Unverified50% confidenceFactExact time
Neural Ordinary Differential Equations (Neural ODE) were proposed by Chen et al. at NeurIPS 2018, parameterizing the continuous-time derivative of the system state with a neural network and integrating via an ODE solver.
1
Sources
50%
Confidence
Long-term
Relevance
10/3/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedNeural 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.72% similarUnverifiedThere exists a study titled 'Emergent Symbolic Structures in Artificial Neural Networks,' focusing on whether neural networks spontaneously form symbolic structures similar to human cognition during training.67% similarUnverifiedThe neuro-symbolic planning direction attempts to use neural networks to learn action models while retaining the reasoning structure of symbolic planners, so as to obtain the best of both worlds.66% similarUnverifiedBengio等人于2003年提出神经概率语言模型,将离散的n-gram统计模型升维为连续向量空间中的神经网络66% 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).66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/969831API
curl https://kongchang.com/api/v1/knowledge/claims/969831MCP
get_claim(id=969831)