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 Thomas L Griffiths

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Average citations per article21.39
Citation Count1,219
Publication count57
Publication years2000-2017
Available for download11
Average downloads per article1,163.00
Downloads (cumulative)12,793
Downloads (12 Months)1,116
Downloads (6 Weeks)129
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57 results found Export Results: bibtexendnoteacmrefcsv

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1
August 2017 IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 0

Deep neural networks have become increasingly successful at solving classic perception problems (e.g., recognizing objects), often reaching or surpassing human-level accuracy. In this abridged report of Peterson et al. [2016], we examine the relationship between the image representations learned by these networks and those of humans. We find that deep ...

2
April 2017
Bibliometrics:
Citation Count: 0

What should we do, or leave undone, in a day or a lifetime? How much messiness should we accept? What balance of the new and familiar is the most fulfilling? These may seem like uniquely human quandaries, but they are not. Computers, like us, confront limited space and time, so ...

3
May 2016 AAMAS '16: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems
Publisher: International Foundation for Autonomous Agents and Multiagent Systems
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 6,   Downloads (12 Months): 44,   Downloads (Overall): 99

Full text available: PDFPDF
The study of human-robot interaction is fundamental to the design and use of robotics in real-world applications. Robots will need to predict and adapt to the actions of human collaborators in order to achieve good performance and improve safety and end-user adoption. This paper evaluates a human-robot collaboration scheme that ...
Keywords: human-agent interaction, intention inference, collaborative task allocation, teamwork

4
April 2016
Bibliometrics:
Citation Count: 1

A fascinating exploration of how insights from computer algorithms can be applied to our everyday lives, helping to solve common decision-making problems and illuminate the workings of the human mindAll our lives are constrained by limited space and time, limits that give rise to a particular set of problems. What ...

5
December 2014 NIPS'14: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2
Publisher: MIT Press
Bibliometrics:
Citation Count: 0

Selecting the right algorithm is an important problem in computer science, because the algorithm often has to exploit the structure of the input to be efficient. The human mind faces the same challenge. Therefore, solutions to the algorithm selection problem can inspire models of human strategy selection and vice versa. ...

6
December 2013 NIPS'13: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 1

Learning a visual concept from a small number of positive examples is a significant challenge for machine learning algorithms. Current methods typically fail to find the appropriate level of generalization in a concept hierarchy for a given set of visual examples. Recent work in cognitive science on Bayesian models of ...

7
August 2013 UAI'13: Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence
Publisher: AUAI Press
Bibliometrics:
Citation Count: 0

We evaluate four computational models of explanation in Bayesian networks by comparing model predictions to human judgments. In two experiments, we present human participants with causal structures for which the models make divergent predictions and either solicit the best explanation for an observed event (Experiment 1) or have participants rate ...

8
December 2012 NIPS'12: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 2
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 1

The human mind has a remarkable ability to store a vast amount of information in memory, and an even more remarkable ability to retrieve these experiences when needed. Understanding the representations and algorithms that underlie human memory search could potentially be useful in other information retrieval settings, including internet search. ...

9
December 2011 NIPS'11: Proceedings of the 24th International Conference on Neural Information Processing Systems
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 0

Rational models of causal induction have been successful in accounting for people's judgments about causal relationships. However, these models have focused on explaining inferences from discrete data of the kind that can be summarized in a 2 × 2 contingency table. This severely limits the scope of these models, since ...

10
December 2011 NIPS'11: Proceedings of the 24th International Conference on Neural Information Processing Systems
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 0

How do people determine which elements of a set are most representative of that set? We extend an existing Bayesian measure of representativeness, which indicates the representativeness of a sample from a distribution, to define a measure of the representativeness of an item to a set. We show that this ...

11
December 2011 NIPS'11: Proceedings of the 24th International Conference on Neural Information Processing Systems
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 0

The object people perceive in an image can depend on its orientation relative to the scene it is in (its reference frame). For example, the images of the symbols × and + differ by a 45 degree rotation. Although real scenes have multiple images and reference frames, psychologists have focused ...

12
August 2011 AAAI'11: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 0

Categories are often organized into hierarchical taxonomies, that is, tree structures where each node represents a labeled category, and a node's parent and children are, respectively, the category's supertype and subtypes. A natural question is whether it is possible to reconstruct category taxonomies in cases where we are not given ...

13
July 2011 The Journal of Machine Learning Research: Volume 12, 2/1/2011
Publisher: JMLR.org
Bibliometrics:
Citation Count: 8
Downloads (6 Weeks): 0,   Downloads (12 Months): 6,   Downloads (Overall): 166

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Standard statistical models of language fail to capture one of the most striking properties of natural languages: the power-law distribution in the frequencies of word tokens. We present a framework for developing statistical models that can generically produce power laws, breaking generative models into two stages. The first stage, the ...

14
July 2011 The Journal of Machine Learning Research: Volume 12, 2/1/2011
Publisher: JMLR.org
Bibliometrics:
Citation Count: 43
Downloads (6 Weeks): 6,   Downloads (12 Months): 68,   Downloads (Overall): 582

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The Indian buffet process is a stochastic process defining a probability distribution over equivalence classes of sparse binary matrices with a finite number of rows and an unbounded number of columns. This distribution is suitable for use as a prior in probabilistic models that represent objects using a potentially infinite ...

15
June 2011 AIED'11: Proceedings of the 15th international conference on Artificial intelligence in education
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 7

Both human and automated tutors must infer what a student knows and plan future actions to maximize learning. Though substantial research has been done on tracking and modeling student learning, there has been significantly less attention on planning teaching actions and how the assumed student model impacts the resulting plans. ...

16
June 2011 CMCL '11: Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics
Publisher: Association for Computational Linguistics
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 1,   Downloads (12 Months): 5,   Downloads (Overall): 44

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Greater learnability has been offered as an explanation as to why certain properties appear in human languages more frequently than others. Languages with greater learnability are more likely to be accurately transmitted from one generation of learners to the next. We explore whether such a learnability bias is sufficient to ...

17
December 2010 NIPS'10: Proceedings of the 23rd International Conference on Neural Information Processing Systems - Volume 1
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 2

Identifying the features of objects becomes a challenge when those features can change in their appearance. We introduce the Transformed Indian Buffet Process (tIBP), and use it to define a nonparametric Bayesian model that infers features that can transform across instantiations. We show that this model can identify features that ...

18
June 2010 ICML'10: Proceedings of the 27th International Conference on International Conference on Machine Learning
Publisher: Omnipress
Bibliometrics:
Citation Count: 0

Transfer learning can be described as the distillation of abstract knowledge from one learning domain or task and the reuse of that knowledge in a related domain or task. In categorization settings, transfer learning is the modification by past experience of prior expectations about what types of categories are likely ...

19 published by ACM
February 2010 Journal of the ACM (JACM): Volume 57 Issue 2, January 2010
Publisher: ACM
Bibliometrics:
Citation Count: 111
Downloads (6 Weeks): 23,   Downloads (12 Months): 254,   Downloads (Overall): 2,986

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We present the nested Chinese restaurant process (nCRP), a stochastic process that assigns probability distributions to ensembles of infinitely deep, infinitely branching trees. We show how this stochastic process can be used as a prior distribution in a Bayesian nonparametric model of document collections. Specifically, we present an application to ...
Keywords: Bayesian nonparametric statistics, unsupervised learning

20 published by ACM
January 2010 ACM Transactions on Information Systems (TOIS): Volume 28 Issue 1, January 2010
Publisher: ACM
Bibliometrics:
Citation Count: 48
Downloads (6 Weeks): 17,   Downloads (12 Months): 130,   Downloads (Overall): 1,818

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We propose an unsupervised learning technique for extracting information about authors and topics from large text collections. We model documents as if they were generated by a two-stage stochastic process. An author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over ...
Keywords: Gibbs sampling, author models, unsupervised learning, perplexity, Topic models



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