no code implementations • 29 Apr 2024 • Sing-Yuan Yeh, Hau-Tieng Wu, Ronen Talmon, Mao-Pei Tsui
Alternating Diffusion (AD) is a commonly applied diffusion-based sensor fusion algorithm.
no code implementations • 4 Apr 2024 • Joseph S. Picard, Amitay Bar, Ronen Talmon
In this paper, as a remedy, we present a comprehensive study of the use of various Riemannian metrics of HPD matrices in CF.
no code implementations • 21 Feb 2024 • Amitay Bar, Rotem Mulayoff, Tomer Michaeli, Ronen Talmon
Langevin dynamics (LD) is widely used for sampling from distributions and for optimization.
1 code implementation • 30 May 2023 • Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne, Ronen Talmon
Finding meaningful representations and distances of hierarchical data is important in many fields.
1 code implementation • 9 Jan 2023 • Amitay Bar, Ronen Talmon
We consider the problem of estimating the direction of arrival of desired acoustic sources in the presence of multiple acoustic interference sources.
no code implementations • 23 Aug 2022 • Ido Cohen, Dan Valsky, Ronen Talmon
Compared to a competing supervised algorithm based on a Hidden Markov Model, our unsupervised method demonstrates similar results in the STN detection task and superior results in the DLOR detection task.
no code implementations • 18 Jul 2022 • David Cohen, Tal Shnitzer, Yuval Kluger, Ronen Talmon
This in turn allows for the extraction of the hidden manifold underlying the features and avoids overfitting, facilitating few-sample FS.
no code implementations • 21 Jan 2022 • Tal Shnitzer, Hau-Tieng Wu, Ronen Talmon
Our approach combines three components that are often considered separately: (i) manifold learning for building operators representing the geometry of the variables, (ii) Riemannian geometry of symmetric positive-definite matrices for multiscale composition of operators corresponding to different time samples, and (iii) spectral analysis of the composite operators for extracting different dynamic modes.
2 code implementations • NeurIPS 2021 • Ya-Wei Eileen Lin, Yuval Kluger, Ronen Talmon
Here, we take a purely geometric approach for label-free alignment of hierarchical datasets and introduce hyperbolic Procrustes analysis (HPA).
1 code implementation • 12 Oct 2021 • Uri Shaham, Jonathan Svirsky, Ori Katz, Ronen Talmon
Latent variable discovery is a central problem in data analysis with a broad range of applications in applied science.
no code implementations • 7 Oct 2021 • Panagiotis Papaioannou, Ronen Talmon, Ioannis Kevrekidis, Constantinos Siettos
We address a three-tier numerical framework based on manifold learning for the forecasting of high-dimensional time series.
no code implementations • 3 Apr 2021 • Lior Aloni, Omer Bobrowski, Ronen Talmon
At the finer (sample) level, we devise a new metric between samples based on manifold learning that facilitates quantitative structural analysis.
1 code implementation • 17 Sep 2020 • Ori Katz, Roy R. Lederman, Ronen Talmon
Our approach combines manifold learning, which is a class of nonlinear data-driven dimension reduction methods, with the well-known Riemannian geometry of symmetric and positive-definite (SPD) matrices.
no code implementations • 9 Sep 2020 • Ofir Lindenbaum, Amir Sagiv, Gal Mishne, Ronen Talmon
A low-dimensional dynamical system is observed in an experiment as a high-dimensional signal; for example, a video of a chaotic pendulums system.
no code implementations • 28 Jul 2020 • Or Yair, Almog Lahav, Ronen Talmon
In this paper, we present new results on the Riemannian geometry of symmetric positive semi-definite (SPSD) matrices.
no code implementations • 23 Jun 2020 • Jacob McErlean, John Malik, Yu-Ting Lin, Ronen Talmon, Hau-Tieng Wu
We evaluated the performance of the proposed algorithm using three respiratory signals recorded from different hardware sensors, and compared it with other existing EDR algorithms.
no code implementations • 9 Apr 2020 • Felix Dietrich, Or Yair, Rotem Mulayoff, Ronen Talmon, Ioannis G. Kevrekidis
We show analytically that our method is guaranteed to provide a set of orthogonal functions that are as jointly smooth as possible, ordered by increasing Dirichlet energy from the smoothest to the least smooth.
1 code implementation • ICML 2020 • Amitay Bar, Ronen Talmon, Ron Meir
Options have been shown to be an effective tool in reinforcement learning, facilitating improved exploration and learning.
no code implementations • 3 Jun 2019 • Or Yair, Felix Dietrich, Ronen Talmon, Ioannis G. Kevrekidis
We model the difference between two domains by a diffeomorphism and use the polar factorization theorem to claim that OT is indeed optimal for DA in a well-defined sense, up to a volume preserving map.
no code implementations • CVPR 2018 • Aviad Levis, Yoav Y. Schechner, Ronen Talmon
This enables geometric self-calibration in imaging of transparent objects.
no code implementations • 1 Jun 2018 • Ariel Schwartz, Ronen Talmon
Specifically, we show that our proposed method facilitates accurate localization of a moving agent from imaging data it collects.
no code implementations • ICLR 2018 • Gautam Pai, Ronen Talmon, Ron Kimmel
We propose a metric-learning framework for computing distance-preserving maps that generate low-dimensional embeddings for a certain class of manifolds.
no code implementations • 16 Nov 2017 • Gautam Pai, Ronen Talmon, Alex Bronstein, Ron Kimmel
This paper explores a fully unsupervised deep learning approach for computing distance-preserving maps that generate low-dimensional embeddings for a certain class of manifolds.
1 code implementation • 18 Aug 2017 • Gal Mishne, Ronen Talmon, Israel Cohen, Ronald R. Coifman, Yuval Kluger
Often the data is such that the observations do not reside on a regular grid, and the given order of the features is arbitrary and does not convey a notion of locality.
no code implementations • 13 Aug 2017 • Almog Lahav, Ronen Talmon, Yuval Kluger
Specifically we show that organizing similar coordinates in clusters can be exploited for the construction of the Mahalanobis distance between samples.
no code implementations • 13 Jan 2017 • Ori Katz, Ronen Talmon, Yu-Lun Lo, Hau-Tieng Wu
We show that without prior knowledge on the different modalities and on the measured system, our method gives rise to a data-driven representation that is well correlated with the underlying sleep process and is robust to noise and sensor-specific effects.
no code implementations • 25 Nov 2016 • Vardan Papyan, Ronen Talmon
The first step in our analysis is to find the common source of variability present in all sensor measurements.
no code implementations • 14 Jun 2016 • Or Yair, Ronen Talmon
In this paper, we address the problem of hidden common variables discovery from multimodal data sets of nonlinear high-dimensional observations.
no code implementations • 11 Apr 2016 • David Dov, Ronen Talmon, Israel Cohen
In this paper, we address the problem of multiple view data fusion in the presence of noise and interferences.
1 code implementation • 30 Jan 2016 • Ronen Talmon, Hau-Tieng Wu
The analysis of data sets arising from multiple sensors has drawn significant research attention over the years.
no code implementations • 6 Nov 2015 • Gal Mishne, Ronen Talmon, Ron Meir, Jackie Schiller, Uri Dubin, Ronald R. Coifman
In the wake of recent advances in experimental methods in neuroscience, the ability to record in-vivo neuronal activity from awake animals has become feasible.