Systematic Exploration of 4-Expert Heterogeneous Mixture-of-Experts via Automated Pipeline Search
arXiv 2606.23739•af60ac074b85edd38674627b57c17d32b5dc6944ebb86b1278b4603be957703f
DPOGaussian-processLoRAMCMCMoEanalogue-hardwareautomated-searchbenchmarkingcausal-discoverycausal-inferencechemical-discoverydomain-generalizationepitope-prediction','protein-surface-modeling','transformers','Gfederated-learninglow-power-mlmachine-learningmeta-learningmixture-of-expertsoffline-reinforcement-learningopen-set-recognitionopen-sourcephysical-neural-networksreproducibilitysampling-biasweight-space-analysis
Paper metadata
- arXiv ID
- 2606.23739
- Version
- Not specified by this published record
- Category
- Computer Science — Machine Learning (cs.LG)
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Evidence and limitations
- Source ID
- arxiv_cs_lg
- Record identifier
- af60ac074b85edd38674627b57c17d32b5dc6944ebb86b1278b4603be957703f
- Enrichment time
- 2026-06-24T08:52:15Z
- AI-assisted enrichment
- Yes
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