3 papers
cs.AI2026
Learning in Deep Networks under Dale's Constraint
Roy Abel, Shimon Ullman
Biologically plausible learning models aim to explain how neural circuits can implement effective learning under the constraints of real neurons. Although significant progress has…
cs.AI2025
Concepts Learned Visually by Infants Can Contribute to Visual Learning and Understanding in AI Models
Shify Treger, Shimon Ullman
Early in development, infants learn to extract surprisingly complex aspects of visual scenes. This early learning comes together with an initial understanding of the extracted conc…
cs.CV2023
Efficient Rehearsal Free Zero Forgetting Continual Learning using Adaptive Weight Modulation
Yonatan Sverdlov, Shimon Ullman
Artificial neural networks encounter a notable challenge known as continual learning, which involves acquiring knowledge of multiple tasks over an extended period. This challenge a…