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Language-Mediated, Object-Centric Representation Learning
We present Language-mediated, Object-centric Representation Learning (LO...
12/31/2020 ∙ by Ruocheng Wang, et al. ∙
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Analyzing machine-learned representations: A natural language case study
As modern deep networks become more complex, and get closer to human-lik...
09/12/2019 ∙ by Ishita Dasgupta, et al. ∙
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Human-in-the-Loop Interpretability Prior
We often desire our models to be interpretable as well as accurate. Prio...
05/29/2018 ∙ by Isaac Lage, et al. ∙
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Estimating scale-invariant future in continuous time
Natural learners must compute an estimate of future outcomes that follow...
02/18/2018 ∙ by Zoran Tiganj, et al. ∙
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Evaluating Compositionality in Sentence Embeddings
An important frontier in the quest for human-like AI is compositional se...
02/12/2018 ∙ by Ishita Dasgupta, et al. ∙
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Deep Successor Reinforcement Learning
Learning robust value functions given raw observations and rewards is no...
06/08/2016 ∙ by Tejas D. Kulkarni, et al. ∙
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Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in ...
04/01/2016 ∙ by Brenden M. Lake, et al. ∙
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Distance Dependent Infinite Latent Feature Models
Latent feature models are widely used to decompose data into a small num...
10/25/2011 ∙ by Samuel J. Gershman, et al. ∙
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A Tutorial on Bayesian Nonparametric Models
A key problem in statistical modeling is model selection, how to choose ...
06/14/2011 ∙ by Samuel J. Gershman, et al. ∙
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