Self-supervised pre-training on multimodal neutrino detector simulations produces reusable representations that improve downstream classification, regression, and data efficiency over training from scratch.
Graphmae: Self-supervised masked graph autoencoders
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SSL4RL reformulates self-supervised learning objectives into dense, verifiable reward signals for RL-based fine-tuning of vision-language models, yielding performance gains on reasoning benchmarks.
SCGFM creates transferable graph representations by aligning heterogeneous topologies to shared learnable geometric bases via Gromov-Wasserstein distances and re-encoding features accordingly.
citing papers explorer
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Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-training
Self-supervised pre-training on multimodal neutrino detector simulations produces reusable representations that improve downstream classification, regression, and data efficiency over training from scratch.
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SSL4RL: Revisiting Self-supervised Learning as Intrinsic Reward for Visual-Language Reasoning
SSL4RL reformulates self-supervised learning objectives into dense, verifiable reward signals for RL-based fine-tuning of vision-language models, yielding performance gains on reasoning benchmarks.
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Structure-Centric Graph Foundation Model via Geometric Bases
SCGFM creates transferable graph representations by aligning heterogeneous topologies to shared learnable geometric bases via Gromov-Wasserstein distances and re-encoding features accordingly.