Ofir Lindenbaum

According to our database, Ofir Lindenbaum authored at least 61 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Classifying Neuronal Cell Types Based on Shared Electrophysiological Information from Humans and Mice.

Ofek Ophir, Orit Shefi, Ofir Lindenbaum

Neuroinformatics, October, 2024

Domain-Generalizable Multiple-Domain Clustering.

Amit Rozner, Barak Battash, Lior Wolf, Ofir Lindenbaum

Trans. Mach. Learn. Res., 2024

TransformLLM: Adapting Large Language Models via LLM-Transformed Reading Comprehension Text.

Iftach Arbel, Yehonathan Refael, Ofir Lindenbaum

CoRR, 2024

AdaRankGrad: Adaptive Gradient-Rank and Moments for Memory-Efficient LLMs Training and Fine-Tuning.

Yehonathan Refael, Jonathan Svirsky, Boris Shustin, Wasim Huleihel, Ofir Lindenbaum

CoRR, 2024

Spectral Self-supervised Feature Selection.

Daniel Segal, Ofir Lindenbaum, Ariel Jaffe

CoRR, 2024

Multiple Descents in Unsupervised Learning: The Role of Noise, Domain Shift and Anomalies.

Kobi Rahimi, Tom Tirer, Ofir Lindenbaum

CoRR, 2024

Sparse Binarization for Fast Keyword Spotting.

Jonathan Svirsky, Uri Shaham, Ofir Lindenbaum

CoRR, 2024

SEL-CIE: Knowledge-Guided Self-Supervised Learning Framework for CIE-XYZ Reconstruction from Non-Linear sRGB Images.

Shir Barzel, Moshe Salhov, Ofir Lindenbaum, Amir Averbuch

CoRR, 2024

Obtaining Favorable Layouts for Multiple Object Generation.

Barak Battash, Amit Rozner, Lior Wolf, Ofir Lindenbaum

CoRR, 2024

Self Supervised Correlation-based Permutations for Multi-View Clustering.

Ran Eisenberg, Jonathan Svirsky, Ofir Lindenbaum

CoRR, 2024

Interpretable Deep Clustering for Tabular Data.

Jonathan Svirsky, Ofir Lindenbaum

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Contextual Feature Selection with Conditional Stochastic Gates.

Ram Dyuthi Sristi, Ofir Lindenbaum, Shira Lifshitz, Maria Lavzin, Jackie Schiller, Gal Mishne, Hadas Benisty

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction.

Idan Cohen, Sharon Gannot, Ofir Lindenbaum

Proceedings of the IEEE International Conference on Acoustics, 2024

Efficient Verification-Based Face Identification.

Amit Rozner, Barak Battash, Ofir Lindenbaum, Lior Wolf

Proceedings of the 18th IEEE International Conference on Automatic Face and Gesture Recognition, 2024

Knowledge Editing in Language Models via Adapted Direct Preference Optimization.

Amit Rozner, Barak Battash, Lior Wolf, Ofir Lindenbaum

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

Revisiting the Noise Model of Stochastic Gradient Descent.

Barak Battash, Lior Wolf, Ofir Lindenbaum

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Support recovery with Projected Stochastic Gates: Theory and application for linear models.

Soham Jana, Henry Li, Yutaro Yamada, Ofir Lindenbaum

Signal Process., December, 2023

Contextual Feature Selection with Conditional Stochastic Gates.

Ram Dyuthi Sristi, Ofir Lindenbaum, Maria Lavzin, Jackie Schiller, Gal Mishne, Hadas Benisty

CoRR, 2023

Gappy local conformal auto-encoders for heterogeneous data fusion: in praise of rigidity.

Erez Peterfreund, Iryna Burak, Ofir Lindenbaum, Jim Gimlett, Felix Dietrich, Ronald R. Coifman, Ioannis G. Kevrekidis

CoRR, 2023

TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models.

Guy Zamberg, Moshe Salhov, Ofir Lindenbaum, Amir Averbuch

CoRR, 2023

Interpretable Deep Clustering.

Jonathan Svirsky, Ofir Lindenbaum

CoRR, 2023

Anomaly Detection with Variance Stabilized Density Estimation.

Amit Rozner, Barak Battash, Henry Li, Lior Wolf, Ofir Lindenbaum

CoRR, 2023

Neuronal Cell Type Classification using Deep Learning.

Ofek Ophir, Orit Shefi, Ofir Lindenbaum

CoRR, 2023

Revisiting the Noise Model of Stochastic Gradient Descent.

Barak Battash, Ofir Lindenbaum

CoRR, 2023

Multi-modal differentiable unsupervised feature selection.

Junchen Yang, Ofir Lindenbaum, Yuval Kluger, Ariel Jaffe

Proceedings of the Uncertainty in Artificial Intelligence, 2023

SG-VAD: Stochastic Gates Based Speech Activity Detection.

Jonathan Svirsky, Ofir Lindenbaum

Proceedings of the IEEE International Conference on Acoustics, 2023

Neuronal Cell Type Classification Using Locally Sparse Networks.

Ofek Ophir, Orit Shefi, Ofir Lindenbaum

Proceedings of the IEEE International Conference on Acoustics, 2023

Domain and Modality Adaptation Using Multi-Kernel Matching.

Tamir Baruch Yampolsky, Ronen Talmon, Ofir Lindenbaum

Proceedings of the 31st European Signal Processing Conference, 2023

2022

Refined least squares for support recovery.

Ofir Lindenbaum, Stefan Steinerberger

Signal Process., 2022

Deep unsupervised feature selection by discarding nuisance and correlated features.

Uri Shaham, Ofir Lindenbaum, Jonathan Svirsky, Yuval Kluger

Neural Networks, 2022

Imbalanced Classification via a Tabular Translation GAN.

Jonathan Gradstein, Moshe Salhov, Yoav Tulpan, Ofir Lindenbaum, Amir Averbuch

CoRR, 2022

Locally Sparse Neural Networks for Tabular Biomedical Data.

Junchen Yang, Ofir Lindenbaum, Yuval Kluger

Proceedings of the International Conference on Machine Learning, 2022

L0-Sparse Canonical Correlation Analysis.

Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Randomly aggregated least squares for support recovery.

Ofir Lindenbaum, Stefan Steinerberger

Signal Process., 2021

Support Recovery with Stochastic Gates: Theory and Application for Linear Models.

Soham Jana, Henry Li, Yutaro Yamada, Ofir Lindenbaum

CoRR, 2021

Probabilistic Robust Autoencoders for Anomaly Detection.

Yariv Aizenbud, Ofir Lindenbaum, Yuval Kluger

CoRR, 2021

Locally Sparse Networks for Interpretable Predictions.

Junchen Yang, Ofir Lindenbaum, Yuval Kluger

CoRR, 2021

Differentiable Unsupervised Feature Selection based on a Gated Laplacian.

Ofir Lindenbaum, Uri Shaham, Erez Peterfreund, Jonathan Svirsky, Nicolas Casey, Yuval Kluger

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Seismic Event Discrimination Using Deep CCA.

Ofir Lindenbaum, Neta Rabin, Yuri Bregman, Amir Averbuch

IEEE Geosci. Remote. Sens. Lett., 2020

Multi-view diffusion maps.

Ofir Lindenbaum, Arie Yeredor, Moshe Salhov, Amir Averbuch

Inf. Fusion, 2020

The Spectral Underpinning of word2vec.

Ariel Jaffe, Yuval Kluger, Ofir Lindenbaum, Jonathan Patsenker, Erez Peterfreund, Stefan Steinerberger

Frontiers Appl. Math. Stat., 2020

Gaussian bandwidth selection for manifold learning and classification.

Ofir Lindenbaum, Moshe Salhov, Arie Yeredor, Amir Averbuch

Data Min. Knowl. Discov., 2020

Deep Gated Canonical Correlation Analysis.

Ofir Lindenbaum, Moshe Salhov, Amir Averbuch, Yuval Kluger

CoRR, 2020

Kernel-based parameter estimation of dynamical systems with unknown observation functions.

Ofir Lindenbaum, Amir Sagiv, Gal Mishne, Ronen Talmon

CoRR, 2020

Let the Data Choose its Features: Differentiable Unsupervised Feature Selection.

Ofir Lindenbaum, Uri Shaham, Jonathan Svirsky, Erez Peterfreund, Yuval Kluger

CoRR, 2020

LOCA: LOcal Conformal Autoencoder for standardized data coordinates.

Erez Peterfreund, Ofir Lindenbaum, Felix Dietrich, Tom Bertalan, Matan Gavish, Ioannis G. Kevrekidis, Ronald R. Coifman

CoRR, 2020

Feature Selection using Stochastic Gates.

Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger

Proceedings of the 37th International Conference on Machine Learning, 2020

Variational Diffusion Autoencoders with Random Walk Sampling.

Henry Li, Ofir Lindenbaum, Xiuyuan Cheng, Alexander Cloninger

Proceedings of the Computer Vision - ECCV 2020, 2020

2019

Diffusion Variational Autoencoders.

Henry Li, Ofir Lindenbaum, Xiuyuan Cheng, Alexander Cloninger

CoRR, 2019

2018

Multiview Kernels for Low-Dimensional Modeling of Seismic Events.

Ofir Lindenbaum, Yuri Bregman, Neta Rabin, Amir Averbuch

IEEE Trans. Geosci. Remote. Sens., 2018

Deep supervised feature selection using Stochastic Gates.

Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban, Yuval Kluger

CoRR, 2018

Geometry Based Data Generation.

Ofir Lindenbaum, Jay S. Stanley III, Guy Wolf, Smita Krishnaswamy

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Kernel Scaling for Manifold Learning and Classification.

Ofir Lindenbaum, Moshe Salhov, Arie Yeredor, Amir Averbuch

CoRR, 2017

Multi-View Kernels for Low-Dimensional Modeling of Seismic Events.

Ofir Lindenbaum, Yuri Bregman, Neta Rabin, Amir Averbuch

CoRR, 2017

2016

Multi-View Kernel Consensus For Data Analysis and Signal Processing.

Moshe Salhov, Ofir Lindenbaum, Avi Silberschatz, Yoel Shkolnisky, Amir Averbuch

CoRR, 2016

Clustering Based on MultiView Diffusion Maps.

Ofir Lindenbaum, Arie Yeredor, Amir Averbuch

Proceedings of the IEEE International Conference on Data Mining Workshops, 2016

2015

Blind Separation of Orthogonal Mixtures of Spatially-Sparse Sources with Unknown Sparsity Levels and with Temporal Blocks.

Ofir Lindenbaum, Arie Yeredor, Ran Vitek, Moshe Mishali

J. Signal Process. Syst., 2015

Musical key extraction using diffusion maps.

Ofir Lindenbaum, Arie Yeredor, Israel Cohen

Signal Process., 2015

MultiView Diffusion Maps.

Ofir Lindenbaum, Arie Yeredor, Moshe Salhov, Amir Averbuch

CoRR, 2015

Learning Coupled Embedding Using MultiView Diffusion Maps.

Ofir Lindenbaum, Arie Yeredor, Moshe Salhov

Proceedings of the Latent Variable Analysis and Signal Separation, 2015

2013

Blind separation of spatially-block-sparse sources from orthogonal mixtures.

Ofir Lindenbaum, Arie Yeredor, Ran Vitek, Moshe Mishali

Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2013