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cs.LG2026
How Many Different Outputs Can a Transformer Generate?
Maxime Meyer, Mario Michelessa, Caroline Chaux +1
We study how we can leverage only a handful of characteristics of a transformer's architecture to closely predict the number of different sequences it can output, both qualitativel…
cs.LG2025
Memory Limitations of Prompt Tuning in Transformers
Maxime Meyer, Mario Michelessa, Caroline Chaux +1
Despite the empirical success of prompt tuning in adapting pretrained language models to new tasks, theoretical analyses of its capabilities remain limited. Existing theoretical wo…
cs.LG2025
Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers
Elad Sofer, Tomer Shaked, Caroline Chaux +1
Machine learning (ML) models are often sensitive to carefully crafted yet seemingly unnoticeable perturbations. Such adversarial examples are considered to be a property of ML mode…