no code implementations • 30 Nov 2023 • Jonathan Huml, Abiy Tasissa, Demba Ba
We propose an autoencoder architecture (WLSC) whose latent representations are implicitly, locally organized for spectral clustering through a Laplacian quadratic form of a bipartite graph, which generates a diverse set of artificial receptive fields that match primate data in V1 as faithfully as recent contrastive frameworks like Local Low Dimensionality, or LLD \citep{lld} that discard sparse dictionary learning.
no code implementations • 29 Nov 2023 • Samuel Lichtenberg, Abiy Tasissa
First, we establish a relationship between the columns of the anchor-mobile block in the distance matrix and the columns of the corresponding block in the Gram matrix via a graph Laplacian.
no code implementations • 10 Jul 2023 • Woojoo Na, Abiy Tasissa
We introduce RACH-Space, an algorithm for labelling unlabelled data in weakly supervised learning, given incomplete, noisy information about the labels.
no code implementations • 10 Mar 2023 • Samuel Lichtenberg, Abiy Tasissa
A central result in CMDS connects the squared Euclidean matrix to a Gram matrix derived from the set of points.
no code implementations • 22 Feb 2023 • Jonathan Huml, Abiy Tasissa, Demba Ba
The classical sparse coding model represents visual stimuli as a linear combination of a handful of learned basis functions that are Gabor-like when trained on natural image data.
no code implementations • 14 Nov 2022 • Ahmed Abbasi, Abiy Tasissa, Shuchin Aeron
The unlabeled sensing problem is to recover an unknown signal from permuted linear measurements.
no code implementations • 21 Oct 2022 • Marshall Mueller, Shuchin Aeron, James M. Murphy, Abiy Tasissa
We show this approach leads to sparse representations in Wasserstein space and addresses the problem of non-uniqueness of barycentric representation.
1 code implementation • 28 Jan 2022 • Matthew Werenski, Ruijie Jiang, Abiy Tasissa, Shuchin Aeron, James M. Murphy
Our first main result leverages the Riemannian geometry of Wasserstein-2 space to provide a procedure for recovering the barycentric coordinates as the solution to a quadratic optimization problem assuming access to the true reference measures.
2 code implementations • 26 Oct 2021 • Ahmed Ali Abbasi, Abiy Tasissa, Shuchin Aeron
The unlabeled sensing problem is to solve a noisy linear system of equations under unknown permutation of the measurements.
no code implementations • 28 Apr 2021 • Abiy Tasissa, Pranay Tankala, Demba Ba
Sparse manifold learning algorithms combine techniques in manifold learning and sparse optimization to learn features that could be utilized for downstream tasks.
no code implementations • 13 Feb 2021 • Emmanouil Theodosis, Bahareh Tolooshams, Pranay Tankala, Abiy Tasissa, Demba Ba
Recent approaches in the theoretical analysis of model-based deep learning architectures have studied the convergence of gradient descent in shallow ReLU networks that arise from generative models whose hidden layers are sparse.
1 code implementation • 8 Jan 2021 • Abiy Tasissa, Duc Nguyen, James Murphy
A method for active learning of hyperspectral images (HSI) is proposed, which combines deep learning with diffusion processes on graphs.
1 code implementation • 3 Dec 2020 • Pranay Tankala, Abiy Tasissa, James M. Murphy, Demba Ba
We theoretically analyze the proposed program by relating the weighted $\ell_1$ penalty in KDS to a weighted $\ell_0$ program.
no code implementations • 16 Jun 2020 • Abiy Tasissa, Emmanouil Theodosis, Bahareh Tolooshams, Demba Ba
We propose a novel dense and sparse coding model that integrates both representation capability and discriminative features.
1 code implementation • 14 Nov 2019 • Ahmed Abbasi, Abiy Tasissa, Shuchin Aeron
Unlabeled sensing is a linear inverse problem where the measurements are scrambled under an unknown permutation leading to loss of correspondence between the measurements and the rows of the sensing matrix.
no code implementations • 12 Apr 2018 • Abiy Tasissa, Rongjie Lai
In this paper, this minimization program is recast as a matrix completion problem of a low-rank $r$ Gram matrix with respect to a suitable basis.