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Christopher M. Bryant
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Christopher M. Bryant12 visible / 12 total nodes / 50 links

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Related contextCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipCo-authorshipWorks onWorks onAuthorshipWorks onWorks onWorks onWorks onWorks onWorks onWorks onAuthorshipWorks onWorks onWorks onWorks onWorks onWorks onAuthorshipAuthorshipAuthorshipAuthorshipTopic signalTopic signalTopic signalAuthorshipAuthorshipAuthorshipChristopher M. BryantAChristopher M. BryantResearcherPractical Scaling Laws: Converting Compute into Performance in a Data-Constrained WorldWPractical Scaling Laws:...preprint / 2026Inverse Design of Multi-Layer Sub-Pixel-Resolution RF Passives Through Grayscale Diffusion with Flexible S-Parameter ConditioningWInverse Design of Multi...preprint / 2026Tommaso DreossiATommaso DreossiResearcherHao LiuAHao LiuResearcherNathan MirmanANathan MirmanResearcherNoah KesslerANoah KesslerResearcherMichael FreiAMichael FreiResearcherHarish KrishnaswamyAHarish KrishnaswamyResearcherHao LiuAHao LiuResearcherMachine LearningTMachine Learning49008 workseess.SPTeess.SP8234 works
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Christopher M. Bryant

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PaperPractical Scaling Laws: Converting Compute into Performance in a Data-Constrained WorldAuthorship / preprint / 2026PaperInverse Design of Multi-Layer Sub-Pixel-Resolution RF Passives Through Grayscale Diffusion with Flexible S-Parameter ConditioningAuthorship / preprint / 2026PersonTommaso DreossiCo-authorship / ResearcherPersonHao LiuCo-authorship / ResearcherPersonNathan MirmanCo-authorship / ResearcherPersonNoah KesslerCo-authorship / ResearcherPersonMichael FreiCo-authorship / ResearcherPersonHarish KrishnaswamyCo-authorship / Researcher

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