About Me

I'm a part of the ELSC international graduate program. I conduct my research at Prof. Prof. Daphna's Weinshall Lab at the Hebrew University of Jerusalem.
My long-term goal is to understand how deep models interact with data. My recent research takes inspiration from the learning of humans and focuses on the importance of different sets of examples at various stages of the learning process of neural networks. My recent works include curriculum learning, active learning on low budgets, and characterization of the learning process of deep models.

Publications

• Pruning the Unlabeled Data to Improve Semi-Supervised Learning
  Guy Hacohen and Daphna Weinshall
  arXiv 2023
  [arXiv]
• Semi-Supervised Learning in the Few-Shot Zero-Shot Scenario
  Noam Fluss, Guy Hacohen and Daphna Weinshall
  arXiv 2023
  [arXiv]
• How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget
  Guy Hacohen and Daphna Weinshall
  NeurIPS 2023
  [arXiv]
• Active Learning Through a Covering Lens
  Ofer Yehuda, Avihu Dekel, Guy Hacohen and Daphna Weinshall
  NeurIPS 2022
  [arXiv]
• Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets
  Guy Hacohen, Avihu Dekel and Daphna Weinshall
  ICML 2022
  [arXiv] [Blog post] [GitHub] [Spotlight]
• The Grammar-Learning Trajectories of Neural Language Models
  Leshem Choshen, Guy Hacohen, Daphna Weinshall, Omri Abend
  ACL, 2022.
  [arXiv]
• Principal Components Bias in Over-parameterized Linear Models, and its Manifestation in Deep Neural Networks
  Guy Hacohen and Daphna Weinshall
  JMLR 2022
  [arXiv] [60 minutes lecture]
• Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets
  Guy Hacohen, Leshem Choshen and Daphna Weinshall
  ICML, 2020.
  [arXiv] [Oral Presentation]
• On The Power of Curriculum Learning in Training Deep Networks
  Guy Hacohen and Daphna Weinshall
  ICML, 2019.
  [arXiv] [GitHub] [Spotlight]