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Neural Associative Memory for Dual-Sequence Modeling
[article]
2016
arXiv
pre-print
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In this work we propose a new architecture for dual-sequence modeling that is based on associative memory. ...
We derive AM-RNNs, a recurrent associative memory (AM) which augments generic recurrent neural networks (RNN). This architecture is extended to the Dual AM-RNN which operates on two AMs at once. ...
Acknowledgments We thank Sebastian Krause, Tim Rocktäschel and Leonhard Hennig for comments on an early draft of this work. ...
arXiv:1606.03864v2
fatcat:udc76ezugjcozktjs7llsbpq6a
Neural Associative Memory for Dual-Sequence Modeling
2016
Proceedings of the 1st Workshop on Representation Learning for NLP
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In this work we propose a new architecture for dual-sequence modeling that is based on associative memory. ...
We derive AM-RNNs, a recurrent associative memory (AM) which augments generic recurrent neural networks (RNN). This architecture is extended to the Dual AM-RNN which operates on two AMs at once. ...
Acknowledgments We thank Sebastian Krause, Tim Rocktäschel and Leonhard Hennig for comments on an early draft of this work. ...
doi:10.18653/v1/w16-1630
dblp:conf/rep4nlp/Weissenborn16
fatcat:7ii2on72bfh6pp7owiojf4xvhi
Dual-Network Memory Model For Temporal Sequences
2014
Zenodo
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We have already proposed a biologically inspired dual-network memory model which can much reduce this forgetting for static patterns. ...
The computer simulation results show the effectiveness of the proposed dual-network memory model. ...
NOVEL DUAL-NETWORK MEMORY MODEL FOR TEMPORAL SEQUENCES Fig. 2 shows the structure of the proposed dual-network memory model. ...
doi:10.5281/zenodo.1336104
fatcat:5k7isd2qafh4lmummkemenbwzq
Research on Ship Motion Prediction Algorithm Based on Dual-pass Long Short-Term Memory Neural Network
2021
IEEE Access
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In response to this problem, a novel type of dual-pass Long Short-Term Memory (LSTM) neural network architecture is developed, on the basis of regular LSTM neural network. ...
INDEX TERMS Dual-pass LSTM neural network, generalized autoregressive conditional heteroskedasticity (GARCH) model, inertial navigation system (INS). ...
GARCH MODEL AND DUAL-PASS LSTM NEURAL NETWORK APPROXIMATION Generalized conditional heteroscedasticity (GARCH) is an autoregressive conditional heteroscedasticity model for the variance of time series ...
doi:10.1109/access.2021.3055253
fatcat:3nuscmei7veybcxvmqxjd7fgue
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
[article]
2018
Frontiers in Neurorobotics
accepted
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In this paper, we propose a dual-memory self-organizing architecture for lifelong learning scenarios. ...
For the consolidation of knowledge in the absence of external sensory input, the episodic memory periodically replays trajectories of neural reactivations. ...
The authors would like to thank Pablo Barros and Vadym Gryshchuk for technical support. ...
doi:10.3389/fnbot.2018.00078
pmid:30546302
pmcid:PMC6279894
arXiv:1805.10966v4
fatcat:ou5sjdf6gbfzrjx5vojuyrnuca
DeepProcess: Supporting business process execution using a MANN-based recommender system
[article]
2021
arXiv
pre-print
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2018 pre-print arXiv:1802.00938v1 fatcat:mv4tmv7lwnfqnes5gobkhinnwq
We propose a novel network architecture, namely Write-Protected Dual Controller Memory-Augmented Neural Network (DCw-MANN), for building prescriptive models. ...
Based on recent advances in the field of deep learning, we present a novel memory-augmented neural network (MANN) based approach for constructing a process-aware recommender system. ...
Fig. 1 . 1 Recurrent Neural Network and Long Short Term Memory
Fig. 2 . 2 The internal structure of LSTM
Fig. 3 . 3 Write-Protected Dual Controller Memory Augmented Neural Network Model operations ...
arXiv:1802.00938v2
fatcat:3besbszopnetljtk4553iet5gq
Memory and attention in deep learning
[article]
2021
arXiv
pre-print
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Turing Machine via Neural Stored-program Memory-a new kind of external memory for neural networks. ...
Its contributions include: (i) presenting a collection of taxonomies for memory, (ii) constructing new memory-augmented neural networks (MANNs) that support multiple control and memory units, (iii) introducing ...
our first contributions: Dual Controller Write-Protected Memory Augmented Neural Network (DCw-MANN), an extension of MANN to model sequence to sequence mapping, and Dual Memory Neural Computer (DMNC) ...
arXiv:2107.01390v1
fatcat:nxxjns7qdfb2fhe53qwwylagui
Dual coding in an auto-associative network model of the hippocampus
2009
BMC Neuroscience
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Here we present a novel spiking neural network model which is, to our knowledge, the first to use a dual coding system in order to learn and recall associations between both temporally coded (spatial) ...
Methods The postulated function of the hippocampus in spatial and episodic memory has been widely and successfully modelled using auto-associative networks [6] . ...
doi:10.1186/1471-2202-10-s1-o7
fatcat:bsztrs363nd6dndx5mrg52rvxq
Dual Coding in an Auto-associative Network Model of the Hippocampus
2009
Frontiers in Computational Neuroscience
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Here we present a novel spiking neural network model which is, to our knowledge, the first to use a dual coding system in order to learn and recall associations between both temporally coded (spatial) ...
Methods The postulated function of the hippocampus in spatial and episodic memory has been widely and successfully modelled using auto-associative networks [6] . ...
doi:10.3389/conf.neuro.10.2009.14.094
fatcat:pq52belhk5f5tidzmcmaj4yqjm
A cognitive framework for explaining serial processing and sequence execution strategies
2014
Psychonomic Bulletin & Review
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We argue that C-SMB accounts for cognitive models developed for a range of sequential motor tasks (like those proposed by Keele et al.). ...
On the basis of this framework, we present a classification of the sequence execution strategies that helps researchers to better understand the cognitive and neural underpinnings of serial movement behavior ...
In conclusion, studies with discrete keying sequences have been explained by the Dual Processor Model (Verwey, 2001) . Like the Dual Processor Model, C-SMB can account for discrete keying sequences. ...
doi:10.3758/s13423-014-0773-4
pmid:25421407
fatcat:zk5zxvhgfnewdeb65vfrdkh7o4
Within the framework of the dual-system model, voluntary action is central to cognition
2019
Attention, Perception & Psychophysics
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However, unlike in previous dual-system models, human cognitive activity involved in task performance is not exclusively associated with one system or the other. ...
Furthermore, within the model, the computational competencies of the two systems are used to construct purposeful sequences of actions-that is, skills. ...
Comparison with earlier dual-system models In earlier versions of the dual-system model, the habit system was associated solely with procedural learning and implicit effects on performance. ...
doi:10.3758/s13414-019-01737-0
pmid:31062301
fatcat:55djnjfjefdsrdw7pp4zdlog6a
Adaptive Value Normalization in the Prefrontal Cortex Is Reduced by Memory Load
2017
eNeuro
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Formal model comparison showed that a divisive normalization model fitted effects of both risk context and working memory demands on PFC activity better than alternative models of value adaptation, and ...
Adaptation facilitates neural representation of a wide range of diverse inputs, including reward values. ...
Acknowledgments: We thank all participants for participation. ...
doi:10.1523/eneuro.0365-17.2017
pmid:28462394
pmcid:PMC5409984
fatcat:2dkov2rnsjhexchgi5iiw3h2xe
Page 3408 of Psychological Abstracts Vol. 92, Issue 10
[page]
2005
Psychological Abstracts
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Implications for dual-process mod- els of memory in old age are discussed. —Journal abstract
28323. Hoffman, Joachim & Sebald, Albrecht. ...
(De- partment of Psychology, Claremont Graduate University, CA) Dual-Pro- cess Models of Associative Recognition in Young and Older Adults: Evidence From Receiver Operating Characteristics. ...
Dissociable neural systems of sequence learning
2012
Advances in Cognitive Psychology
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sequence learning, neural systems, hippocampus, basal ganglia, serial reaction time task, artificial grammar task Although current theories all point to distinct neural systems for sequence learning, no ...
reviews these two distinctions, yet concludes that these traditional dichotomies prove insufficient to account for all data on sequence learning and its neural organization. instead, a broader theoretical ...
Ultimately, this model is of great interest since it highly relates with general principles of associative learning and memory (O'Reilly & Rudy, 2001) . ...
doi:10.5709/acp-0105-1
fatcat:kkzrphsu7jhgrijqz2dqsoy3pm
Dissociable neural systems of sequence learning
2012
Advances in Cognitive Psychology
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Although current theories all point to distinct neural systems for sequence learning, no consensus has been reached on which factors crucially define this distinction. ...
This paper reviews these two distinctions, yet concludes that these traditional dichotomies prove insufficient to account for all data on sequence learning and its neural organization. ...
Ultimately, this model is of great interest since it highly relates with general principles of associative learning and memory (O'Reilly & Rudy, 2001) . ...
doi:10.2478/v10053-008-0105-1
pmid:22679463
pmcid:PMC3367868
fatcat:jvwmof3ni5ffvdnu6bglioi4ee
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