Evgenii Zheltonozhskii

According to our database, Evgenii Zheltonozhskii authored at least 25 papers between 2017 and 2024.

Collaborative distances:

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

2024

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, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian J. McAuley, Han Hu, Torsten Scholak, Sébastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, et al.

CoRR, 2024

2023

Adversarial robustness via noise injection in smoothed models.

Yaniv Nemcovsky, Evgenii Zheltonozhskii, Chaim Baskin, Brian Chmiel, Alex M. Bronstein, Avi Mendelson

Appl. Intell., April, 2023

Semi-Supervised Semantic Segmentation via Marginal Contextual Information.

Moshe Kimhi, Shai Kimhi, Evgenii Zheltonozhskii, Or Litany, Chaim Baskin

CoRR, 2023

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, Jenny Chim, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Benjamin Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy V, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Nour Moustafa-Fahmy, Urvashi Bhattacharyya, Wenhao Yu, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Tri Dao, Mayank Mishra, Alex Gu, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, Harm de Vries

CoRR, 2023

2022

GoToNet: Fast Monocular Scene Exposure and Exploration.

Tom Avrech, Evgenii Zheltonozhskii, Chaim Baskin, Ehud Rivlin

J. Intell. Robotic Syst., 2022

Single-node attacks for fooling graph neural networks.

Ben Finkelshtein, Chaim Baskin, Evgenii Zheltonozhskii, Uri Alon

Neurocomputing, 2022

On Recoverability of Graph Neural Network Representations.

Maxim Fishman, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Avi Mendelson

CoRR, 2022

Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels.

Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson, Alex M. Bronstein, Or Litany

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

End-to-End Referring Video Object Segmentation with Multimodal Transformers.

Adam Botach, Evgenii Zheltonozhskii, Chaim Baskin

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Weakly Supervised Discovery of Semantic Attributes.

Ameen Ali, Tomer Galanti, Evgenii Zheltonozhskii, Chaim Baskin, Lior Wolf

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

2021

Loss aware post-training quantization.

Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson

Mach. Learn., 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

Intersection Regularization for Extracting Semantic Attributes.

Ameen Ali, Tomer Galanti, Evgeniy Zheltonozhskiy, Chaim Baskin, Lior Wolf

CoRR, 2021

2020

Single-Node Attack for Fooling Graph Neural Networks.

Ben Finkelshtein, Chaim Baskin, Evgenii Zheltonozhskii, Uri Alon

CoRR, 2020

Self-Supervised Learning for Large-Scale Unsupervised Image Clustering.

Evgenii Zheltonozhskii, Chaim Baskin, Alex M. Bronstein, Avi Mendelson

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

Colored Noise Injection for Training Adversarially Robust Neural Networks.

Evgenii Zheltonozhskii, Chaim Baskin, Yaniv Nemcovsky, Brian Chmiel, Avi Mendelson, Alex M. Bronstein

CoRR, 2020

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

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

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

Smoothed Inference for Adversarially-Trained Models.

Yaniv Nemcovsky, Evgenii Zheltonozhskii, Chaim Baskin, Brian Chmiel, Alexander M. Bronstein, Avi Mendelson

CoRR, 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

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

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

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

Proceedings of the 2018 IEEE International Parallel and Distributed Processing Symposium Workshops, 2018

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