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A global geometric framework for nonlinear dimensionality reduction.
TLDR
An approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set and efficiently computes a globally optimal solution, and is guaranteed to converge asymptotically to the true structure. Expand
Human-level concept learning through probabilistic program induction
TLDR
A computational model is described that learns in a similar fashion and does so better than current deep learning algorithms and can generate new letters of the alphabet that look “right” as judged by Turing-like tests of the model's output in comparison to what real humans produce. Expand
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
TLDR
A novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convolutional networks and generative adversarial nets, and a powerful 3D shape descriptor which has wide applications in 3D object recognition. Expand
Topics in semantic representation.
TLDR
This article analyzes the abstract computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference that leads to a novel approach to semantic representation in which word meanings are represented in terms of a set of probabilistic topics. Expand
Hierarchical Topic Models and the Nested Chinese Restaurant Process
TLDR
A Bayesian approach is taken to generate an appropriate prior via a distribution on partitions that allows arbitrarily large branching factors and readily accommodates growing data collections. Expand
The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth
TLDR
A simple model for semantic growth is described, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node, which generates appropriate small-world statistics and power-law connectivity distributions. Expand
Learning Systems of Concepts with an Infinite Relational Model
TLDR
A nonparametric Bayesian model is presented that discovers systems of related concepts and applies the approach to four problems: clustering objects and features, learning ontologies, discovering kinship systems, and discovering structure in political data. Expand
Meta-Learning for Semi-Supervised Few-Shot Classification
TLDR
This work proposes novel extensions of Prototypical Networks that are augmented with the ability to use unlabeled examples when producing prototypes, and confirms that these models can learn to improve their predictions due to unlabeling examples, much like a semi-supervised algorithm would. Expand
Separating Style and Content with Bilinear Models
TLDR
A general framework for learning to solve two-factor tasks using bilinear models, which provide sufficiently expressive representations of factor interactions but can nonetheless be fit to data using efficient algorithms based on the singular value decomposition and expectation-maximization are presented. Expand
One shot learning of simple visual concepts
TLDR
A generative model of how characters are composed from strokes is introduced, where knowledge from previous characters helps to infer the latent strokes in novel characters, using a massive new dataset of handwritten characters. Expand
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