Unverified50% confidenceFactExact time
The neural tangent kernel (NTK) is the kernel limit of infinitely wide neural networks under gradient descent training; its linearization assumption ignores feature learning during training of deep networks, making it difficult to explain the complex generalization phenomena of finite-width nonlinear networks.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe NTK framework cannot capture the feature learning phenomena observed in real finite-width networks.76% similarUnverifiedNeural networks encounter vanishing gradient problems as layers are stacked deeper, making training difficult to converge, and it was not until around 2010 that a series of technical breakthroughs solved the challenge of training deep networks, giving rise to the term deep neural network (DNN).75% similarUnverifiedNeural operators are a class of deep learning models that learn mappings between infinite-dimensional function spaces, aiming to approximate the solution operator of PDEs, are resolution-independent, and representative architectures include FNO and DeepONet.66% similarUnverified残差连接与层归一化解决了深层网络训练不稳定的问题,使模型可堆叠到数十层66% similarUnverified深度神经网络由输入层、若干隐藏层、输出层构成,每个神经元对前一层输入做加权求和加偏置后通过激活函数做非线性变换65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1065874API
curl https://kongchang.com/api/v1/knowledge/claims/1065874MCP
get_claim(id=1065874)