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8d2355364e9a2ba1f82f975414937b43-Paper.pdf
Foraquickcomparison, see Table1and Related Work. Asreinforcement grows, t representation Q-function, RLalgorithms.
Composing Parameter-Efficient Modules with Arithmetic Operations
Our approach requires no additional training and enables highly flexible module composition.
For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets
Hierarchical Bayesian methods enable information sharing across regression problems on multiple groups of data.
CurriculumFine-tuningofVisionFoundationModel forMedicalImageClassificationUnderLabelNoise
Our experimental results demonstrate that CUFIT outperforms previous methods across various medical image benchmarks.
ABanditLearningAlgorithmandApplicationsto AuctionDesign
We introduce a regularity notion that generalizes the notion of concavity.
NeuralArchitectureGeneratorOptimization
Neural Architecture Search (NAS) wasfirst proposed toachievestate-of-the-art performance through thediscoveryofnewarchitecture patterns, without human intervention.