Learning to Forget: Continual Prediction with LSTM

Gers, Felix A.; Schmidhuber, Jürgen; Cummins, Fred

doi:10.1162/089976600300015015

Cited by 4,503 publications

(2,575 citation statements)

References 13 publications

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“…LSTM had more successful runs, and learns much faster, than real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking. However, Gers et al [7] identified a weakness in LSTM networks processing continual input streams that were not a priori segmented into subsequences with explicitly marked ends where the internal state of the network could be reset. Without resets, the state could grow indefinitely and eventually cause the network to break down.…”

Section: Recurrent Neural Network (Rnns)mentioning

confidence: 99%

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

2018

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This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A diagram of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results.Keywords: artificial intelligence; smart machine tools; learning algorithms; intelligent manufacturing; fault diagnosis and prognosis Brief IntroductionWe believe that a new epoch of the "Industrial Internet of Things (IIoT) plus artificial intelligence (AI)", characterized by big machinery data, data-driven techniques, ubiquitous networks, mass innovation, automatic intelligence, cross-border integration, and shared services, has arrived [1-3]. The fast development and combination of new AI and energy technologies, for materials, bioscience, the Internet, and new-generation information exchange, is a fundamental part of this new epoch. This will, in turn, permit game-changing transformation of models, ecosystems, and means in the light of their application to national security, well-being, and the economy. The main objective is a review and summary of recent achievement in data-based techniques, especially for complicated industrial applications, offering reference for further study from both an academic and practical point of view. Yin et al.[1] describes a brief evolutionary overview of data-based techniques over the last two decades. Recent development of modern industrial applications is presented mainly from the perspectives of monitoring and control. Their methodology, based on process measurements and model-data integrated techniques, will be introduced in the next study. Jeschke et al. [2] developed the core system science needed to enable the development of complex IIoT/manufacturing cyber-physical systems (CPS). Moreover, readers can learn the current state of IIoT and the concept of cybermanufacturing from this book. In 2014, Lund et al. [3] described the central issues contributing to, and characterizing, the worldwide and regional growth of the IoT. Besides, researchers can utilize the trend analysis of IoT their region markets in the future.There are many AI algorithms for machine health monitoring and other machine tool applications: The second-order recurrent neural networks (RNN) method for the learning and extraction of finite

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Section: Recurrent Neural Network (Rnns)mentioning

confidence: 99%

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

2018

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“…The input, output and forget gates are connected via "peepholes". For a full specification of the LSTM model we refer to (Hochreiter & Schmidhuber, 1997) and (Gers et al, 2000).…”

Section: Long Short-term Memorymentioning

confidence: 99%

Generating Time: Rhythmic Perception, Prediction and Production with Recurrent Neural Networks

Elmsley

Weyde

Armstrong

2017

Journal of Creative Music Systems

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In the quest for a convincing musical agent that performs in real time alongside human performers, the issues surrounding expressively timed rhythm must be addressed. Current beat tracking methods are not sufficient to follow rhythms automatically when dealing with varying tempo and expressive timing. In the generation of rhythm, some existing interactive systems ignore the pulse entirely, or fix a tempo after some time spent listening to input. Since music unfolds in time, we take the view that musical timing needs to be at the core of a music generation system. Our research explores a connectionist machine learning approach to expressive rhythm generation, based on cognitive and neurological models. Two neural network models are combined within one integrated system. A Gradient Frequency Neural Network (GFNN) models the perception of periodicities by resonating nonlinearly with the musical input, creating a hierarchy of strong and weak oscillations that relate to the metrical structure. A Long Short-term Memory Recurrent Neural Network (LSTM) models longer-term temporal relations based on the GFNN output.The output of the system is a prediction of when in time the next rhythmic event is likely to occur. These predictions can be used to produce new rhythms, forming a generative model.We have trained the system on a dataset of expressively performed piano solos and evaluated its ability to accurately predict rhythmic events. Based on the encouraging results, we conclude that the GFNN-LSTM model has great potential to add the ability to follow and generate expressive rhythmic structures to real-time interactive systems.

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“…2): one representing a cortical area (frontal or parietal) that learns the environment via unsupervised learning mechanisms, and one representing the basal ganglia and the dopaminergic system responsible for upcoming reward estimates and reward estimation errors. The cortical area was modeled using LSTM networks (Hochreiter and Schmidhuber 1997;Gers et al 2000;Gers et al 2002) to learn its environment. LSTM is a general neural network learning algorithm used in a wide range of machine learning applications (Eck and Schmidhuber 2002;Bakker 2002) that implements working memory in an intuitive way using gated recurrent loop mechanisms.…”

Section: The Modelmentioning

confidence: 99%

“…2 and the signal is fed back (as part of the gradient of the error function) into the LSTM for weight updates. The model uses the full form of the LSTM network that can be found in (Hochreiter and Schmidhuber 1997;Gers et al 2000;Gers et al 2002). An LSTM network consists of a set of inputs, memory neurons (called state cells in the LSTM literature), gates and a bank of outputs.…”

Section: Lstm Model Of the Cortexmentioning

confidence: 99%

“…To update the weights and minimize the LSTM prediction error, an estimator of the gradient of this error with respect to the memory block weights must be computed. The mathematical details of this gradient are beyond the scope of this paper, but can be found in (Hochreiter and Schmidhuber 1997;Gers et al 2000;Gers et al 2002). We used a learning rate of α LSTM = 0.5 and an eligibility trace-decay parameter of l LSTM =0.8.…”

Section: Lstm Model Of the Cortexmentioning

confidence: 99%

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Alternative time representation in dopamine models

2009

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Dopaminergic neuron activity has been modeled during learning and appetitive behavior, most commonly using the temporal-difference (TD) algorithm. However, a proper representation of elapsed time and of the exact task is usually required for the model to work. Most models use timing elements such as delay-line representations of time that are not biologically realistic for intervals in the range of seconds. The interval-timing literature provides several alternatives. One of them is that timing could emerge from general network dynamics, instead of coming from a dedicated circuit. Here, we present a general rate-based learning model based on long short-term memory (LSTM) networks that learns a time representation when needed. Using a naïve network learning its environment in conjunction with TD, we reproduce dopamine activity in appetitive trace conditioning with a constant CS-US interval, including probe trials with unexpected delays. The proposed model learns a representation of the environment dynamics in an adaptive biologically plausible framework, without recourse to delay lines or other special-purpose circuits. Instead, the model predicts that the task-dependent representation of time is learned by experience, is encoded in ramp-like changes in single-neuron activity distributed across small neural networks, and reflects a temporal integration mechanism resulting from the inherent dynamics of recurrent loops within the network. The model also reproduces the known finding that trace conditioning is more difficult than delay conditioning and that the learned representation of the task can be highly dependent on the types of trials experienced during training. Finally, it suggests that the phasic dopaminergic signal could facilitate learning in the cortex.

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Learning to Forget: Continual Prediction with LSTM

Cited by 4,503 publications

References 13 publications

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

Generating Time: Rhythmic Perception, Prediction and Production with Recurrent Neural Networks

Alternative time representation in dopamine models

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