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A conditional invertible neural network (cINN) is a variant of normalizing flows that maps a simple base distribution to the target posterior distribution through invertible transformations, and can give the log-likelihood of the posterior density in exact form.
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10/14/2026
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UnverifiedThe learned summary statistics are fed into two parallel branches: the model selection network computes posterior model probabilities, and multiple model-specific conditional invertible neural networks (cINNs) separately estimate the parameter posterior distributions under each model.73% similarUnverifiedCommon normalizing flow architectures include Real-NVP, Neural Autoregressive Flow (NAF), and Continuous Normalizing Flow (CNF), each with trade-offs in computational efficiency and expressiveness.67% similarUnverifiedThe convolution operation in convolutional neural networks (CNNs) inherently possesses translation invariance.67% similarVerified神经网络的反向传播算法是链式法则的直接应用，梯度下降依赖微积分中的偏导数概念67% similarUnverifiedThe phenomenon that optimization of over-parameterized neural networks easily converges to good solutions can be explained by Neural Tangent Kernel theory67% similar
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