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)

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)

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)

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)

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)

StarCoder: may the source be with you! - Raymond Li; Loubna Ben Allal; Yangtian Zi; Niklas Muennighoff; Denis Kocetkov; Chenghao Mou; Marc Marone; Christopher Akiki; Jia Li 015; Jenny Chim; Qian Liu 012; Evgenii Zheltonozhskii; et al. (Trans. Mach. Learn. Res., 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (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)

StarCoder 2 and The Stack v2: The Next Generation. - Anton Lozhkov; Raymond Li; Loubna Ben Allal; Federico Cassano; Joel Lamy-Poirier; Nouamane Tazi; Ao Tang; Dmytro Pykhtar; Jiawei Liu 004; Yuxiang Wei 003; Tianyang Liu 003; Max Tian; et al. (CoRR, 2024)

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