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ORCIDChaim Baskin
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- affiliation: Technion, Israel Institute of Technology, Israel
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2020 – today
- 2024
[j9]Klára Janousková, Tamir Shor, Chaim Baskin, Jiri Matas:
Single Image Test-Time Adaptation for Segmentation. Trans. Mach. Learn. Res. 2024 (2024)- [j8]Moshe Kimhi, Shai Kimhi, Evgenii Zheltonozhskii, Or Litany, Chaim Baskin:
Semi-Supervised Semantic Segmentation via Marginal Contextual Information. Trans. Mach. Learn. Res. 2024 (2024) - [c13]Gabriele Serussi, Tamir Shor, Tom Hirshberg, Chaim Baskin, Alex M. Bronstein:
Active propulsion noise shaping for multi-rotor aircraft localization. IROS 2024: 472-479 - [c12]Mitchell Keren Taraday, Almog David, Chaim Baskin:
Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks. NeurIPS 2024 - [i41]Gabriele Serussi, Tamir Shor, Tom Hirshberg, Chaim Baskin
, Alex M. Bronstein:
Active propulsion noise shaping for multi-rotor aircraft localization. CoRR abs/2402.17289 (2024) - [i40]Tamir Shor, Chaim Baskin, Alexander M. Bronstein:
Leveraging Latents for Efficient Thermography Classification and Segmentation. CoRR abs/2404.06589 (2024) - [i39]Zachary Bamberger, Ofek Glick, Chaim Baskin, Yonatan Belinkov:
DEPTH: Discourse Education through Pre-Training Hierarchically. CoRR abs/2405.07788 (2024) - [i38]Maor Dikter, Tsachi Blau, Chaim Baskin:
Conceptual Learning via Embedding Approximations for Reinforcing Interpretability and Transparency. CoRR abs/2406.08840 (2024) - [i37]Eden Grad, Moshe Kimhi, Lion Halika, Chaim Baskin:
Benchmarking Label Noise in Instance Segmentation: Spatial Noise Matters. CoRR abs/2406.10891 (2024) - [i36]Moshe Kimhi, David Vainshtein, Chaim Baskin, Dotan Di Castro:
Robot Instance Segmentation with Few Annotations for Grasping. CoRR abs/2407.01302 (2024) - [i35]Tamir Shor, Chaim Baskin, Alexander M. Bronstein:
TEAM PILOT - Learned Feasible Extendable Set of Dynamic MRI Acquisition Trajectories. CoRR abs/2409.12777 (2024) - [i34]Mitchell Keren Taraday, Almog David, Chaim Baskin:
Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks. CoRR abs/2409.19414 (2024) - [i33]Tsachi Blau, Moshe Kimhi, Yonatan Belinkov, Alexander M. Bronstein, Chaim Baskin:
Context-aware Prompt Tuning: Advancing In-Context Learning with Adversarial Methods. CoRR abs/2410.17222 (2024) - [i32]Moshe Kimhi, Idan Kashani, Avi Mendelson, Chaim Baskin:
Hysteresis Activation Function for Efficient Inference. CoRR abs/2411.10573 (2024) - [i31]Yaniv Nemcovsky, Avi Mendelson, Chaim Baskin:
Sparse patches adversarial attacks via extrapolating point-wise information. CoRR abs/2411.16162 (2024) - 2023
- [j7]Yaniv Nemcovsky, Evgenii Zheltonozhskii, Chaim Baskin, Brian Chmiel, Alex M. Bronstein, Avi Mendelson:
Adversarial robustness via noise injection in smoothed models. Appl. Intell. 53(8): 9483-9498 (2023) - [j6]Or Feldman, Amit Boyarski, Shai Feldman, Dani Kogan, Avi Mendelson, Chaim Baskin:
Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings. Trans. Mach. Learn. Res. 2023 (2023) - [c11]Mitchell Keren Taraday, Chaim Baskin:
Enhanced Meta Label Correction for Coping with Label Corruption. ICCV 2023: 16249-16258 - [c10]Itay Eilat, Ben Finkelshtein, Chaim Baskin, Nir Rosenfeld:
Strategic Classification with Graph Neural Networks. ICLR 2023 - [i30]Tsachi Blau, Roy Ganz, Chaim Baskin, Michael Elad, Alexander M. Bronstein:
Classifier Robustness Enhancement Via Test-Time Transformation. CoRR abs/2303.15409 (2023) - [i29]Mitchell Keren Taraday, Chaim Baskin:
Enhanced Meta Label Correction for Coping with Label Corruption. CoRR abs/2305.12961 (2023) - [i28]Moshe Kimhi, Shai Kimhi, Evgenii Zheltonozhskii, Or Litany, Chaim Baskin:
Semi-Supervised Semantic Segmentation via Marginal Contextual Information. CoRR abs/2308.13900 (2023) - [i27]Klára Janousková, Tamir Shor, Chaim Baskin, Jiri Matas:
Single Image Test-Time Adaptation for Segmentation. CoRR abs/2309.14052 (2023) - [i26]Or Feldman, Chaim Baskin:
Leveraging Temporal Graph Networks Using Module Decoupling. CoRR abs/2310.02721 (2023) - 2022
- [j5]Ben Finkelshtein, Chaim Baskin, Evgenii Zheltonozhskii, Uri Alon:
Single-node attacks for fooling graph neural networks. Neurocomputing 513: 1-12 (2022) - [j4]Tom Avrech, Evgenii Zheltonozhskii, Chaim Baskin, Ehud Rivlin:
GoToNet: Fast Monocular Scene Exposure and Exploration. J. Intell. Robotic Syst. 105(3): 65 (2022) - [c9]Yaniv Nemcovsky, Matan Jacoby, Alex M. Bronstein, Chaim Baskin:
Physical Passive Patch Adversarial Attacks on Visual Odometry Systems. ACCV (7) 2022: 518-534 - [c8]Ameen Ali, Tomer Galanti, Evgenii Zheltonozhskii, Chaim Baskin, Lior Wolf:
Weakly Supervised Discovery of Semantic Attributes. CLeaR 2022: 44-69 - [c7]Adam Botach, Evgenii Zheltonozhskii, Chaim Baskin:
End-to-End Referring Video Object Segmentation with Multimodal Transformers. CVPR 2022: 4975-4985 - [c6]Ben Finkelshtein, Chaim Baskin, Haggai Maron, Nadav Dym:
A Simple and Universal Rotation Equivariant Point-Cloud Network. TAG-ML 2022: 107-115 - [c5]Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson, Alex M. Bronstein, Or Litany:
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels. WACV 2022: 387-397 - [i25]Maxim Fishman, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Avi Mendelson:
On Recoverability of Graph Neural Network Representations. CoRR abs/2201.12843 (2022) - [i24]Or Feldman, Amit Boyarski, Shai Feldman, Dani Kogan, Avi Mendelson, Chaim Baskin:
Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings. CoRR abs/2201.13410 (2022) - [i23]Ben Finkelshtein, Chaim Baskin, Haggai Maron, Nadav Dym:
A Simple and Universal Rotation Equivariant Point-cloud Network. CoRR abs/2203.01216 (2022) - [i22]Tal Rozen, Moshe Kimhi, Brian Chmiel, Avi Mendelson, Chaim Baskin:
Bimodal Distributed Binarized Neural Networks. CoRR abs/2204.02004 (2022) - [i21]Moshe Kimhi, Tal Rozen, Tal Kopetz, Olya Sirkin, Avi Mendelson, Chaim Baskin:
FBM: Fast-Bit Allocation for Mixed-Precision Quantization. CoRR abs/2205.15437 (2022) - [i20]Itay Eilat, Ben Finkelshtein, Chaim Baskin, Nir Rosenfeld:
Strategic Classification with Graph Neural Networks. CoRR abs/2205.15765 (2022) - [i19]Tom Avrech, Evgenii Zheltonozhskii, Chaim Baskin, Ehud Rivlin:
GoToNet: Fast Monocular Scene Exposure and Exploration. CoRR abs/2206.05967 (2022) - [i18]Yaniv Nemcovsky, Matan Yaakoby, Alex M. Bronstein, Chaim Baskin:
Physical Passive Patch Adversarial Attacks on Visual Odometry Systems. CoRR abs/2207.05729 (2022) - 2021
- [b1]Chaim Baskin:
Designing Deep Neural Networks for Efficient and Robust Inference. Technion - Israel Institute of Technology, Israel, 2021 - [j3]Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson:
CAT: Compression-Aware Training for bandwidth reduction. J. Mach. Learn. Res. 22: 269:1-269:20 (2021) - [j2]Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson:
Loss aware post-training quantization. Mach. Learn. 110(11): 3245-3262 (2021) - [i17]Ameen Ali, Tomer Galanti, Evgeniy Zheltonozhskiy, Chaim Baskin, Lior Wolf:
Intersection Regularization for Extracting Semantic Attributes. CoRR abs/2103.11888 (2021) - [i16]Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson, Alex M. Bronstein, Or Litany:
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels. CoRR abs/2103.13646 (2021) - [i15]Adam Botach, Evgenii Zheltonozhskii, Chaim Baskin:
End-to-End Referring Video Object Segmentation with Multimodal Transformers. CoRR abs/2111.14821 (2021) - 2020
- [c4]Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Yevgeny Yermolin, Alex Karbachevsky, Alex M. Bronstein, Avi Mendelson:
Feature Map Transform Coding for Energy-Efficient CNN Inference. IJCNN 2020: 1-9 - [i14]Evgenii Zheltonozhskii, Chaim Baskin, Yaniv Nemcovsky, Brian Chmiel, Avi Mendelson, Alex M. Bronstein:
Colored Noise Injection for Training Adversarially Robust Neural Networks. CoRR abs/2003.02188 (2020) - [i13]Alex Karbachevsky, Chaim Baskin, Evgenii Zheltonozhskii, Yevgeny Yermolin, Freddy Gabbay, Alex M. Bronstein, Avi Mendelson:
HCM: Hardware-Aware Complexity Metric for Neural Network Architectures. CoRR abs/2004.08906 (2020) - [i12]Evgenii Zheltonozhskii, Chaim Baskin, Alex M. Bronstein, Avi Mendelson:
Self-Supervised Learning for Large-Scale Unsupervised Image Clustering. CoRR abs/2008.10312 (2020) - [i11]Ben Finkelshtein, Chaim Baskin, Evgenii Zheltonozhskii, Uri Alon:
Single-Node Attack for Fooling Graph Neural Networks. CoRR abs/2011.03574 (2020)
2010 – 2019
- 2019
- [j1]Chaim Baskin, Natan Liss, Eli Schwartz, Evgenii Zheltonozhskii, Raja Giryes, Alex M. Bronstein, Avi Mendelson:
UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks. ACM Trans. Comput. Syst. 37(1-4): 4:1-4:15 (2019) - [c3]Nir Diamant, Dean Zadok, Chaim Baskin, Eli Schwartz, Alexander M. Bronstein:
Beholder-Gan: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level. ICIP 2019: 739-743 - [i10]Nir Diamant, Dean Zadok, Chaim Baskin, Eli Schwartz, Alexander M. Bronstein:
Beholder-GAN: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level. CoRR abs/1902.02593 (2019) - [i9]Yochai Zur, Chaim Baskin, Evgenii Zheltonozhskii, Brian Chmiel, Itay Evron, Alexander M. Bronstein, Avi Mendelson:
Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks. CoRR abs/1904.09872 (2019) - [i8]Brian Chmiel, Chaim Baskin, Ron Banner, Evgenii Zheltonozhskii, Yevgeny Yermolin, Alex Karbachevsky, Alexander M. Bronstein, Avi Mendelson:
Feature Map Transform Coding for Energy-Efficient CNN Inference. CoRR abs/1905.10830 (2019) - [i7]Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii, Ron Banner, Alexander M. Bronstein, Avi Mendelson:
CAT: Compression-Aware Training for bandwidth reduction. CoRR abs/1909.11481 (2019) - [i6]Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alexander M. Bronstein, Avi Mendelson:
Loss Aware Post-training Quantization. CoRR abs/1911.07190 (2019) - [i5]Yaniv Nemcovsky, Evgenii Zheltonozhskii, Chaim Baskin, Brian Chmiel, Alexander M. Bronstein, Avi Mendelson:
Smoothed Inference for Adversarially-Trained Models. CoRR abs/1911.07198 (2019) - 2018
- [c2]Chaim Baskin, Natan Liss, Evgenii Zheltonozhskii, Alexander M. Bronstein, Avi Mendelson:
Streaming Architecture for Large-Scale Quantized Neural Networks on an FPGA-Based Dataflow Platform. IPDPS Workshops 2018: 162-169 - [i4]Chaim Baskin, Eli Schwartz, Evgenii Zheltonozhskii, Natan Liss, Raja Giryes, Alexander M. Bronstein, Avi Mendelson:
UNIQ: Uniform Noise Injection for the Quantization of Neural Networks. CoRR abs/1804.10969 (2018) - [i3]Chaim Baskin, Natan Liss, Yoav Chai, Evgenii Zheltonozhskii, Eli Schwartz, Raja Giryes, Avi Mendelson, Alexander M. Bronstein:
NICE: Noise Injection and Clamping Estimation for Neural Network Quantization. CoRR abs/1810.00162 (2018) - [i2]Natan Liss, Chaim Baskin, Avi Mendelson, Alexander M. Bronstein, Raja Giryes:
Efficient non-uniform quantizer for quantized neural network targeting reconfigurable hardware. CoRR abs/1811.10869 (2018) - 2017
- [c1]Evgeny Gershikov, Chaim Baskin:
Efficient Horizon Line Detection Using an Energy Function. RACS 2017: 110-115 - [i1]Chaim Baskin, Natan Liss, Avi Mendelson, Evgenii Zheltonozhskii:
Streaming Architecture for Large-Scale Quantized Neural Networks on an FPGA-Based Dataflow Platform. CoRR abs/1708.00052 (2017)
Coauthor Index
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