On the difficulty of training Recurrent Neural Networks

On the difficulty of training Recurrent Neural Networks

16 Feb 2013 | Razvan Pascanu, Tomas Mikolov, Yoshua Bengio
The paper "On the difficulty of training Recurrent Neural Networks" by Razvan Pascanu addresses the challenges of training Recurrent Neural Networks (RNNs), particularly focusing on the *vanishing gradient* and *exploding gradient* problems. The author explores these issues from analytical, geometric, and dynamical systems perspectives to propose effective solutions. The paper introduces a gradient norm clipping strategy to handle exploding gradients and a soft constraint to address vanishing gradients. Empirical validation is provided to support the proposed solutions. The analysis reveals that the vanishing gradient problem occurs when the long-term components of the gradient decay to zero, while the exploding gradient problem arises when these components grow exponentially. The paper also discusses the connection between these issues and bifurcation points in dynamical systems, suggesting that crossing these points can lead to large changes in the network's state. The proposed solutions, including gradient clipping and a regularization term, are shown to improve performance on both synthetic and natural tasks, such as polyphonic music prediction and language modeling.The paper "On the difficulty of training Recurrent Neural Networks" by Razvan Pascanu addresses the challenges of training Recurrent Neural Networks (RNNs), particularly focusing on the *vanishing gradient* and *exploding gradient* problems. The author explores these issues from analytical, geometric, and dynamical systems perspectives to propose effective solutions. The paper introduces a gradient norm clipping strategy to handle exploding gradients and a soft constraint to address vanishing gradients. Empirical validation is provided to support the proposed solutions. The analysis reveals that the vanishing gradient problem occurs when the long-term components of the gradient decay to zero, while the exploding gradient problem arises when these components grow exponentially. The paper also discusses the connection between these issues and bifurcation points in dynamical systems, suggesting that crossing these points can lead to large changes in the network's state. The proposed solutions, including gradient clipping and a regularization term, are shown to improve performance on both synthetic and natural tasks, such as polyphonic music prediction and language modeling.
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