Tag: artificial neural networks
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FaCT: Faithful Concept Traces for Neural Network Explainability
Discover FaCT, a novel method offering faithful concept traces to explain neural network decisions with improved interpretability and consistency.
Improving Symbolic Distillation via Teacher-Student Smoothness
Discover how teacher-student smoothness alignment enhances genetic programming-based symbolic distillation for better explainable AI models.
Mathematical Link Between Layer Norm & Dynamic Activations
Explore the mathematical relationship between layer normalization and dynamic activation functions, introducing DyISRU for improved neural network performa...
Loop Corrections Impact on Random Feature Model Errors
Explore how loop corrections affect training and generalization errors in random feature models, enhancing neural network performance analysis.
Dense Associative Memory: Finite-Size and Robustness Analysis
Explore finite-size guarantees and adversarial robustness in Dense Associative Memory, enhancing neural network efficiency and reliability.