Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Mishne, Gal"'
Finding meaningful distances between high-dimensional data samples is an important scientific task. To this end, we propose a new tree-Wasserstein distance (TWD) for high-dimensional data with two key aspects. First, our TWD is specifically designed
Externí odkaz:
http://arxiv.org/abs/2410.21107
Recurrent neural networks (RNNs) are a widely used tool for sequential data analysis, however, they are still often seen as black boxes of computation. Understanding the functional principles of these networks is critical to developing ideal model ar
Externí odkaz:
http://arxiv.org/abs/2406.01969
The distinguishing power of graph transformers is closely tied to the choice of positional encoding: features used to augment the base transformer with information about the graph. There are two primary types of positional encoding: absolute position
Externí odkaz:
http://arxiv.org/abs/2402.14202
Autor:
Mishne, Gal, Charles, Adam
Optical imaging of the brain has expanded dramatically in the past two decades. New optics, indicators, and experimental paradigms are now enabling in-vivo imaging from the synaptic to the cortex-wide scales. To match the resulting flood of data acro
Externí odkaz:
http://arxiv.org/abs/2402.08811
Autor:
Shi, Changhao, Mishne, Gal
Graph Laplacian learning, also known as network topology inference, is a problem of great interest to multiple communities. In Gaussian graphical models (GM), graph learning amounts to endowing covariance selection with the Laplacian structure. In gr
Externí odkaz:
http://arxiv.org/abs/2402.08105
Autor:
Sristi, Ram Dyuthi, Lindenbaum, Ofir, Lifshitz, Shira, Lavzin, Maria, Schiller, Jackie, Mishne, Gal, Benisty, Hadas
Feature selection is a crucial tool in machine learning and is widely applied across various scientific disciplines. Traditional supervised methods generally identify a universal set of informative features for the entire population. However, feature
Externí odkaz:
http://arxiv.org/abs/2312.14254
In this paper, we explore the untapped intersection of the graph connection Laplacian and discrete optimal transport to propose a novel framework for studying optimal parallel transport between vector fields on graphs. Our study establishes feasibili
Externí odkaz:
http://arxiv.org/abs/2312.10295
Autor:
Cloninger, Alexander, Mishne, Gal, Oslandsbotn, Andreas, Robertson, Sawyer Jack, Wan, Zhengchao, Wang, Yusu
We investigate the concept of effective resistance in connection graphs, expanding its traditional application from undirected graphs. We propose a robust definition of effective resistance in connection graphs by focusing on the duality of Dirichlet
Externí odkaz:
http://arxiv.org/abs/2308.09690
Motivated by the need to address the degeneracy of canonical Laplace learning algorithms in low label rates, we propose to reformulate graph-based semi-supervised learning as a nonconvex generalization of a \emph{Trust-Region Subproblem} (TRS). This
Externí odkaz:
http://arxiv.org/abs/2308.00142
Autor:
Shi, Changhao, Mishne, Gal
Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matrix to reveal the spectral decomposition of signals in the graph freque
Externí odkaz:
http://arxiv.org/abs/2306.08201