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Neural Tangents: Fast and Easy Infinite Neural Networks in Python

arXiv 1912.02803 ↗First posted 5 Dec 2019stat.ML cs.LGcited by 149

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Abstract (arXiv)

Neural Tangents is a library designed to enable research into infinite-width neural networks. It provides a high-level API for specifying complex and hierarchical neural network architectures. These networks can then be trained and evaluated either at finite-width as usual or in their infinite-width limit. Infinite-width networks can be trained analytically using exact Bayesian inference or using gradient descent via the Neural Tangent Kernel. Additionally, Neural Tangents provides tools to study gradient descent training dynamics of wide but finite networks in either function space or weight space. The entire library runs out-of-the-box on CPU, GPU, or TPU. All computations can be automatically distributed over multiple accelerators with near-linear scaling in the number of devices. Neural Tangents is available at www.github.com/google/neural-tangents. We also provide an accompanying interactive Colab notebook.

Key facts. Cited by 149 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Dec 2019.

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Citing papers per year: 2020: 1; 2021: 38; 2022: 49; 2023: 20; 2024: 13; 2025: 17; 2026: 11.

149 papers.

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  1. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 608
  2. 2021Explaining Neural Scaling Lawscited by 363
  3. 2021Dataset Distillation with Infinitely Wide Convolutional Networkscited by 218
  4. 2020Spectral Bias and Task-Model Alignment Explain Generalization in Kernel Regression and Infinitely Wide Neural Networkscited by 166
  5. 2021Epistemic Neural Networkscited by 120
  6. 2022Efficient Dataset Distillation Using Random Feature Approximationcited by 111
  7. 2021Neural Networks as Kernel Learners: The Silent Alignment Effectcited by 106
  8. 2021Priors in Bayesian Deep Learning: A Reviewcited by 101
  9. 2021A Structured Dictionary Perspective on Implicit Neural Representationscited by 100
  10. 2021Provably efficient machine learning for quantum many-body problemscited by 91
  11. 2022More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalizecited by 91
  12. 2022Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matterscited by 76
38 more
  1. 2022Fast Finite Width Neural Tangent Kernelcited by 62
  2. 2021Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptationcited by 50
  3. 2021What can linearized neural networks actually say about generalization?cited by 49
  4. 2021Neural Contextual Bandits without Regretcited by 40
  5. 2022Second-order regression models exhibit progressive sharpening to the edge of stabilitycited by 40
  6. 2022Limitations of the NTK for Understanding Generalization in Deep Learningcited by 35
  7. 2021A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNscited by 33
  8. 2021Properties of the After Kernelcited by 27
  9. 2022NeuralEF: Deconstructing Kernels by Deep Neural Networkscited by 27
  10. 2021The Eigenlearning Framework: A Conservation Law Perspective on Kernel Regression and Wide Neural Networkscited by 26
  11. 2021Rethinking Influence Functions of Neural Networks in the Over-parameterized Regimecited by 26
  12. 2022Gradient-based Bi-level Optimization for Deep Learning: A Surveycited by 26
  13. 2021A Theory of Neural Tangent Kernel Alignment and Its Influence on Trainingcited by 25
  14. 2022Quantitative Gaussian Approximation of Randomly Initialized Deep Neural Networkscited by 25
  15. 2022Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernelscited by 25
  16. 2022The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimescited by 25
  17. 2022Neural Tangent Kernel: A Surveycited by 23
  18. 2021Batched Neural Banditscited by 22
  19. 2022Fast Rates for Noisy Interpolation Require Rethinking the Effects of Inductive Biascited by 22
  20. 2022Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel Regressioncited by 22
  21. 2023Improved machine learning algorithm for predicting ground state propertiescited by 22
  22. 2021Out-of-Distribution Generalization in Kernel Regressioncited by 21
  23. 2021A Neural Tangent Kernel Perspective of GANscited by 21
  24. 2021Uniform Generalization Bounds for Overparameterized Neural Networkscited by 21
  25. 2022Random matrix analysis of deep neural network weight matricescited by 21
  26. 2022What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?cited by 21
  27. 2023Regularizing Second-Order Influences for Continual Learningcited by 21
  28. 2022Intrinsic Neural Fields: Learning Functions on Manifoldscited by 20
  29. 2022Infinite Recommendation Networks: A Data-Centric Approachcited by 20
  30. 2023Phase diagram of early training dynamics in deep neural networks: effect of the learning rate, depth, and widthcited by 19
  31. 2022Fast Neural Kernel Embeddings for General Activationscited by 18
  32. 2022Sampling-based inference for large linear models, with application to linearised Laplacecited by 18
  33. 2023More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatorycited by 18
  34. 2024Emergent Equivariance in Deep Ensemblescited by 18
  35. 2021Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural Networkscited by 17
  36. 2021On the Equivalence between Neural Network and Support Vector Machinecited by 17
  37. 2021Functional Regularization for Reinforcement Learning via Learned Fourier Featurescited by 17
  38. 2021Eigenspace Restructuring: a Principle of Space and Frequency in Neural Networkscited by 17

50 most cited of 149 All 149 (JSON)