A Koopman Approach to Understanding Sequence Neural Models. - Ilan Naiman; Omri Azencot (CoRR, 2021)

An Operator Theoretic Approach for Analyzing Sequence Neural Networks. - Ilan Naiman; Omri Azencot (AAAI, 2023)

Multifactor Sequential Disentanglement via Structured Koopman Autoencoders. - Nimrod Berman; Ilan Naiman; Omri Azencot (ICLR, 2023)

Sample and Predict Your Latent: Modality-free Sequential Disentanglement via Contrastive Estimation. - Ilan Naiman; Nimrod Berman; Omri Azencot (ICML, 2023)

Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs. - Ilan Naiman; N. Benjamin Erichson; Pu Ren; Michael W. Mahoney; Omri Azencot (ICLR, 2024)

Sequential Disentanglement by Extracting Static Information From A Single Sequence Element. - Nimrod Berman; Ilan Naiman; Idan Arbiv; Gal Fadlon; Omri Azencot (ICML, 2024)

Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series. - Ilan Naiman; Nimrod Berman; Itai Pemper; Idan Arbiv; Gal Fadlon; Omri Azencot (NeurIPS, 2024)

Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling. - Pu Ren; Rie Nakata; Maxime Lacour; Ilan Naiman; Nori Nakata; Jialin Song; Zhengfa Bi; Osman Asif Malik; Dmitriy Morozov; Omri Azencot; N. Benjamin Erichson; Michael W. Mahoney (CoRR, 2024)

LV-MAE: Learning Long Video Representations through Masked-Embedding Autoencoders. - Ilan Naiman; Emanuel Ben Baruch; Oron Anschel; Alon Shoshan; Igor Kviatkovsky; Manoj Aggarwal; Gérard G. Medioni (CoRR, 2025)

One-Step Offline Distillation of Diffusion-based Models via Koopman Modeling. - Nimrod Berman; Ilan Naiman; Moshe Eliasof; Hedi Zisling; Omri Azencot (CoRR, 2025)

Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approach. - Tal Gonen; Itai Pemper; Ilan Naiman; Nimrod Berman; Omri Azencot (CoRR, 2025)