3 citations · 3 across the 6 of their papers we have counts for
5 papers · 1 filter
TensorLens: End-to-End Transformer Analysis via High-Order Attention Tensors
Ido Andrew Atad, Itamar Zimerman, Shahar Katz +1
Attention matrices are fundamental to transformer research, supporting a broad range of applications including interpretability, visualization, manipulation, and distillation. Yet,…
Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs
Roy Eisenstadt, Itamar Zimerman, Lior Wolf
Recently, techniques such as explicit structured reasoning have demonstrated strong test-time scaling behavior by enforcing a separation between the model's internal "thinking" pro…
Revisiting LRP: Positional Attribution as the Missing Ingredient for Transformer Explainability
Yarden Bakish, Itamar Zimerman, Hila Chefer +1
The development of effective explainability tools for Transformers is a crucial pursuit in deep learning research. One of the most promising approaches in this domain is Layer-wise…
On the Expressivity of Selective State-Space Layers: A Multivariate Polynomial Approach
Edo Cohen-Karlik, Itamar Zimerman, Liane Galanti +3
Recent advances in efficient sequence modeling have introduced selective state-space layers, a key component of the Mamba architecture, which have demonstrated remarkable success i…
On the Long Range Abilities of Transformers
Itamar Zimerman, Lior Wolf
Despite their dominance in modern DL and, especially, NLP domains, transformer architectures exhibit sub-optimal performance on long-range tasks compared to recent layers that are…