You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

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 • 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 • 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.

2 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.

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.

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.

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 • 29 Oct 2017 • Anna C. Gilbert, Lalit Jain

The distances between the data points are far from satisfying a metric.

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.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.