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We are interested in learning programs for multiple related tasks given only a few train- ing examples per task. Since the program for a single task is underdetermined by its data, we introduce a nonparametric hierar- chical Bayesian prior over programs which shares statistical strength across multiple tasks. The key
GitHub is where people build software. More than 27 million people use GitHub to discover, fork, and contribute to over 80 million projects.
Bayesian Programming. 13. Bayesian Program. Description. Question. Specification. Identification. •Variables. •Decomposition. •Parametric Forms. •Learning from instances
In addition to introducing new one-shot learning challenge problems, this paper also introduces. Hierarchical Bayesian Program Learning (HBPL), a model that exploits the principles of composi- tionality and causality to learn a wide range of simple visual concepts from just a single example. We compared the model with
You may not like it! But artificial intelligence jumped a bit closer this year with the development of “Bayesian. Program Learning," by Lake, Salakhutdinov, and Tenenbaum, published in Science. It's news because for decades I've been hearing about how hard it is to achieve artificial intelligence, and the most successful
an approximate Bayesian computation method to learn the programs, whose executions generate samples that reading group, the ma- chine learning lunches and the brainstorming artificial intelligence forum sessions. This work, in particular that on learning probabilistic programs, and my skills have also significantly
Sampling for Bayesian Program Learning. Kevin Ellis. Brain and Cognitive Sciences. MIT ellisk@mit.edu. Armando Solar-Lezama. CSAIL. MIT asolar@csail.mit.edu. Joshua B. Tenenbaum. Brain and Cognitive Sciences. MIT jbt@mit.edu. Abstract. Towards learning programs from data, we introduce the problem of sampling.
Dec 10, 2015 This paper introduces the Bayesian program learning (BPL) framework, capable of learning a large class of visual concepts from just a single example and generalizing in ways that are mostly indistinguishable from people. Concepts are rep- resented as simple probabilistic programs—that is, probabilistic
15.4. Learning Structure of Bayesian Networks 198. 15.5. Bayesian Evolution? 199. 16. Frequently Asked Question and. Frequently Argued Matter 201. 16.1. APPLICATIONS OF BAYESIAN PROGRAMMING (WHAT ARE?) 201. 16.2. BAYES, THOMAS (WHO IS?) 202. 16.3. BAYESIAN DECISION THEORY (WHAT IS?) 202.
Dec 10, 2016 The story. Key ingredients: ? Learning from few examples. ? Modeling your uncertainty. ? Coming up with explanations (generative/causal models). The toolkit: Programming Languages meets Bayes. “Bayesian Program Learning": [Lake et al, 2015]; [Liang et al, 2010]
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