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Specifying and Interpreting Reinforcement Learning Policies through Simulatable Machine Learning
[article]
2022
arXiv
pre-print
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Finally, to close the loop on human-specification, we produce explanations of the final learned policy, in multiple modalities, to provide the user a final depiction about the learned policy of the agent ...
Human-AI collaborative policy synthesis is a procedure in which (1) a human initializes an autonomous agent's behavior, (2) Reinforcement Learning improves the human specified behavior, and (3) the agent ...
Policy Explanation User Study: Additional Details All participants were recruited on mechanical turk. ...
arXiv:2101.07140v3
fatcat:h5a2ltgz3vdllix4blr55uumxu
Bottom-up learning of explicit knowledge using a Bayesian algorithm and a new Hebbian learning rule
2011
Neural Networks
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In TELECAST, implicit processing is modeled using an unsupervised connectionist network (the Joint Probability EXtractor: JPEX) while explicit (causal) knowledge is implemented using a Bayesian belief ...
This phenomenon has recently received much attention in empirical research that was not accompanied by a corresponding work effort in cognitive modeling. ...
The CLARION model was composed of two feedforward connectionist networks. ...
doi:10.1016/j.neunet.2010.12.002
pmid:21239141
fatcat:slaaekszhbak7fznyf42k2ysbe
Deep Learning in Science
[article]
2020
arXiv
pre-print
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Therefore, we empirically assess how DL adoption relates to re-combinatorial novelty and scientific impact in the health sciences. ...
Much of the recent success of Artificial Intelligence (AI) has been spurred on by impressive achievements within a broader family of machine learning methods, commonly referred to as Deep Learning (DL) ...
of the empirical evidence on the nexus between re-combinatorial novelty and impact comes from bibliometric studies on scientific publications and patents. ...
arXiv:2009.01575v2
fatcat:4ttqgjdjfjbydp7flnhcgg5p7m
Implicit assumptions about implicit learning
1994
Behavioral and Brain Sciences
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The assumption that learning in some of these tasks (e.g., artificial grammar learning) is predominantly based on rule abstraction is questionable. ...
A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. ...
We would like to express our considerable gratitude to colleagues who have commented on this work, especially Axel ...
doi:10.1017/s0140525x00035159
fatcat:fqtxatogcnbdndgdn7vujkausq
Word learning as Bayesian inference
2007
Psychological review
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The main alternatives to hypothesis elimination are based on some form of associative learning, such as connectionist networks (using internal layers of "hidden" units and appropriately designed input ...
While both hypothesis elimination and associative learning models offer certain important insights, we will argue that neither approach provides an adequate framework for explaining how people learn the ...
Author Note The authors contributed equally to this work and they are listed in order of birth. This ...
doi:10.1037/0033-295x.114.2.245
pmid:17500627
fatcat:duhdlg5cbfa2tfjnxyjqezqjfq
Deep Learning for Text Style Transfer: A Survey
[article]
2021
arXiv
pre-print
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Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others. ...
It has a long history in the field of natural language processing, and recently has re-gained significant attention thanks to the promising performance brought by deep neural models. ...
In Proceedings of the
learning style instance supported latent 2018 Conference on Empirical Methods in
space. ...
arXiv:2011.00416v5
fatcat:wfw3jfh2mjfupbzrmnztsqy4ny
A neurocomputational account of taxonomic responding and fast mapping in early word learning
2010
Psychological review
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The model demonstrates how an established constraint on lexical learning which has often been regarded as domain-specific can emerge from domain-general learning principles that are simultaneously biologically ...
We present a neuro-computational model using self-organising maps that accounts for the emergence of taxonomic responding and fast mapping in early word learning, as well as a rapid increase in the rate ...
Classical backpropagation networks need constant supervision provided by an error-correction learning algorithm in order to generate meaningful patterns of generalisation. ...
doi:10.1037/a0018130
pmid:20063962
fatcat:gqjmauc4hvgm5kfr2jfz4hbo64
Spatial Learning and Action Planning in a Prefrontal Cortical Network Model
2011
PLoS Computational Biology
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Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. ...
We model a prefrontal network mediating distributed information processing for spatial learning and action planning. ...
Adding borders or barriers would result in the ''recruitment'' of new C 2 units preferentially active on either one side or the other of the barriers. ...
doi:10.1371/journal.pcbi.1002045
pmid:21625569
pmcid:PMC3098199
fatcat:7gaw35j4jjg3hmetpv4vlyduiu
The exploitation of distributional information in syllable processing
2004
Journal of Neurolinguistics
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We then analyze the ability of computational models to account for these results, successively considering a connectionist model based on the automatic computation of statistical regularities (SRN) [Cogn ...
In order to address this issue, we first propose an overview of some basic measures of association, going from the simple co-occurrence frequency to the normative measure of contingency, r w : We then ...
Acknowledgements We would like to thank Padraic Monoghan and Zoltan Dienes for their valuable comments on an earlier version of this manuscript, and Aurelien Perruchet for his contribution to Appendix ...
doi:10.1016/s0911-6044(03)00059-9
fatcat:vgmvm3my7rd7jneqx7ggnkpygm
Deep Learning for Text Style Transfer: A Survey
2021
Computational Linguistics
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Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others. ...
It has a long history in the field of natural language processing, and recently has re-gained significant attention thanks to the promising performance brought by deep neural models. ...
In Proceedings of the
learning style instance supported latent 2018 Conference on Empirical Methods in
space. ...
doi:10.1162/coli_a_00426
fatcat:v7vmb62ckfcu5k5mpu2pydnrxy
Modeling language acquisition in atypical phenotypes
2003
Psychological review
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An increasing number of connectionist models have been proposed to explain behavioral deficits in developmental disorders. ...
The authors examine these issues in depth with respect to a series of new simulations investigating past-tense formation in Williams syndrome. ...
Connectionist Models of Past-Tense Formation Connectionist theories of past-tense formation have converged on construing this domain in terms of an associative system that learns the relationship between ...
doi:10.1037/0033-295x.110.4.647
pmid:14599237
fatcat:5p5kiv252rhudg4sf2xqupqfda
Hocus-Socus: An Error Catastrophe for Complex Hebbian Learning Implies Neocortical Proofreading
[article]
2010
arXiv
pre-print
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Because output firing depends on input firing via the relevant connection strengths, Hebbian learning provides, in a feedback manner, sensitivity to input correlations. ...
Thus if 3 pixels correlate, they may give an "edge". ...
Acknowledgements We thank the following for comments on a draft of this paper: H. Barlow, P. Dayan ...
arXiv:1012.0946v1
fatcat:pr7zjimj4ja6bpqvarz2dhjcrq
Are imaging and lesioning convergent methods for assessing functional specialisation? Investigations using an artificial neural network
2012
Brain and Cognition
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SBI therefore distinguishes the ways in which different processes (e.g., producing regular vs. irregular verbs) exploit the same fixed network connectivity structure to drive behaviour (see also Sanger ...
The model comprised two processing 'routes': one a direct route between layers of input and output units, while the other, indirect, route featured an intermediate layer of processing units. ...
Acknowledgements This work was supported by European Commission grant NEST-029088(ANALOGY), ESRC grant RES-062-23-2721, and a Leverhulme Study Abroad Fellowship to MT. ...
doi:10.1016/j.bandc.2011.10.003
pmid:22088777
fatcat:m32iatirq5dcjatgov7rz3liam
Critical periods and catastrophic interference effects in the development of self-organizing feature maps
2008
Developmental Science
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We explored the relative contribution of network parameters (for example, whether learning rate and neighbourhood reduce across training), the representational resources available to the network, and the ...
We argue that the impact of map organization on behaviour must be interpreted in terms of the cognitive processes that the map is driving. ...
For example, at least one popular computational methodology for studying development -backpropagation connectionist networks -has indicated that catastrophic interference may be a serious problem for the ...
doi:10.1111/j.1467-7687.2008.00682.x
pmid:18466371
fatcat:nkkx4ke22raitmg5c7sl25k4nu
Unsupervised learning for vascular heterogeneity assessment of glioblastoma based on magnetic resonance imaging: The Hemodynamic Tissue Signature
[article]
2020
arXiv
pre-print
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This thesis focuses on the research and development of the Hemodynamic Tissue Signature (HTS) method: an unsupervised machine learning approach to describe the vascular heterogeneity of glioblastomas by ...
The HTS builds on the concept of habitats. An habitat is defined as a sub-region of the lesion with a particular MRI profile describing a specific physiological behavior. ...
) o 1 = 0.0582 → (vs. t 2 = 0.05) concluding the training of the network. ...
arXiv:2009.06288v1
fatcat:dum2y7fuuve73lxbb2any6iak4
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