49 citations · 70 across the 15 of their papers we have counts for
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cs.LG2025
ParaRNN: Unlocking Parallel Training of Nonlinear RNNs for Large Language Models
Federico Danieli, Pau Rodriguez, Miguel Sarabia +2
Recurrent Neural Networks (RNNs) laid the foundation for sequence modeling, but their intrinsic sequential nature restricts parallel computation, creating a fundamental barrier to…
cs.CL2025
LinEAS: End-to-end Learning of Activation Steering with a Distributional Loss
Pau Rodriguez, Michal Klein, Eleonora Gualdoni +5
The growing use of generative models in daily life calls for efficient mechanisms to control their generation, to e.g., produce safe content or provide users with tools to explore…