Search Results for author: Nadav Dym

Found 6 papers, 1 papers with code

Low Dimensional Invariant Embeddings for Universal Geometric Learning

no code implementations5 May 2022 Nadav Dym, Steven J. Gortler

We apply this methodology to obtain an efficient scheme for computing separating invariants for several classical group actions which have been studied in the invariant learning literature.

A Simple and Universal Rotation Equivariant Point-cloud Network

1 code implementation2 Mar 2022 Ben Finkelshtein, Chaim Baskin, Haggai Maron, Nadav Dym

Equivariance to permutations and rigid motions is an important inductive bias for various 3D learning problems.

Neural Network Approximation of Refinable Functions

no code implementations28 Jul 2021 Ingrid Daubechies, Ronald DeVore, Nadav Dym, Shira Faigenbaum-Golovin, Shahar Z. Kovalsky, Kung-Ching Lin, Josiah Park, Guergana Petrova, Barak Sober

Namely, we show that refinable functions are approximated by the outputs of deep ReLU networks with a fixed width and increasing depth with accuracy exponential in terms of their number of parameters.

On the Universality of Rotation Equivariant Point Cloud Networks

no code implementations ICLR 2021 Nadav Dym, Haggai Maron

We first derive two sufficient conditions for an equivariant architecture to have the universal approximation property, based on a novel characterization of the space of equivariant polynomials.


Expression of Fractals Through Neural Network Functions

no code implementations27 May 2019 Nadav Dym, Barak Sober, Ingrid Daubechies

The combination of this phenomenon with the capacity, demonstrated here, of DNNs to efficiently approximate IFS may contribute to the success of DNNs, particularly striking for image processing tasks, as well as suggest new algorithms for representing self similarities in images based on the DNN mechanism.

Linearly Converging Quasi Branch and Bound Algorithms for Global Rigid Registration

no code implementations ICCV 2019 Nadav Dym, Shahar Ziv Kovalsky

In recent years, several branch-and-bound (BnB) algorithms have been proposed to globally optimize rigid registration problems.

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