no code implementations • 3 Feb 2024 • Cecilia Aguerrebere, Mark Hildebrand, Ishwar Singh Bhati, Theodore Willke, Mariano Tepper
In this work, we study LVQ in streaming similarity search.
1 code implementation • 26 Dec 2023 • Mariano Tepper, Ishwar Singh Bhati, Cecilia Aguerrebere, Mark Hildebrand, Ted Willke
In this work, we present LeanVec, a framework that combines linear dimensionality reduction with vector quantization to accelerate similarity search on high-dimensional vectors while maintaining accuracy.
1 code implementation • 4 Oct 2023 • Seyed Saman Saboksayr, Gonzalo Mateos, Mariano Tepper
We deal with the combinatorial problem of learning directed acyclic graph (DAG) structure from observational data adhering to a linear structural equation model (SEM).
1 code implementation • 7 Apr 2023 • Cecilia Aguerrebere, Ishwar Bhati, Mark Hildebrand, Mariano Tepper, Ted Willke
In this work, we present new techniques and systems for creating faster and smaller graph-based indices.
no code implementations • 13 May 2022 • Romain Cosentino, Anirvan Sengupta, Salman Avestimehr, Mahdi Soltanolkotabi, Antonio Ortega, Ted Willke, Mariano Tepper
When used for transfer learning, the projector is discarded since empirical results show that its representation generalizes more poorly than the encoder's.
no code implementations • 8 Jun 2020 • Mariano Tepper, Dipanjan Sengupta, Ted Willke
We compare POSH, Binary OSL, and SphericalHash to several state-of-the-art hashing methods and provide empirical results for the superiority of the proposed methods across a wide range of standard benchmarks and parameter settings.
no code implementations • 3 Jun 2019 • Mariano Tepper
We posit that hippocampal place cells encode information about future locations under a transition distribution observed as an agent explores a given (physical or conceptual) space.
1 code implementation • NeurIPS 2018 • Anirvan Sengupta, Cengiz Pehlevan, Mariano Tepper, Alexander Genkin, Dmitri Chklovskii
Many neurons in the brain, such as place cells in the rodent hippocampus, have localized receptive fields, i. e., they respond to a small neighborhood of stimulus space.
no code implementations • 19 Jun 2017 • Mariano Tepper, Anirvan M. Sengupta, Dmitri Chklovskii
In solving hard computational problems, semidefinite program (SDP) relaxations often play an important role because they come with a guarantee of optimality.
no code implementations • CVPR 2017 • Mariano Tepper, Guillermo Sapiro
In this work, we introduce a highly efficient algorithm to address the nonnegative matrix underapproximation (NMU) problem, i. e., nonnegative matrix factorization (NMF) with an additional underapproximation constraint.
1 code implementation • 18 Oct 2016 • Mariano Tepper, Guillermo Sapiro
In this work we introduce a comprehensive algorithmic pipeline for multiple parametric model estimation.
1 code implementation • 18 May 2015 • Mariano Tepper, Guillermo Sapiro
To address this, in this work we propose to use structured random compression, that is, random projections that exploit the data structure, for two NMF variants: classical and separable.
no code implementations • 30 Apr 2014 • Mariano Tepper, Guillermo Sapiro
We consider grouping as a general characterization for problems such as clustering, community detection in networks, and multiple parametric model estimation.
no code implementations • 29 May 2013 • Thiago V. Spina, Mariano Tepper, Amy Esler, Vassilios Morellas, Nikolaos Papanikolopoulos, Alexandre X. Falcão, Guillermo Sapiro
Video object segmentation is a challenging problem due to the presence of deformable, connected, and articulated objects, intra- and inter-object occlusions, object motion, and poor lighting.