no code implementations • 24 May 2017 • Anna C. Gilbert, Yi Zhang, Kibok Lee, Yuting Zhang, Honglak Lee
Several recent works have empirically observed that Convolutional Neural Nets (CNNs) are (approximately) invertible.
no code implementations • 29 Oct 2017 • Anna C. Gilbert, Lalit Jain
The distances between the data points are far from satisfying a metric.
3 code implementations • 19 Jul 2018 • Anna C. Gilbert, Rishi Sonthalia
Here, we present a new algorithm MR-MISSING that extends these previous algorithms and can be used to compute low dimensional representation on data sets with missing entries.
no code implementations • 25 Sep 2019 • Anna C. Gilbert, Rishi Sonthalia
Given a set of distances amongst points, determining what metric representation is most “consistent” with the input distances or the metric that captures the relevant geometric features of the data is a key step in many machine learning algorithms.
1 code implementation • 8 May 2020 • Rishi Sonthalia, Anna C. Gilbert
Given a set of dissimilarity measurements amongst data points, determining what metric representation is most "consistent" with the input measurements or the metric that best captures the relevant geometric features of the data is a key step in many machine learning algorithms.
3 code implementations • NeurIPS 2020 • Rishi Sonthalia, Anna C. Gilbert
Given data, finding a faithful low-dimensional hyperbolic embedding of the data is a key method by which we can extract hierarchical information or learn representative geometric features of the data.
no code implementations • 2 Jul 2020 • Umang Varma, Lalit Jain, Anna C. Gilbert
In this paper we modify a popular and well studied method, RankCentrality for rank aggregation to account for few comparisons and that incorporates additional feature information.
1 code implementation • NeurIPS 2021 • Rishi Sonthalia, Gregory Van Buskirk, Benjamin Raichel, Anna C. Gilbert
While $D_l$ is not metric, when given as input to cMDS instead of $D$, it empirically results in solutions whose distance to $D$ does not increase when we increase the dimension and the classification accuracy degrades less than the cMDS solution.
no code implementations • 13 Aug 2022 • Yulan Zhang, Anna C. Gilbert, Stefan Steinerberger
Modern methods in dimensionality reduction are dominated by nonlinear attraction-repulsion force-based methods (this includes t-SNE, UMAP, ForceAtlas2, LargeVis, and many more).
no code implementations • 23 Sep 2023 • Anna C. Gilbert, Kevin O'Neill
The success of algorithms in the analysis of high-dimensional data is often attributed to the manifold hypothesis, which supposes that this data lie on or near a manifold of much lower dimension.
no code implementations • 1 Mar 2024 • Anna C. Gilbert, Kevin O'Neill
This paper introduces a novel, non-deterministic method for embedding data in low-dimensional Euclidean space based on computing realizations of a Gaussian process depending on the geometry of the data.