Reda Boumasmoud

Finite precision in transformers can drastically alter memory operations, revealing a surprising hierarchy of expressivity based on attention mechanisms.

Causal evaluation reveals that relying on correlational analysis can lead to significant misinterpretations of task learnability in language models.

Recurrent neural networks can exhibit drastically different expressivity based on the arithmetic model used, revealing hidden capabilities tied to quantization methods.