Hello! I am a Research Scientist at IBM Research. I received my M.Sc. in Computer Science from Tel Aviv University, where I worked with Professor Lior Wolf.
I am interested in questions related to large‑scale models, such as scalable learning, architectural design, and optimization.
Derived relatively tight generalization bounds for sparse neural networks (e.g., convolutional networks), shedding light on the pivotal influence of the network’s sparsity on its ability to generalize. We empirically showed that our bound is significantly tighter than alternative generalization bounds for convolutional network from the literature.
Comparative Generalization Bounds for Deep Neural Networks
Observed that neural networks trained for classification exhibit a property in which Neural Collapse propagates backward from the penultimate layer to several preceding layers. We introduced the notion of “effective depth,” which captures the number of bottom layers in a network that do not experience neural collapse. We empirically demonstrated that a model’s effective depth is independent of its actual depth and adapts to the complexity of the data.
From Grounding to Planning: Benchmarking Bottlenecks in Web Agents with Mind2Web
Segev Shlomov, Ben Wiesel, Aviad Sela, Ido Levy, Liane Galanti, Roy Abitbol
We explored whether action planning or element grounding capabilities are more crucial for executing
action-based tasks from natural language queries (e.g., booking a flight, searching for content on Google).
Using the Mind2Web benchmark, we tested various agents, isolating and assessing the contributions of
planning and grounding capabilities to performance.
On the Expressivity of Selective State-Space Layers: A Multivariate Polynomial Approach
Edo Cohen-Karlik*, Itamar Zimerman*, Liane Galanti*, Ido Atad, Amir Globerson, Lior Wolf
We show that selective state-space layers are more expressive than linear transformers, enabling richer sequence representations while preserving generalization and computational efficiency.
