Efficient Horizon Line Detection Using an Energy Function. - Evgeny Gershikov; Chaim Baskin (RACS, 2017)

Streaming Architecture for Large-Scale Quantized Neural Networks on an FPGA-Based Dataflow Platform. - Chaim Baskin; Natan Liss; Avi Mendelson; Evgenii Zheltonozhskii (CoRR, 2017)

Streaming Architecture for Large-Scale Quantized Neural Networks on an FPGA-Based Dataflow Platform. - Chaim Baskin; Natan Liss; Evgenii Zheltonozhskii; Alexander M. Bronstein; Avi Mendelson (IPDPS Workshops, 2018)

UNIQ: Uniform Noise Injection for the Quantization of Neural Networks. - Chaim Baskin; Eli Schwartz; Evgenii Zheltonozhskii; Natan Liss; Raja Giryes; Alexander M. Bronstein; Avi Mendelson (CoRR, 2018)

NICE: Noise Injection and Clamping Estimation for Neural Network Quantization. - Chaim Baskin; Natan Liss; Yoav Chai; Evgenii Zheltonozhskii; Eli Schwartz; Raja Giryes; Avi Mendelson; Alexander M. Bronstein (CoRR, 2018)

Efficient non-uniform quantizer for quantized neural network targeting reconfigurable hardware. - Natan Liss; Chaim Baskin; Avi Mendelson; Alexander M. Bronstein; Raja Giryes (CoRR, 2018)

Beholder-Gan: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level. - Nir Diamant; Dean Zadok; Chaim Baskin; Eli Schwartz; Alexander M. Bronstein (ICIP, 2019)

Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks. - Yochai Zur; Chaim Baskin; Evgenii Zheltonozhskii; Brian Chmiel; Itay Evron; Alexander M. Bronstein; Avi Mendelson (CoRR, 2019)

Smoothed Inference for Adversarially-Trained Models. - Yaniv Nemcovsky; Evgenii Zheltonozhskii; Chaim Baskin; Brian Chmiel; Alexander M. Bronstein; Avi Mendelson (CoRR, 2019)

UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks. - Chaim Baskin; Natan Liss; Eli Schwartz; Evgenii Zheltonozhskii; Raja Giryes; Alex M. Bronstein; Avi Mendelson (ACM Trans. Comput. Syst., 2019)

Feature Map Transform Coding for Energy-Efficient CNN Inference. - Brian Chmiel; Chaim Baskin; Evgenii Zheltonozhskii; Ron Banner; Yevgeny Yermolin; Alex Karbachevsky; Alex M. Bronstein; Avi Mendelson (IJCNN, 2020)

Colored Noise Injection for Training Adversarially Robust Neural Networks. - Evgenii Zheltonozhskii; Chaim Baskin; Yaniv Nemcovsky; Brian Chmiel; Avi Mendelson; Alex M. Bronstein (CoRR, 2020)

HCM: Hardware-Aware Complexity Metric for Neural Network Architectures. - Alex Karbachevsky; Chaim Baskin; Evgenii Zheltonozhskii; Yevgeny Yermolin; Freddy Gabbay; Alex M. Bronstein; Avi Mendelson (CoRR, 2020)

Self-Supervised Learning for Large-Scale Unsupervised Image Clustering. - Evgenii Zheltonozhskii; Chaim Baskin; Alex M. Bronstein; Avi Mendelson (CoRR, 2020)

Single-Node Attack for Fooling Graph Neural Networks. - Ben Finkelshtein; Chaim Baskin; Evgenii Zheltonozhskii; Uri Alon 002 (CoRR, 2020)

Intersection Regularization for Extracting Semantic Attributes. - Ameen Ali; Tomer Galanti; Evgeniy Zheltonozhskiy; Chaim Baskin; Lior Wolf (CoRR, 2021)

CAT: Compression-Aware Training for bandwidth reduction. - Chaim Baskin; Brian Chmiel; Evgenii Zheltonozhskii; Ron Banner; Alex M. Bronstein; Avi Mendelson (J. Mach. Learn. Res., 2021)

Loss aware post-training quantization. - Yury Nahshan; Brian Chmiel; Chaim Baskin; Evgenii Zheltonozhskii; Ron Banner; Alex M. Bronstein; Avi Mendelson (Mach. Learn., 2021)

Designing Deep Neural Networks for Efficient and Robust Inference. - Chaim Baskin (2021)

Physical Passive Patch Adversarial Attacks on Visual Odometry Systems. - Yaniv Nemcovsky; Matan Jacoby; Alex M. Bronstein; Chaim Baskin (ACCV (7), 2022)

Weakly Supervised Discovery of Semantic Attributes. - Ameen Ali; Tomer Galanti; Evgenii Zheltonozhskii; Chaim Baskin; Lior Wolf (CLeaR, 2022)

End-to-End Referring Video Object Segmentation with Multimodal Transformers. - Adam Botach; Evgenii Zheltonozhskii; Chaim Baskin (CVPR, 2022)

A Simple and Universal Rotation Equivariant Point-Cloud Network. - Ben Finkelshtein; Chaim Baskin; Haggai Maron; Nadav Dym (TAG-ML, 2022)

Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels. - Evgenii Zheltonozhskii; Chaim Baskin; Avi Mendelson; Alex M. Bronstein; Or Litany (WACV, 2022)

On Recoverability of Graph Neural Network Representations. - Maxim Fishman; Chaim Baskin; Evgenii Zheltonozhskii; Ron Banner; Avi Mendelson (CoRR, 2022)

Bimodal Distributed Binarized Neural Networks. - Tal Rozen; Moshe Kimhi; Brian Chmiel; Avi Mendelson; Chaim Baskin (CoRR, 2022)

FBM: Fast-Bit Allocation for Mixed-Precision Quantization. - Moshe Kimhi; Tal Rozen; Tal Kopetz; Olya Sirkin; Avi Mendelson; Chaim Baskin (CoRR, 2022)

Physical Passive Patch Adversarial Attacks on Visual Odometry Systems. - Yaniv Nemcovsky; Matan Yaakoby; Alex M. Bronstein; Chaim Baskin (CoRR, 2022)

Single-node attacks for fooling graph neural networks. - Ben Finkelshtein; Chaim Baskin; Evgenii Zheltonozhskii; Uri Alon 002 (Neurocomputing, 2022)

GoToNet: Fast Monocular Scene Exposure and Exploration. - Tom Avrech; Evgenii Zheltonozhskii; Chaim Baskin; Ehud Rivlin (J. Intell. Robotic Syst., 2022)

Enhanced Meta Label Correction for Coping with Label Corruption. - Mitchell Keren Taraday; Chaim Baskin (ICCV, 2023)

Strategic Classification with Graph Neural Networks. - Itay Eilat; Ben Finkelshtein; Chaim Baskin; Nir Rosenfeld (ICLR, 2023)

Classifier Robustness Enhancement Via Test-Time Transformation. - Tsachi Blau; Roy Ganz; Chaim Baskin; Michael Elad; Alexander M. Bronstein (CoRR, 2023)

Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings. - Or Feldman; Amit Boyarski; Shai Feldman; Dani Kogan; Avi Mendelson; Chaim Baskin (Trans. Mach. Learn. Res., 2023)

Adversarial robustness via noise injection in smoothed models. - Yaniv Nemcovsky; Evgenii Zheltonozhskii; Chaim Baskin; Brian Chmiel; Alex M. Bronstein; Avi Mendelson (Appl. Intell., 2023)

Hysteresis Activation Function for Efficient Inference. - Moshe Kimhi; Idan Kashani; Chaim Baskin; Avi Mendelson (ENLSP, 2024)

Active propulsion noise shaping for multi-rotor aircraft localization. - Gabriele Serussi; Tamir Shor; Tom Hirshberg; Chaim Baskin; Alex M. Bronstein (IROS, 2024)

Leveraging Temporal Graph Networks Using Module Decoupling. - Or Feldman; Chaim Baskin (LoG, 2024)

Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks. - Mitchell Keren Taraday; Almog David; Chaim Baskin (NeurIPS, 2024)

Leveraging Latents for Efficient Thermography Classification and Segmentation. - Tamir Shor; Chaim Baskin; Alexander M. Bronstein (CoRR, 2024)

DEPTH: Discourse Education through Pre-Training Hierarchically. - Zachary Bamberger; Ofek Glick; Chaim Baskin; Yonatan Belinkov (CoRR, 2024)

Benchmarking Label Noise in Instance Segmentation: Spatial Noise Matters. - Eden Grad; Moshe Kimhi; Lion Halika; Chaim Baskin (CoRR, 2024)

TEAM PILOT - Learned Feasible Extendable Set of Dynamic MRI Acquisition Trajectories. - Tamir Shor; Chaim Baskin; Alexander M. Bronstein (CoRR, 2024)

Context-aware Prompt Tuning: Advancing In-Context Learning with Adversarial Methods. - Tsachi Blau; Moshe Kimhi; Yonatan Belinkov; Alexander M. Bronstein; Chaim Baskin (CoRR, 2024)

Sparse patches adversarial attacks via extrapolating point-wise information. - Yaniv Nemcovsky; Avi Mendelson; Chaim Baskin (CoRR, 2024)

Single Image Test-Time Adaptation for Segmentation. - Klára Janousková; Tamir Shor; Chaim Baskin; Jiri Matas (Trans. Mach. Learn. Res., 2024)

Semi-Supervised Semantic Segmentation via Marginal Contextual Information. - Moshe Kimhi; Shai Kimhi; Evgenii Zheltonozhskii; Or Litany; Chaim Baskin (Trans. Mach. Learn. Res., 2024)

DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations. - Krishna Sri Ipsit Mantri; Carola-Bibiane Schönlieb; Bruno Ribeiro 001; Chaim Baskin; Moshe Eliasof (CVPR, 2025)

Class-Conditioned Transformation for Enhanced Robust Image Classification. - Tsachi Blau; Roy Ganz; Chaim Baskin; Michael Elad; Alex M. Bronstein (WACV, 2025)

Conceptual Learning via Embedding Approximations for Reinforcing Interpretability and Transparency. - Maor Dikter; Tsachi Blau; Chaim Baskin (WACV, 2025)

Robot Instance Segmentation with Few Annotations for Grasping. - Moshe Kimhi; David Vainshtein; Chaim Baskin; Dotan Di Castro (WACV, 2025)

Enhancing Jailbreak Attacks via Compliance-Refusal-Based Initialization. - Amit Levi; Rom Himelstein; Yaniv Nemcovsky; Avi Mendelson; Chaim Baskin (CoRR, 2025)

Adversarial Robustness in Parameter-Space Classifiers. - Tamir Shor; Ethan Fetaya; Chaim Baskin; Alexander M. Bronstein (CoRR, 2025)

On Adversarial Attacks In Acoustic Drone Localization. - Tamir Shor; Chaim Baskin; Alexander M. Bronstein (CoRR, 2025)

T1-PILOT: Optimized Trajectories for T1 Mapping Acceleration. - Tamir Shor; Moti Freiman; Chaim Baskin; Alexander M. Bronstein (CoRR, 2025)

Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling. - Jonathan Fhima; Jan Van Eijgen; Lennert Beeckmans; Thomas Jacobs; Moti Freiman; Luis Filipe Nakayama; Ingeborg Stalmans; Chaim Baskin; Joachim A. Behar (CoRR, 2025)

FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks. - Or Feldman; Krishna Sri Ipsit Mantri; Carola-Bibiane Schönlieb; Chaim Baskin; Moshe Eliasof (CoRR, 2025)