arXiv:2112.15026
공식 코드 · Papers with Code
Two Instances of Interpretable Neural Network for Universal Approximations
원문 초록
This paper proposes two bottom-up interpretable neural network (NN) constructions for universal approximation, namely Triangularly-constructed NN (TNN) and Semi-Quantized Activation NN (SQANN). Further notable properties are (1) resistance to catastrophic forgetting (2) existence of proof for arbitrarily high accuracies (3) the ability to identify samples that are out-of-distribution through interpretable activation "fingerprints".
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Quantile-Based Deep Reinforcement Learning using Two-Timescale Policy Gradient Algorithms
Leveraging the two timescale regime to demonstrate convergence of neural networks
Training a Two Layer ReLU Network Analytically
MiniRBT: A Two-stage Distilled Small Chinese Pre-trained Model
FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction
Using Simple Incentives to Improve Two-Sided Fairness in Ridesharing Systems
토론
이 논문과 연결된 포럼 글이 아직 없습니다.