110,076 research outputs found
From Imitation to Prediction, Data Compression vs Recurrent Neural Networks for Natural Language Processing
In recent studies [1][13][12] Recurrent Neural Networks were used for
generative processes and their surprising performance can be explained by their
ability to create good predictions. In addition, data compression is also based
on predictions. What the problem comes down to is whether a data compressor
could be used to perform as well as recurrent neural networks in natural
language processing tasks. If this is possible,then the problem comes down to
determining if a compression algorithm is even more intelligent than a neural
network in specific tasks related to human language. In our journey we
discovered what we think is the fundamental difference between a Data
Compression Algorithm and a Recurrent Neural Network
Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks
Quantum systems interacting with an unknown environment are notoriously
difficult to model, especially in presence of non-Markovian and
non-perturbative effects. Here we introduce a neural network based approach,
which has the mathematical simplicity of the
Gorini-Kossakowski-Sudarshan-Lindblad master equation, but is able to model
non-Markovian effects in different regimes. This is achieved by using recurrent
neural networks for defining Lindblad operators that can keep track of memory
effects. Building upon this framework, we also introduce a neural network
architecture that is able to reproduce the entire quantum evolution, given an
initial state. As an application we study how to train these models for quantum
process tomography, showing that recurrent neural networks are accurate over
different times and regimes.Comment: 10 pages, 8 figure
Lateral inhibition: inherent recurrent processes in coherent optical propagation
Processes that are analogous to the neural process of recurrent lateral inhibition can be found in optical systems that consist of a shift-invariant system and a Fabry-Perot cavity. The properties of the optical recurrent system are derived and demonstrated by computer simulation. The simulation shows that optical lateral inhibition can be used to enhance the outline of an amplitude object and to make phase-only objects directly detectable and visible. The optical recurrent system is compared with frequency-plane spatial filtering. Requirements and practical limitations for the design of an optical recurrent system are also discussed
Modelling and control of chaotic processes through their Bifurcation Diagrams generated with the help of Recurrent Neural Network models: Part 1—simulation studies
Many real-world processes tend to be chaotic and also do not lead to satisfactory analytical modelling. It has been shown here that for such chaotic processes represented through short chaotic noisy time-series, a multi-input and multi-output recurrent neural networks model can be built which is capable of capturing the process trends and predicting the future values from any given starting condition. It is further shown that this capability can be achieved by the Recurrent Neural Network model when it is trained to very low value of mean squared error. Such a model can then be used for constructing the Bifurcation Diagram of the process leading to determination of desirable operating conditions. Further, this multi-input and multi-output model makes the process accessible for control using open-loop/closed-loop approaches or bifurcation control etc. All these studies have been carried out using a low dimensional discrete chaotic system of Hénon Map as a representative of some real-world processes
Bidirectional truncated recurrent neural networks for efficient speech denoising
We propose a bidirectional truncated recurrent neural network architecture for speech denoising. Recent work showed that deep recurrent neural networks perform well at speech denoising tasks and outperform feed forward architectures [1]. However, recurrent neural networks are difficult to train and their simulation does not allow for much parallelization. Given the increasing availability of parallel computing architectures like GPUs this is disadvantageous. The architecture we propose aims to retain the positive properties of recurrent neural networks and deep learning while remaining highly parallelizable. Unlike a standard recurrent neural network, it processes information from both past and future time steps. We evaluate two variants of this architecture on the Aurora2 task for robust ASR where they show promising results. The models outperform the ETSI2 advanced front end and the SPLICE algorithm under matching noise conditions.We propose a bidirectional truncated recurrent neural network architecture for speech denoising. Recent work showed that deep recurrent neural networks perform well at speech denoising tasks and outperform feed forward architectures [1]. However, recurrent neural networks are difficult to train and their simulation does not allow for much parallelization. Given the increasing availability of parallel computing architectures like GPUs this is disadvantageous. The architecture we propose aims to retain the positive properties of recurrent neural networks and deep learning while remaining highly parallelizable. Unlike a standard recurrent neural network, it processes information from both past and future time steps. We evaluate two variants of this architecture on the Aurora2 task for robust ASR where they show promising results. The models outperform the ETSI2 advanced front end and the SPLICE algorithm under matching noise conditions.P
Predictive-State Decoders: Encoding the Future into Recurrent Networks
Recurrent neural networks (RNNs) are a vital modeling technique that rely on
internal states learned indirectly by optimization of a supervised,
unsupervised, or reinforcement training loss. RNNs are used to model dynamic
processes that are characterized by underlying latent states whose form is
often unknown, precluding its analytic representation inside an RNN. In the
Predictive-State Representation (PSR) literature, latent state processes are
modeled by an internal state representation that directly models the
distribution of future observations, and most recent work in this area has
relied on explicitly representing and targeting sufficient statistics of this
probability distribution. We seek to combine the advantages of RNNs and PSRs by
augmenting existing state-of-the-art recurrent neural networks with
Predictive-State Decoders (PSDs), which add supervision to the network's
internal state representation to target predicting future observations.
Predictive-State Decoders are simple to implement and easily incorporated into
existing training pipelines via additional loss regularization. We demonstrate
the effectiveness of PSDs with experimental results in three different domains:
probabilistic filtering, Imitation Learning, and Reinforcement Learning. In
each, our method improves statistical performance of state-of-the-art recurrent
baselines and does so with fewer iterations and less data.Comment: NIPS 201
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