activity
20192021
most citedIn-Distribution Interpretability for Challenging Modalities

5 citations · 9 across the 4 of their papers we have counts for

collaborators

7 papers

cs.LG2021

A Rate-Distortion Framework for Explaining Black-box Model Decisions

Stefan Kolek, Duc Anh Nguyen, Ron Levie +2

We present the Rate-Distortion Explanation (RDE) framework, a mathematically well-founded method for explaining black-box model decisions. The framework is based on perturbations o…

math.FA2021

Wavelet Design with Optimally Localized Ambiguity Function: a Variational Approach

Ron Levie, Efrat Krimer Avraham, Nir Sochen

In this paper, we design mother wavelets for the 1D continuous wavelet transform with some optimality properties. An optimal mother wavelet here is one that has an ambiguity functi…

math.NA2020

Quasi Monte Carlo Time-Frequency Analysis

Ron Levie, Haim Avron, Gitta Kutyniok

We study signal processing tasks in which the signal is mapped via some generalized time-frequency transform to a higher dimensional time-frequency space, processed there, and synt…

cs.LG20205 cited

In-Distribution Interpretability for Challenging Modalities

Cosmas Heiß, Ron Levie, Cinjon Resnick +2

It is widely recognized that the predictions of deep neural networks are difficult to parse relative to simpler approaches. However, the development of methods to investigate the m…

eess.SP20204 cited

Real-time Localization Using Radio Maps

Çağkan Yapar, Ron Levie, Gitta Kutyniok +1

This paper deals with the problem of localization in a cellular network in a dense urban scenario. Global Navigation Satellite System typically performs poorly in urban environment…

eess.SP2019

RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks

Ron Levie, Çağkan Yapar, Gitta Kutyniok +1

In this paper we propose a highly efficient and very accurate deep learning method for estimating the propagation pathloss from a point (transmitter location) to any point