Human-level concept learning through probabilistic program. . People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy.. Human-level concept learning through probabilistic program induction Science. 2015 Dec 11;350(6266):1332-8. doi: 10.1126/science.aab3050.
Human-level concept learning through probabilistic program. from www.scientificamerican.com
Human-level concept learning through probabilistic program induction. Brenden M. Lake Ruslan Salakhutdinovand Joshua B. Tenenbaum Authors Info & Affiliations. Science..
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Human-level concept learning through probabilistic program induction Brenden M. Lake,1* Ruslan Salakhutdinov,2 Joshua B. Tenenbaum3 People learning new concepts can often.
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In this process, we make "infinite use of finite means", enabling us to learn new concepts quickly and nest concepts within each-other. While program induction and synthesis.
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2. Department of Computer Science and Department of Statistics, University of Toronto, 6 King's College Road, Toronto, ON M5S 3G4, Canada. 1 author. 3. Department of.
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BPL model for one-shot learning. Matlab source code for one-shot learning of handwritten characters with Bayesian Program Learning (BPL). Citing this code. Please cite the following.
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Details. Human-level concept learning through probabilistic program induction (Authors: Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum) Abstract: yet.
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The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human.
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Human-level concept learning through probabilistic program induction. B. Lake, R. Salakhutdinov, J. Tenenbaum. Published 11 December 2015. Computer Science. Science..
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On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches. We also present several “visual Turing tests”.
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2. 2 Institute for Advanced Studies in Basic Sciences Human-level concept learning through probabilistic program induction Mohammad Amid Abbasi Teacher: Dr.P.Razzaghi.
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11:30-11:45 Joshua Rule Learning list concepts through program induction ; 11:45-12:00 Neil Bramley Grounding compositional hypothesis generation. "Human-level concept learning.
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A team from DeepMind and Google Research leverages neural networks to automatically construct effective heuristics from a dataset for mixed integer programming (MIP) problems..
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Human-level concept learning through probabilistic program induction Brenden M. Lake,1* Ruslan Salakhutdinov,2 Joshua B. Tenenbaum3 People learning new concepts can often.
Source: omgteam.github.io
Abstract. People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of.
Source: omgteam.github.io
Human-level concept learning through probabilistic program induction (sciencemag.org) 96 points by novalis78 on Dec 14, 2015. a human being could group a segway or unicycle.
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Human-level concept learning through probabilistic program induction. People learning new concepts can often generalize successfully from just a single example, yet machine learning.
