Stochastic triplet embedding

By L. V. D. van der Maaten, Kilian Q. Weinberger. 2012

227 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization (2020, methodology): “…embedding from triples of points, with comparisons between points in the form of “a is more similar to b than to c.” Approaches using triplets include DrLIM (Hadsell et al., 2006), t-STE (van der Maaten and Weinberger, 2012), SNaCK (Wilber et al., 2015) and TriMAP (Amid and Warmuth, 2019).”
TriMap: Large-scale Dimensionality Reduction Using Triplets (2019, methodology): “Finally, note that there exist connections between TriMap and a number of triplet (aka ordinal) embedding methods such as t-STE (Van Der Maaten & Weinberger, 2012).”
Bayesian representation learning with oracle constraints (2015, methodology): “r work Similarity-based learning, for instance via crowdsourcing, has been tackled in various ways in the community before. Notably, crowd-kernels are inferred and used for various vision tasks using [1] which assumes a ﬁxed Student-t structure to produce an embedding using similarity constraints fr”
Learning Concept Embeddings with Combined Human-Machine Expertise (2015, methodology): “Although taste and appearance are often correlated, any poor diner who has confused guacamole and wasabi knows that foods that taste very different may look deceptively similar because the strongest visual cues may not be reliable.”
Teaching Categories to Human Learners with Visual Explanations (2018, methodology): “We used t-STE [36] to generate a 5D embedding of the images which was then used to construct our hypothesis space.”
Disentangling Nonlinear Perceptual Embeddings With Multi-Query Triplet Networks (2016, background): “In particular, we strive for the distance between images of semantically similar objects to be small, and the distance between images of semantically different objects to be large.”
A more globally accurate dimensionality reduction method using triplets (2018, result): “Similar to (van der Maaten & Weinberger, 2012), we can deﬁne the satisfaction probability of the triplet ( i, j, k ) as Notice that Pr ijk → 1 whenever q ij q ik .”
Similarity Comparisons for Interactive Fine-Grained Categorization (2014, methodology): “, zN ], such that for each triplet (i, j, l) the similarity of zi and zj is large in comparison to the similarity of zi and zl according to a Student-t kernel; we refer the reader to [36] for additional details.”
Limitations of Weak Labels for Embedding and Tagging (2020, unknown): “This approach has been used successfully in various domains related to audio signal processing [2, 3].”