integration windows" that substantially constrain the