Search Results for author: Daniel Pimentel-Alarcón

Found 5 papers, 0 papers with code

Tensor Methods for Nonlinear Matrix Completion

no code implementations26 Apr 2018 Greg Ongie, Daniel Pimentel-Alarcón, Laura Balzano, Rebecca Willett, Robert D. Nowak

This approach will succeed in many cases where traditional LRMC is guaranteed to fail because the data are low-rank in the tensorized representation but not in the original representation.

Low-Rank Matrix Completion

Geometry of the Minimum Volume Confidence Sets

no code implementations16 Feb 2022 Heguang Lin, Mengze Li, Daniel Pimentel-Alarcón, Matthew Malloy

Prior work showed the minimum-volume confidence sets are the level-sets of a discontinuous function defined by an exact p-value.

Fusion Subspace Clustering for Incomplete Data

no code implementations22 May 2022 Usman Mahmood, Daniel Pimentel-Alarcón

This paper introduces {\em fusion subspace clustering}, a novel method to learn low-dimensional structures that approximate large scale yet highly incomplete data.

Clustering Model Selection

Contrastive Learning with Orthonormal Anchors (CLOA)

no code implementations27 Mar 2024 Huanran Li, Daniel Pimentel-Alarcón

This study focuses on addressing the instability issues prevalent in contrastive learning, specifically examining the InfoNCE loss function and its derivatives.

Contrastive Learning

TransFusion: Contrastive Learning with Transformers

no code implementations27 Mar 2024 Huanran Li, Daniel Pimentel-Alarcón

This paper proposes a novel framework, TransFusion, designed to make the process of contrastive learning more analytical and explainable.

Contrastive Learning Data Augmentation

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