publications

List of my publications in reversed chronological order. generated by jekyll-scholar.

2021

  1. arXiv
    End-to-End Referring Video Object Segmentation with Multimodal Transformers
    Adam Botach, Evgenii Zheltonozhskii, and Chaim Baskin
    arXiv pre-print 2021
  2. arXiv
    Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels
    Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson, Alex M. Bronstein, and Or Litany
    arXiv pre-print 2021
  3. arXiv
    Weakly Supervised Recovery of Semantic Attributes
    Ameen Ali, Tomer Galanti, Evgenii Zheltonozhskii, Chaim Baskin, and Lior Wolf
    arXiv pre-print 2021
  4. Early-Stage Neural Network Hardware Performance Analysis
    Alex Karbachevsky, Chaim Baskin, Evgenii Zheltonozhskii, Yevgeny Yermolin, Freddy Gabbay, Alex M. Bronstein, and Avi Mendelson
    Sustainability 2021
  5. ML
    Loss Aware Post-Training Quantization
    Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, and Avi Mendelson
    Machine Learning 2021
  6. CAT: Compression-Aware Training for Bandwidth Reduction
    Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, and Avi Mendelson
    Journal of Machine Learning Research 2021
  7. NICE: Noise Injection and Clamping Estimation for Neural Network Quantization
    Chaim Baskin, Evgenii Zheltonozhskii, Tal Rozen, Natan Liss, Yoav Chai, Eli Schwartz, Raja Giryes, Alexander M. Bronstein, and Avi Mendelson
    Mathematics 2021
  8. UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks
    Chaim Baskin, Natan Liss, Eli Schwartz, Evgenii Zheltonozhskii, Raja Giryes, Alex M. Bronstein, and Avi Mendelson
    ACM Transactions on Computer Systems 2021

2020

  1. arXiv
    Single-Node Attack for Fooling Graph Neural Networks
    Ben Finkelshtein, Chaim Baskin, Evgenii Zheltonozhskii, and Uri Alon
    arXiv pre-print 2020
  2. NeurIPS
    workshop
    Self-Supervised Learning for Large-Scale Unsupervised Image Clustering
    Evgenii Zheltonozhskii, Chaim Baskin, Alex M. Bronstein, and Avi Mendelson
    NeurIPS Self-Supervised Learning Workshop 2020
  3. arXiv
    Colored Noise Injection for Training Adversarially Robust Neural Networks
    Evgenii Zheltonozhskii, Chaim Baskin, Yaniv Nemcovsky, Brian Chmiel, Avi Mendelson, and Alex M. Bronstein
    arXiv pre-print 2020
  4. IJCNN Oral
    Feature Map Transform Coding for Energy-Efficient CNN Inference
    Brian Chmiel, Chaim Baskin, Ron Banner, Evgenii Zheltonozhskii, Yevgeny Yermolin, Alex Karbachevsky, Alex M. Bronstein, and Avi Mendelson
    In International Joint Conference on Neural Networks (IJCNN) 2020

2019

  1. arXiv
    Smoothed Inference for Adversarially-Trained Models
    Yaniv Nemcovsky, Evgenii Zheltonozhskii, Chaim Baskin, Brian Chmiel, Alex M. Bronstein, and Avi Mendelson
    arXiv pre-print 2019
  2. ICML
    workshop
    Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks
    Yochai Zur, Chaim Baskin, Evgenii Zheltonozhskii, Brian Chmiel, Itay Evron, Alex M. Bronstein, and Avi Mendelson
    ICML AutoML Workshop 2019

2018

  1. IPDPS
    workshop
    Streaming Architecture for Large-Scale Quantized Neural Networks on an FPGA-Based Dataflow Platform
    Chaim Baskin, Natan Liss, Evgenii Zheltonozhskii, Alex M. Bronstein, and Avi Mendelson
    In IEEE International Parallel and Distributed Processing Symposium Workshops 2018