First VLTI-GRAVITY near-infrared observations of blazars indicate possible detection of unresolved or partially resolved jet emission in Ton 599, though data cannot distinguish extended structure from instrumental coherence loss.
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DynaMiCS uses short probing runs to build a slope matrix of cross-domain effects and solves a constrained optimization over mixture weights to improve targets while respecting performance bounds on constrained domains.
Transformer components arise as the natural solution to precision-weighted directional state estimation on the hypersphere.
MIRA is a new analytic score for conditional distribution accuracy derived from equal probability mass assignment, enabling Bayesian model comparison via direct posterior validation.
Modified LCEN for classification selects sparse features and achieves high F1 and MCC scores, while the diffMCC loss outperforms weighted cross-entropy by 4.9% F1 and 8.5% MCC on average across experiments.
citing papers explorer
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VLTI-GRAVITY observations of blazars
First VLTI-GRAVITY near-infrared observations of blazars indicate possible detection of unresolved or partially resolved jet emission in Ton 599, though data cannot distinguish extended structure from instrumental coherence loss.
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DynaMiCS: Fine-tuning LLMs with Performance Constraints using Dynamic Mixtures
DynaMiCS uses short probing runs to build a slope matrix of cross-domain effects and solves a constrained optimization over mixture weights to improve targets while respecting performance bounds on constrained domains.
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RT-Transformer: The Transformer Block as a Spherical State Estimator
Transformer components arise as the natural solution to precision-weighted directional state estimation on the hypersphere.
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MIRA: A Score for Conditional Distribution Accuracy and Model Comparison
MIRA is a new analytic score for conditional distribution accuracy derived from equal probability mass assignment, enabling Bayesian model comparison via direct posterior validation.
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Improving Performance in Classification Tasks with LCEN and the Weighted Focal Differentiable MCC Loss
Modified LCEN for classification selects sparse features and achieves high F1 and MCC scores, while the diffMCC loss outperforms weighted cross-entropy by 4.9% F1 and 8.5% MCC on average across experiments.