Designing deep neural networks is an art that often involves an expensive search
over candidate architectures. To overcome this for recurrent neural nets (RNNs), we
establish a connection between the hidden state dynamics in an RNN and gradient
descent (GD). We then integrate momentum into this framework and propose a new
family of RNNs, called MomentumRNNs. We theoretically prove and numerically
demonstrate that MomentumRNNs alleviate the vanishing gradient issue in training
RNNs. We study the momentum long-short term memory (MomentumLSTM) and
verify its advantages in convergence speed and accuracy over its LSTM counterpart
across a variety of benchmarks. We also demonstrate that MomentumRNN is
applicable to many types of recurrent cells, including those in the state-of-the-art orthogonal RNNs. Finally, we show that other advanced momentum-based
optimization methods, such as Adam and Nesterov accelerated gradients with
a restart, can be easily incorporated into the MomentumRNN framework for
designing new recurrent cells with even better performance.
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SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization
In this paper we consider the training stability of recurrent neural networks (RNNs) and propose a family of RNNs, namely SBO-RNN, that can be formulated using stochastic bilevel optimization (SBO). With the help of stochastic gradient descent (SGD), we manage to convert the SBO problem into an RNN where the feedforward and backpropagation solve the lower and upper-level optimization for learning hidden states and their hyperparameters, respectively. We prove that under mild conditions there is no vanishing or exploding gradient in training SBO-RNN. Empirically we demonstrate our approach with superior performance on several benchmark datasets, with fewer parameters, less training data, and much faster convergence. Code is available at https://zhang-vislab.github.io.
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- Award ID(s):
- 2021871
- NSF-PAR ID:
- 10341223
- Date Published:
- Journal Name:
- Thirty-fifth Conference on Neural Information Processing Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Designing deep neural networks is an art that often involves an expensive search over candidate architectures. To overcome this for recurrent neural nets (RNNs), we establish a connection between the hidden state dynamics in an RNN and gradient descent (GD). We then integrate momentum into this framework and propose a new family of RNNs, called {\em MomentumRNNs}. We theoretically prove and numerically demonstrate that MomentumRNNs alleviate the vanishing gradient issue in training RNNs. We study the momentum long-short term memory (MomentumLSTM) and verify its advantages in convergence speed and accuracy over its LSTM counterpart across a variety of benchmarks. We also demonstrate that MomentumRNN is applicable to many types of recurrent cells, including those in the state-of-the-art orthogonal RNNs. Finally, we show that other advanced momentum-based optimization methods, such as Adam and Nesterov accelerated gradients with a restart, can be easily incorporated into the MomentumRNN framework for designing new recurrent cells with even better performance.more » « less
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Accepted Manuscript1.0
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