Unverified50% confidenceFactExact time
Neural ODE was proposed by Chen Tianqi et al. in a NeurIPS 2018 Best Paper, with the core idea of treating the discrete layers of a residual network as the Euler discretization of a continuous dynamical system.
1
Sources
50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe phenomenon that optimization of over-parameterized neural networks easily converges to good solutions can be explained by Neural Tangent Kernel theory74% similarUnverifiedThe neural network scaling laws proposed by Kaplan et al. in 2020 indicate that model performance has a power-law relationship with data volume, parameter count, and compute, where the model's cross-entropy loss L and parameter count N satisfy L ∝ N^(-α).73% similarUnverified贝叶斯定理在朴素贝叶斯分类器、贝叶斯神经网络、变分自编码器(VAE)和扩散模型中都有直接应用69% similarUnverified卷积神经网络的核心创新在于权重共享与局部连接机制,通过滑动卷积核共享同一组参数降低模型参数量68% similarUnverified对抗样本概念由Szegedy等人于2013年在论文《Intriguing properties of neural networks》中正式提出68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/895895API
curl https://kongchang.com/api/v1/knowledge/claims/895895MCP
get_claim(id=895895)