Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Recurrent neural networks (RNNs) are difficult to train on sequence processing tasks, not only because input noise may be amplified through feedback, but also because any inaccuracy in the weights has similar consequences as input noise.
We describe a method for denoising the hidden state during training to achieve more robust representations thereby improving generalization performance.
Attractor dynamics are incorporated into the hidden state to `clean up' representations at each step of a sequence.
The attractor dynamics are trained through an auxillary denoising loss to recover previously experienced hidden states from noisy versions of those states.
This state-denoised recurrent neural network (SDRNN) performs multiple steps of internal processing for each external sequence step.
On a range of tasks, we show that the SDRNN outperforms a generic RNN as well as a variant of the SDRNN with attractor dynamics on the hidden state but without the auxillary loss.
We argue that attractor dynamics---and corresponding connectivity constraints---are an essential component of the deep learning arsenal and should be invoked not only for recurrent networks but also for improving deep feedforward nets and intertask transfer.
Noise robustness is a fundamental challenge for every information processing system.
A traditional approach to handling noise is to design preprocessing stages that estimate and filter noise from an input signal BID5 .
More recently in machine learning, loss functions have been explored to achieve invariance to task-irrelevant perturbations in the input BID18 BID22 .
Although such methods are suitable for handling external noise, we argue in this article that internal noise can be an even greater challenge.
To explain what we mean by internal noise, consider a deep-net architecture in which representations are transformed from one hidden layer to the next.
To the degree that the network weights are not precisely tuned to extract only the critical features of the domain, irrelevant features may be selected and may potentially interfere with subsequent processing.
Recurrent architectures are particularly fragile because internal-state dynamics can amplify noise over the course of sequence processing (Anonymous, 1994) .
In this article, we propose a suppression method that improves the generalization performance of deep nets.
We focus on recurrent nets, where the potential benefits are most significant.Our approach draws inspiration from the human brain, a notably robust and flexible information processing system.
Although the brain operates in an extremely high-dimensional continuous state space, the conscious mind categorizes and interprets the world via language.
We argue that categorization and interpretation processes serve to suppress noise and increase the reliability of behavior.
Categorization treats instances that vary in incidental ways as identical: a chair is something you can sit on regardless of whether it is made of wood or metal.
Language plays a similar role in cognition.
For example, BID21 demonstrated that color terms of one's native language influence a perceptual-judgment task-selecting a color patch that best matches a reference patch.
The linguistic label assigned to a patch is the basis of matching, not the low-level perceptual data.Suppressing variability facilitates communication in information-processing systems.
Whether we are considering groups of humans, regions within the brain, components of an articulated neural net architecture, or layers of a feedforward network, information must be communicated from a sender to a receiver.
To ensure that the receiver interprets a message as intended, the sender should limit its messages to a canonical form that the receiver has interpreted successfully in the past.
Language serves this role in inter-human communication.
We explore noise suppression methods to serve this role in intra-network communication in deep-learning models.In the next section, we describe a recurrent neural network architecture for cleaning up noisy representations-an attractor net.
We then propose integrating attractor networks into deep networks, specifically sequence-processing networks, for denoising internal states.
We present a series of experiments on small-scale problems to illustrate the methodology and analyze its benefits, followed by experiments on more naturalistic problems which also show reliable and sometimes substantial benefits of denoising, particularly in data-limited situations.
We postpone a discussion of related work until we present the technical content of our work, which should facilitate a comparison.
Noise robustness is a highly desirable property in neural networks.
When a neural network performs well, it naturally exhibits a sort of noise suppression: activation in a layer is relatively invariant to noise injected at lower layers BID1 .
We developed a recurrent net architecture with an explicit objective of attaining noise robustness, regardless of whether the noise is in the inputs, output labels, or the model parameters.
The notion of using attractor dynamics to ensure representations are well-formed in the context of the task at hand is quite general, and we believe that attractor networks should prove useful in deep feedforward nets as well as in recurrent nets.
In fact, many of the deepest vision nets might well be reconceptualized as approximating attractor dynamics.
For example, we are currently investigating the use of convolutional attractor networks for image transformations such as denoising, superresolution, and coloring BID19 .
Successfully transforming images requires satisfying many simultaneous constraints which are elegantly and compactly expressed in a Lyapunov (energy) function and can be approximated by deep feedforward nets.
A denoising approach may also be particularly effective for transfer learning.
In transfer learning, representations from one domain are applied to another, quite analogous to our SDRNN which leverages representations from one training epoch to support the next.
We propose a mechanism for denoising the internal state of an RNN to improve generalization performance.