Learning Polynomials with Neural Networks

Alexandr Andoni, Rina Panigrahy, Gregory Valiant, Li Zhang
; Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):1908-1916, 2014.

Abstract

We study the effectiveness of learning low degree polynomials using neural networks by the gradient descent method. While neural networks have been shown to have great expressive power, and gradient descent has been widely used in practice for learning neural networks, few theoretical guarantees are known for such methods. In particular, it is well known that gradient descent can get stuck at local minima, even for simple classes of target functions. In this paper, we present several positive theoretical results to support the effectiveness of neural networks. We focus on two-layer neural networks (i.e. one hidden layer) where the top layer node is a linear function, similar to \citebarron93. First we show that for a randomly initialized neural network with sufficiently many hidden units, the gradient descent method can learn any low degree polynomial. Secondly, we show that if we use complex-valued weights (the target function can still be real), then under suitable conditions, there are no “robust local minima”: the neural network can always escape a local minimum by performing a random perturbation. This property does not hold for real-valued weights. Thirdly, we discuss whether sparse polynomials can be learned with \emphsmall neural networks, where the size is dependent on the sparsity of the target function.

Cite this Paper


BibTeX
@InProceedings{pmlr-v32-andoni14, title = {Learning Polynomials with Neural Networks}, author = {Alexandr Andoni and Rina Panigrahy and Gregory Valiant and Li Zhang}, booktitle = {Proceedings of the 31st International Conference on Machine Learning}, pages = {1908--1916}, year = {2014}, editor = {Eric P. Xing and Tony Jebara}, volume = {32}, number = {2}, series = {Proceedings of Machine Learning Research}, address = {Bejing, China}, month = {22--24 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v32/andoni14.pdf}, url = {http://proceedings.mlr.press/v32/andoni14.html}, abstract = {We study the effectiveness of learning low degree polynomials using neural networks by the gradient descent method. While neural networks have been shown to have great expressive power, and gradient descent has been widely used in practice for learning neural networks, few theoretical guarantees are known for such methods. In particular, it is well known that gradient descent can get stuck at local minima, even for simple classes of target functions. In this paper, we present several positive theoretical results to support the effectiveness of neural networks. We focus on two-layer neural networks (i.e. one hidden layer) where the top layer node is a linear function, similar to \citebarron93. First we show that for a randomly initialized neural network with sufficiently many hidden units, the gradient descent method can learn any low degree polynomial. Secondly, we show that if we use complex-valued weights (the target function can still be real), then under suitable conditions, there are no “robust local minima”: the neural network can always escape a local minimum by performing a random perturbation. This property does not hold for real-valued weights. Thirdly, we discuss whether sparse polynomials can be learned with \emphsmall neural networks, where the size is dependent on the sparsity of the target function.} }
Endnote
%0 Conference Paper %T Learning Polynomials with Neural Networks %A Alexandr Andoni %A Rina Panigrahy %A Gregory Valiant %A Li Zhang %B Proceedings of the 31st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2014 %E Eric P. Xing %E Tony Jebara %F pmlr-v32-andoni14 %I PMLR %J Proceedings of Machine Learning Research %P 1908--1916 %U http://proceedings.mlr.press %V 32 %N 2 %W PMLR %X We study the effectiveness of learning low degree polynomials using neural networks by the gradient descent method. While neural networks have been shown to have great expressive power, and gradient descent has been widely used in practice for learning neural networks, few theoretical guarantees are known for such methods. In particular, it is well known that gradient descent can get stuck at local minima, even for simple classes of target functions. In this paper, we present several positive theoretical results to support the effectiveness of neural networks. We focus on two-layer neural networks (i.e. one hidden layer) where the top layer node is a linear function, similar to \citebarron93. First we show that for a randomly initialized neural network with sufficiently many hidden units, the gradient descent method can learn any low degree polynomial. Secondly, we show that if we use complex-valued weights (the target function can still be real), then under suitable conditions, there are no “robust local minima”: the neural network can always escape a local minimum by performing a random perturbation. This property does not hold for real-valued weights. Thirdly, we discuss whether sparse polynomials can be learned with \emphsmall neural networks, where the size is dependent on the sparsity of the target function.
RIS
TY - CPAPER TI - Learning Polynomials with Neural Networks AU - Alexandr Andoni AU - Rina Panigrahy AU - Gregory Valiant AU - Li Zhang BT - Proceedings of the 31st International Conference on Machine Learning PY - 2014/01/27 DA - 2014/01/27 ED - Eric P. Xing ED - Tony Jebara ID - pmlr-v32-andoni14 PB - PMLR SP - 1908 DP - PMLR EP - 1916 L1 - http://proceedings.mlr.press/v32/andoni14.pdf UR - http://proceedings.mlr.press/v32/andoni14.html AB - We study the effectiveness of learning low degree polynomials using neural networks by the gradient descent method. While neural networks have been shown to have great expressive power, and gradient descent has been widely used in practice for learning neural networks, few theoretical guarantees are known for such methods. In particular, it is well known that gradient descent can get stuck at local minima, even for simple classes of target functions. In this paper, we present several positive theoretical results to support the effectiveness of neural networks. We focus on two-layer neural networks (i.e. one hidden layer) where the top layer node is a linear function, similar to \citebarron93. First we show that for a randomly initialized neural network with sufficiently many hidden units, the gradient descent method can learn any low degree polynomial. Secondly, we show that if we use complex-valued weights (the target function can still be real), then under suitable conditions, there are no “robust local minima”: the neural network can always escape a local minimum by performing a random perturbation. This property does not hold for real-valued weights. Thirdly, we discuss whether sparse polynomials can be learned with \emphsmall neural networks, where the size is dependent on the sparsity of the target function. ER -
APA
Andoni, A., Panigrahy, R., Valiant, G. & Zhang, L.. (2014). Learning Polynomials with Neural Networks. Proceedings of the 31st International Conference on Machine Learning, in PMLR 32(2):1908-1916

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