40 citations · 72 across the 18 of their papers we have counts for
12 papers · 1 filter
Grow, Don't Overwrite: Fine-tuning Without Forgetting
Dyah Adila, Hanna Mazzawi, Benoit Dherin +1
Adapting pre-trained models to specialized tasks often leads to catastrophic forgetting, where new knowledge overwrites foundational capabilities. Existing methods either compromis…
Equivalence of Context and Parameter Updates in Modern Transformer Blocks
Adrian Goldwaser, Michael Munn, Javier Gonzalvo +1
Recent research has established that the impact of context in a vanilla transformer can be represented implicitly by forming a token-dependent, rank-1 patch to its MLP weights. Thi…
On residual network depth
Benoit Dherin, Michael Munn
Deep residual architectures, such as ResNet and the Transformer, have enabled models of unprecedented depth, yet a formal understanding of why depth is so effective remains an open…
Transmuting prompts into weights
Hanna Mazzawi, Benoit Dherin, Michael Munn +3
A growing body of research has demonstrated that the behavior of large language models can be effectively controlled at inference time by directly modifying their internal states,…
Learning by solving differential equations
Benoit Dherin, Michael Munn, Hanna Mazzawi +3
Modern deep learning algorithms use variations of gradient descent as their main learning methods. Gradient descent can be understood as the simplest Ordinary Differential Equation…
How iteration order influences convergence and stability in deep learning
Benoit Dherin, Benny Avelin, Anders Karlsson +3
Despite exceptional achievements, training neural networks remains computationally expensive and is often plagued by instabilities that can degrade convergence. While learning rate…