2 citations · 3 across the 4 of their papers we have counts for
6 papers
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…
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 without training: The implicit dynamics of in-context learning
Benoit Dherin, Michael Munn, Hanna Mazzawi +2
One of the most striking features of Large Language Models (LLMs) is their ability to learn in-context. Namely at inference time an LLM is able to learn new patterns without any ad…
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…
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…
The Impact of Geometric Complexity on Neural Collapse in Transfer Learning
Michael Munn, Benoit Dherin, Javier Gonzalvo
Many of the recent remarkable advances in computer vision and language models can be attributed to the success of transfer learning via the pre-training of large foundation models.…