Neural tangent kernel

In the study of artificial neural networks (ANNs), the neural tangent kernel (NTK) is a kernel which describes the evolution of deep artificial neural networks during their training by gradient descent. It allows ANNs to be studied using theoretical tools from Kernel Methods.

For most common neural network architectures, in the limit of large layer width the NTK becomes constant. This enables simple closed form statements to be made about neural network predictions, training dynamics, generalization, and loss surfaces. For example, it guarantees that wide enough ANNs converge to a global minimum when trained to minimize an empirical loss. The NTK of large width networks is also related to several other large width limits of neural networks.

The NTK was introduced in 2018 by Arthur Jacot, Franck Gabriel and Clément Hongler.[1] It was also implicit in some contemporaneous work.[2][3][4]

Definition

Scalar output case

An Artificial Neural Network (ANN) with scalar output consists in a family of functions ${\displaystyle f\left(\cdot ,\theta \right):\mathbb {R} ^{n_{\mathrm {in} }}\to \mathbb {R} }$ parametrized by a vector of parameters ${\displaystyle \theta \in \mathbb {R} ^{P}}$.

The Neural Tangent Kernel (NTK) is a kernel ${\displaystyle \Theta$ :\mathbb {R} ^{n_{\mathrm {in} }}\times \mathbb {R} ^{n_{\mathrm {in} }}\to \mathbb {R} } defined by

$${\displaystyle \Theta \left(x,y;\theta \right)=\sum _{p=1}^{P}\partial _{\theta _{p}}f\left(x;\theta \right)\partial _{\theta _{p}}f\left(y;\theta \right).}$$

In the language of kernel methods, the NTK ${\displaystyle \Theta }$ is the kernel associated with the feature map ${\displaystyle \left(x\mapsto \partial _{\theta _{p}}f\left(x;\theta \right)\right)_{p=1,\ldots ,P}}$.

Vector output case

An ANN with vector output of size ${\displaystyle n_{\mathrm {out} }}$ consists in a family of functions ${\displaystyle f\left(\cdot$ ;\theta \right):\mathbb {R} ^{n_{\mathrm {in} }}\to \mathbb {R} ^{n_{\mathrm {out} }}} parametrized by a vector of parameters ${\displaystyle \theta \in \mathbb {R} ^{P}}$.

In this case, the Neural Tangent Kernel ${\displaystyle \Theta$ :\mathbb {R} ^{n_{\mathrm {in} }}\times \mathbb {R} ^{n_{\mathrm {in} }}\to {\mathcal {M}}_{n_{\mathrm {out} }}\left(\mathbb {R} \right)} is a matrix-valued kernel, with values in the space of ${\displaystyle n_{\mathrm {out} }\times n_{\mathrm {out} }}$ matrices, defined by

$${\displaystyle \Theta _{k,l}\left(x,y;\theta \right)=\sum _{p=1}^{P}\partial _{\theta _{p}}f_{k}\left(x;\theta \right)\partial _{\theta _{p}}f_{l}\left(y;\theta \right).}$$

Derivation

When optimizing the parameters ${\displaystyle \theta \in \mathbb {R} ^{P}}$ of an ANN to minimize an empirical loss through gradient descent, the NTK governs the dynamics of the ANN output function ${\displaystyle f_{\theta }}$ throughout the training.

Scalar output case

For a dataset ${\displaystyle \left(x_{i}\right)_{i=1,\ldots ,n}\subset \mathbb {R} ^{n_{\mathrm {in} }}}$ with scalar labels ${\displaystyle \left(z_{i}\right)_{i=1,\ldots ,n}\subset \mathbb {R} }$ and a loss function ${\displaystyle c:\mathbb {R} \to \mathbb {R} }$, the associated empirical loss, defined on functions ${\displaystyle f:\mathbb {R} ^{n_{\mathrm {in} }}\to \mathbb {R} }$, is given by

$${\displaystyle {\mathcal {C}}\left(f\right)=\sum _{i=1}^{n}c\left(f\left(x_{i}\right),z_{i}\right).}$$

When training the ANN ${\displaystyle f\left(\cdot$ ;\theta \right):\mathbb {R} ^{n_{\mathrm {in} }}\to \mathbb {R} } is trained to fit the dataset (i.e. minimize ${\displaystyle {\mathcal {C}}}$) via continuous-time gradient descent, the parameters ${\displaystyle \left(\theta \left(t\right)\right)_{t\geq 0}}$ evolve through the ordinary differential equation:

$${\displaystyle \partial _{t}\theta \left(t\right)=-\nabla {\mathcal {C}}\left(f\left(\cdot$$ ;\theta \right)\right).}

During training the ANN output function follows an evolution differential equation given in terms of the NTK:

${\displaystyle \partial _{t}f\left(x;\theta \left(t\right)\right)=-\sum _{i=1}^{n}\Theta \left(x,x_{i};\theta \right)\partial _{w}c\left(w,z_{i}\right){\Big |}_{w=f\left(x_{i};\theta \left(t\right)\right)}.}$

This equation shows how the NTK drives the dynamics of ${\displaystyle f\left(\cdot$ ;\theta \left(t\right)\right)} in the space of functions ${\displaystyle \mathbb {R} ^{n_{\mathrm {in} }}\to \mathbb {R} }$ during training.

Vector output case

For a dataset ${\displaystyle \left(x_{i}\right)_{i=1,\ldots ,n}\subset \mathbb {R} ^{n_{\mathrm {in} }}}$ with vector labels ${\displaystyle \left(z_{i}\right)_{i=1,\ldots ,n}\subset \mathbb {R} ^{n_{\mathrm {out} }}}$ and a loss function ${\displaystyle c:\mathbb {R} ^{n_{\mathrm {out} }}\to \mathbb {R} }$, the corresponding empirical loss on functions ${\displaystyle f:\mathbb {R} ^{n_{\mathrm {in} }}\to \mathbb {R} ^{n_{\mathrm {out} }}}$ is defined by

$${\displaystyle {\mathcal {C}}\left(f\right)=\sum _{i=1}^{n}c\left(f\left(x_{i}\right),z_{i}\right).}$$

The training of ${\displaystyle f_{\theta \left(t\right)}}$ through continuous-time gradient descent yields the following evolution in function space driven by the NTK:

$${\displaystyle \partial _{t}f_{k}\left(x;\theta \left(t\right)\right)=-\sum _{i=1}^{n}\sum _{l=1}^{n_{\mathrm {out} }}\Theta _{k,l}\left(x,x_{i};\theta \right)\partial _{w_{l}}c\left(\left(w_{1},\ldots ,w_{n_{\mathrm {out} }}\right),z_{i}\right){\Big |}_{w=f\left(x_{i};\theta \left(t\right)\right)}.}$$

Interpretation

The NTK ${\displaystyle \Theta \left(x,x_{i};\theta \right)}$ represents the influence of the loss gradient ${\displaystyle \partial _{w}c\left(w,z_{i}\right){\big |}_{w=f\left(x_{i};\theta \right)}}$ with respect to example ${\displaystyle i}$ on the evolution of ANN output ${\displaystyle f\left(x;\theta \right)}$ through a gradient descent step: in the scalar case, this reads

$${\displaystyle f\left(x;\theta \left(t+\epsilon \right)\right)-f\left(x;\theta \left(t\right)\right)\approx \epsilon \sum _{i=1}^{n}\Theta \left(x,x_{i};\theta \left(t\right)\right)\partial _{w}c\left(w,z_{i}\right){\big |}_{w=f\left(x_{i};\theta \right)}.}$$

In particular, each data point ${\displaystyle x_{i}}$ influences the evolution of the output ${\displaystyle f\left(x;\theta \right)}$ for each ${\displaystyle x}$ throughout the training, in a way that is captured by the NTK ${\displaystyle \Theta \left(x,x_{i};\theta \right)}$.

Large-width limit

Recent theoretical and empirical work in Deep Learning has shown the performance of ANNs to strictly improve as their layer widths grow larger.[5][6] For various ANN architectures, the NTK yields precise insight into the training in this large-width regime.[1][7][8][9][10][11]

Wide fully-connected ANNs have a deterministic NTK, which remains constant throughout training

Consider an ANN with fully-connected layers ${\displaystyle \ell =0,\ldots ,L}$ of widths ${\displaystyle n_{0}=n_{\mathrm {in} },n_{1},\ldots ,n_{L}=n_{\mathrm {out} }}$, so that ${\displaystyle f\left(\cdot$ ;\theta \right)=R_{L-1}\circ \cdots \circ R_{0}} , where ${\displaystyle R_{\ell }=\sigma \circ A_{\ell }}$ is the composition of an affine transformation ${\displaystyle A_{i}}$ with the pointwise application of a nonlinearity ${\displaystyle \sigma$ :\mathbb {R} \to \mathbb {R} } , where ${\displaystyle \theta }$ parametrizes the maps ${\displaystyle A_{0},\ldots ,A_{L-1}}$. The parameters ${\displaystyle \theta \in \mathbb {R} ^{P}}$ are initialized randomly, in an independent identically distributed way.

The scale of the NTK as the widths grow is affected by the exact parametrization of the ${\displaystyle A_{i}}$'s and by the initialization of the parameters. This motivates the so-called NTK parametrization ${\displaystyle A_{\ell }\left(x\right)={\frac {1}{\sqrt {n_{\ell }}}}W^{\left(\ell \right)}x+b^{\left(\ell \right)}}$. This parametrization ensures that if the parameters ${\displaystyle \theta \in \mathbb {R} ^{P}}$ are initialized as standard normal variables, the NTK has a finite nontrivial limit. In the large-width limit, the NTK converges to a deterministic (non-random) limit ${\displaystyle \Theta _{\infty }}$, which stays constant in time.

The NTK ${\displaystyle \Theta _{\infty }}$ is explicitly given by ${\displaystyle \Theta _{\infty }=\Theta ^{\left(L\right)}}$, where ${\displaystyle \Theta ^{\left(L\right)}}$ is determined by the set of recursive equations:

${\displaystyle {\begin{aligned}\Theta ^{\left(1\right)}\left(x,y\right)&=\Sigma ^{\left(1\right)}\left(x,y\right),\\\Sigma ^{\left(1\right)}\left(x,y\right)&={\frac {1}{n_{\mathrm {in} }}}x^{T}y+1,\\\Theta ^{\left(\ell +1\right)}\left(x,y\right)&=\Theta ^{\left(\ell \right)}\left(x,y\right){\dot {\Sigma }}^{\left(\ell +1\right)}\left(x,y\right)+\Sigma ^{\left(\ell +1\right)}\left(x,y\right),\\\Sigma ^{\left(\ell +1\right)}\left(x,y\right)&=L_{\Sigma ^{\left(\ell \right)}}^{\sigma }\left(x,y\right),\\{\dot {\Sigma }}^{\left(\ell +1\right)}\left(x,y\right)&=L_{\Sigma ^{\left(\ell \right)}}^{\dot {\sigma }},\end{aligned}}}$

where ${\displaystyle L_{K}^{f}}$ denotes the kernel defined in terms of the Gaussian expectation:

${\displaystyle L_{K}^{f}\left(x,y\right)=\mathbb {E} _{\left(X,Y\right)\sim {\mathcal {N}}\left(0,{\begin{pmatrix}K\left(x,x\right)&K\left(x,y\right)\\K\left(y,x\right)&K\left(y,y\right)\end{pmatrix}}\right)}\left[f\left(X\right)f\left(Y\right)\right].}$

In this formula the kernels ${\displaystyle \Sigma ^{\left(\ell \right)}}$ are the so-called activation kernels[12][13][14] of the ANN.

Wide fully connected networks are linear in their parameters throughout training

The NTK describes the evolution of neural networks under gradient descent in function space. Dual to this perspective is an understanding of how neural networks evolve in parameter space, since the NTK is defined in terms of the gradient of the ANN's outputs with respect to its parameters. In the infinite width limit, the connection between these two perspectives becomes especially interesting. The NTK remaining constant throughout training at large widths co-occurs with the ANN being well described throughout training by its first order Taylor expansion around its parameters at initialization:[9]

${\displaystyle f\left(x;\theta (t)\right)=f\left(x;\theta (0)\right)+\nabla _{\theta }f\left(x;\theta (0)\right)\left(\theta (t)-\theta (0)\right)+{\mathcal {O}}\left(\min \left(n_{1}\dots n_{L-1}\right)^{-{\frac {1}{2}}}\right).}$

Other architectures

The NTK can be studied for various ANN architectures[10], in particular Convolutional Neural Networks (CNNs)[15], Recurrent Neural Networks (RNNs), Transformer Neural Networks.[16] In such settings, the large-width limit corresponds to letting the number of parameters grow, while keeping the number of layers fixed: for CNNs, this amounts to letting the number of channels grow.

Applications

Convergence to a global minimum

For a convex loss functional ${\displaystyle {\mathcal {C}}}$ with a global minimum, if the NTK remains positive-definite during training, the loss of the ANN ${\displaystyle {\mathcal {C}}\left(f\left(\cdot$ ;\theta \left(t\right)\right)\right)} converges to that minimum as ${\displaystyle t\to \infty }$. This positive-definiteness property has been shown in a number of cases, yielding the first proofs that large-width ANNs converge to global minima during training.[1][7][17]

Kernel methods

The NTK gives a rigorous connection between the inference performed by infinite-width ANNs and that performed by kernel methods: when the loss function is the least-squares loss, the inference performed by an ANN is in expectation equal to the kernel ridge regression (with zero ridge) with respect to the NTK ${\displaystyle \Theta _{\infty }}$. This suggests that the performance of large ANNs in the NTK parametrization can be replicated by kernel methods for suitably chosen kernels.[1][10]

Software libraries

Neural Tangents is a free and open-source Python library used for computing and doing inference with the infinite width NTK and Neural network Gaussian process (NNGP) corresponding to various common ANN architectures.[18]

References

1. Jacot, Arthur; Gabriel, Franck; Hongler, Clement (2018), Bengio, S.; Wallach, H.; Larochelle, H.; Grauman, K. (eds.), "Neural Tangent Kernel: Convergence and Generalization in Neural Networks" (PDF), Advances in Neural Information Processing Systems 31, Curran Associates, Inc., pp. 8571–8580, arXiv:1806.07572, Bibcode:2018arXiv180607572J, retrieved 2019-11-27
2. Li, Yuanzhi; Liang, Yingyu (2018). "Learning overparameterized neural networks via stochastic gradient descent on structured data". Advances in Neural Information Processing Systems.
3. Allen-Zhu, Zeyuan; Li, Yuanzhi; Song, Zhao (2018). "A convergence theory for deep learning via overparameterization". International Conference on Machine Learning.
4. Du, Simon S; Zhai, Xiyu; Poczos, Barnabas; Aarti, Singh (2019). "Gradient descent provably optimizes over-parameterized neural networks". International Conference on Learning Representations.
5. Novak, Roman; Bahri, Yasaman; Abolafia, Daniel A.; Pennington, Jeffrey; Sohl-Dickstein, Jascha (2018-02-15). "Sensitivity and Generalization in Neural Networks: an Empirical Study". arXiv:1802.08760. Bibcode:2018arXiv180208760N. Cite journal requires |journal= (help)
6. Canziani, Alfredo; Paszke, Adam; Culurciello, Eugenio (2016-11-04). "An Analysis of Deep Neural Network Models for Practical Applications". arXiv:1605.07678. Bibcode:2016arXiv160507678C. Cite journal requires |journal= (help)
7. Allen-Zhu, Zeyuan; Li, Yuanzhi; Song, Zhao (2018-11-09). "A Convergence Theory for Deep Learning via Over-Parameterization". International Conference on Machine Learning: 242–252. arXiv:1811.03962.
8. Du, Simon; Lee, Jason; Li, Haochuan; Wang, Liwei; Zhai, Xiyu (2019-05-24). "Gradient Descent Finds Global Minima of Deep Neural Networks". International Conference on Machine Learning: 1675–1685. arXiv:1811.03804.
9. Lee, Jaehoon; Xiao, Lechao; Schoenholz, Samuel S.; Bahri, Yasaman; Novak, Roman; Sohl-Dickstein, Jascha; Pennington, Jeffrey (2018-02-15). "Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent". arXiv:1902.06720. Cite journal requires |journal= (help)
10. Arora, Sanjeev; Du, Simon S; Hu, Wei; Li, Zhiyuan; Salakhutdinov, Russ R; Wang, Ruosong (2019), "On Exact Computation with an Infinitely Wide Neural Net", NeurIPS: 8139–8148, arXiv:1904.11955
11. Huang, Jiaoyang; Yau, Horng-Tzer (2019-09-17). "Dynamics of Deep Neural Networks and Neural Tangent Hierarchy". arXiv:1909.08156.
12. Cho, Youngmin; Saul, Lawrence K. (2009), Bengio, Y.; Schuurmans, D.; Lafferty, J. D.; Williams, C. K. I. (eds.), "Kernel Methods for Deep Learning" (PDF), Advances in Neural Information Processing Systems 22, Curran Associates, Inc., pp. 342–350, retrieved 2019-11-27
13. Daniely, Amit; Frostig, Roy; Singer, Yoram (2016), Lee, D. D.; Sugiyama, M.; Luxburg, U. V.; Guyon, I. (eds.), "Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity" (PDF), Advances in Neural Information Processing Systems 29, Curran Associates, Inc., pp. 2253–2261, arXiv:1602.05897, Bibcode:2016arXiv160205897D, retrieved 2019-11-27
14. Lee, Jaehoon; Bahri, Yasaman; Novak, Roman; Schoenholz, Samuel S.; Pennington, Jeffrey; Sohl-Dickstein, Jascha (2018-02-15). "Deep Neural Networks as Gaussian Processes". Cite journal requires |journal= (help)
15. Yang, Greg (2019-02-13). "Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation". arXiv:1902.04760 [cs.NE].
16. Hron, Jiri; Bahri, Yasaman; Sohl-Dickstein, Jascha; Novak, Roman (2020-06-18). "Infinite attention: NNGP and NTK for deep attention networks". International Conference on Machine Learning. 2020. arXiv:2006.10540. Bibcode:2020arXiv200610540H.
17. Allen-Zhu, Zeyuan; Li, Yuanzhi; Song, Zhao (2018-10-29). "On the convergence rate of training recurrent neural networks". NeurIPS. arXiv:1810.12065.
18. Novak, Roman; Xiao, Lechao; Hron, Jiri; Lee, Jaehoon; Alemi, Alexander A.; Sohl-Dickstein, Jascha; Schoenholz, Samuel S. (2019-12-05), "Neural Tangents: Fast and Easy Infinite Neural Networks in Python", International Conference on Learning Representations (ICLR), 2020, arXiv:1912.02803, Bibcode:2019arXiv191202803N