Tag: artificial neural networks
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Efficient Uncertainty Quantification Using Gradient Norms
Discover an efficient isotropic method for uncertainty quantification in large language models using gradient norms and a single model pass.
Monodense Neural Model for Accurate Price Elasticity
Discover how the Monodense deep neural model improves accuracy in estimating item price elasticity to optimize pricing and boost revenue.
Derived-Field Optimization Boosts Budgeted Neural Simulations
Discover how Derived-Field Optimization preserves fine-scale detail in budgeted neural simulators, enhancing accuracy under storage constraints.
Rethinking Long-Range Dependency in Integer Multiplication
Discover how reimagining computational spacetime solves integer multiplication without long-range dependency in neural networks.
Spectral Edge Thesis: Phase Transitions in Neural Training
Discover the Spectral Edge Thesis, a new framework explaining phase transitions in neural network training and its impact on learning dynamics.