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A Foundation Model for Error Correction Codes
Yoni Choukroun, Lior Wolf
ICLR, 2024
We propose a new foundational decoder that is code, length, and rate invariant and able to reach SOTA even on zero-shot scenarios.
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Deep Quantum Error Correction
Yoni Choukroun, Lior Wolf
AAAI, 2024
We outperform existing solutions by overcoming several quantum ECC challenges with a deep-learning-based decoder.
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Reconstructing the Hemodynamic Response Function via a Bimodal Transformer
Yoni Choukroun, Lior Golgher, Pablo Blinder, Lior Wolf
MICCAI, 2023
We learn the HRF time series via a geometry-aware transformer applied to the neurovascular modalities.
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Denoising Diffusion Error Correction Codes
Yoni Choukroun, Lior Wolf
ICLR, 2023 (Notable Paper)
We modelize the channel corruption and the decoding process as a forward and reverse diffusion process.
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Error Correction Code Transformer
Yoni Choukroun, Lior Wolf
Neurips, 2022
We employ an adapted Transformer in order to incorporate information about the code and to allow efficient cross-analysis of the channel's output elements.
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Geometric Transformer for End-to-End Molecule Properties Prediction
Yoni Choukroun, Lior Wolf
IJCAI, 2022
We develop a novel Transformer based architecture able to learn atomic interactions and we propose a novel molecular data augmentation strategy.
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Primal-Dual Sequential Subspace Optimization for Saddle-point Problems
Yoni Choukroun, Michael Zibulevski, Pavel Kisilev
NeuripsW, Optimization for Machine Learning 2020
We present a proximal subspace optimization algorithm coupled with a second-order oracle backtracking line-search for the optimization of saddle-point problems.
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Low-bit quantization of neural networks for efficient inference
Yoni Choukroun, Eli Kravchik, Fan Yang, Pavel Kisilev
ICCVW, 2019
We develop post-training quantization of neural networks to up to 4 bits using an optimal MSE minimization, dual kernel refinement and differentiable finetuning of the quantizatio parameters.
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Hamiltonian Operator for Spectral Shape Analysis
Yoni Choukroun, Alon Shtern, Alex Bronstein, Ron Kimmel
IEEE Transactions on Visualization and Computer Graphics, 2018
We propose the analysis and the adaptation of the Hamiltonian operator to the field of shape analysis.
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Sparse approximation of 3D meshes using the spectral geometry of the Hamiltonian operator
Yoni Choukroun, Gautam Pai, Ron Kimmel
Journal of Mathematical Imaging and Vision, 2018
We propose the compression of 3D surfaces using sparse matching pursuit over the mesh dictionary built upon the Hamiltonian Operator eigenspace.
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Deep learning for Decoding of Linear Codes- a Syndrome-based Approach
Amir Bennatan*, Yoni Choukroun*, Pavel Kisilev
IEEE International Symposium on Information Theory (ISIT), 2018
We present a preprocessing method of the channel output in order to make the model robust to codeword overfitting.
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Schrödinger Operator for Sparse Approximation of 3D Meshes.
Yoni Choukroun, Gautam Pai, Ron Kimmel
Proceedings of the Symposium on Geometry Processing: Posters, 2017
We propose the compression of 3D meshes using sparse matching pursuit over the mesh dictionary.
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Mammogram Classification and Abnormality Detection from Nonlocal Labels using Deep Multiple Instance Neural Network.
Yoni Choukroun, Ran Bakalo, Rami Ben-Ari, Ayelet Akselrod-Ballin, Ela Barkan, Pavel Kisilev
Visual Computing for Biology and Medicine, 2017
We develop a patch-based multiple instance learning method for mammogram classification.
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Consistent Discretization and Minimization of the l1 Norm on Manifolds.
Alex Bronstein, Yoni Choukroun, Ron Kimmel, Matan Sela
International Conference on 3D Vision (3DV), 2016
We propose a novel IRLS approximation of the L1 on manifolds equivalent to a constrained iterative Hamiltonian eigendecomposition.
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