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ORCIDRoy R. Lederman
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2020 – today
- 2025
[j5]Ori Katz, Roy R. Lederman, Ronen Talmon:
Multimodal manifold learning using kernel interpolation along geodesic paths. Inf. Fusion 114: 102637 (2025)- 2023
- [i15]Daniel G. Edelberg, Roy R. Lederman:
Using VAEs to Learn Latent Variables: Observations on Applications in cryo-EM. CoRR abs/2303.07487 (2023) - [i14]Roy R. Lederman, Bogdan Toader:
On Manifold Learning in Plato's Cave: Remarks on Manifold Learning and Physical Phenomena. CoRR abs/2304.14248 (2023) - [i13]Bogdan Toader, Marcus A. Brubaker, Roy R. Lederman:
Efficient high-resolution refinement in cryo-EM with stochastic gradient descent. CoRR abs/2311.16100 (2023) - 2022
- [c4]James A. Brofos, Marylou Gabrié, Marcus A. Brubaker, Roy R. Lederman:
Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals. AISTATS 2022: 5949-5986 - 2021
- [c3]James A. Brofos, Roy R. Lederman:
Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo. ICML 2021: 1072-1081 - [i12]James A. Brofos, Marcus A. Brubaker, Roy R. Lederman:
Manifold Density Estimation via Generalized Dequantization. CoRR abs/2102.07143 (2021) - [i11]Zhou Fan, Roy R. Lederman, Yi Sun, Tianhao Wang, Sheng Xu:
Maximum likelihood for high-noise group orbit estimation and single-particle cryo-EM. CoRR abs/2107.01305 (2021) - 2020
- [i10]James A. Brofos, Roy R. Lederman:
Non-Canonical Hamiltonian Monte Carlo. CoRR abs/2008.08191 (2020) - [i9]Ori Katz, Roy R. Lederman, Ronen Talmon:
Spectral Flow on the Manifold of SPD Matrices for Multimodal Data Processing. CoRR abs/2009.08062 (2020) - [i8]James A. Brofos, Roy R. Lederman:
Magnetic Manifold Hamiltonian Monte Carlo. CoRR abs/2010.07753 (2020)
2010 – 2019
- 2019
- [i7]Roy R. Lederman, Joakim Andén, Amit Singer:
Hyper-Molecules: on the Representation and Recovery of Dynamical Structures, with Application to Flexible Macro-Molecular Structures in Cryo-EM. CoRR abs/1907.01589 (2019) - [i6]Roy R. Lederman, Stefan Steinerberger:
Extreme Values of the Fiedler Vector on Trees. CoRR abs/1912.08327 (2019) - 2018
- [j4]Uri Shaham, Roy R. Lederman:
Learning by coincidence: Siamese networks and common variable learning. Pattern Recognit. 74: 52-63 (2018) - [c2]Nicolas Boumal, Tamir Bendory
, Roy R. Lederman, Amit Singer:
Heterogeneous multireference alignment: A single pass approach. CISS 2018: 1-6 - 2017
- [i5]Roy R. Lederman, Amit Singer:
Continuously heterogeneous hyper-objects in cryo-EM and 3-D movies of many temporal dimensions. CoRR abs/1704.02899 (2017) - [i4]Nicolas Boumal, Tamir Bendory, Roy R. Lederman, Amit Singer:
Heterogeneous multireference alignment: a single pass approach. CoRR abs/1710.02590 (2017) - [i3]Roy R. Lederman:
Numerical Algorithms for the Computation of Generalized Prolate Spheroidal Functions. CoRR abs/1710.02874 (2017) - 2016
- [j3]Roy R. Lederman, Vladimir Rokhlin:
On the Analytical and Numerical Properties of the Truncated Laplace Transform. Part II. SIAM J. Numer. Anal. 54(2): 665-687 (2016) - [i2]Roy R. Lederman, Amit Singer:
A Representation Theory Perspective on Simultaneous Alignment and Classification. CoRR abs/1607.03464 (2016) - 2015
- [j2]Roy R. Lederman, Vladimir Rokhlin:
On the Analytical and Numerical Properties of the Truncated Laplace Transform I. SIAM J. Numer. Anal. 53(3): 1214-1235 (2015) - [c1]Roy R. Lederman, Ronen Talmon, Hau-Tieng Wu, Yu-Lun Lo, Ronald R. Coifman:
Alternating diffusion for common manifold learning with application to sleep stage assessment. ICASSP 2015: 5758-5762 - [i1]Uri Shaham, Roy R. Lederman:
Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks. CoRR abs/1512.08806 (2015) - 2013
- [j1]Roy R. Lederman:
A random-permutations-based approach to fast read alignment. BMC Bioinform. 14(S-5): S8 (2013)
Coauthor Index
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