11 citations · 24 across the 20 of their papers we have counts for
23 papers
Why the Brain Consolidates: Predictive Forgetting for Optimal Generalisation
Zafeirios Fountas, Adnan Oomerjee, Haitham Bou-Ammar +2
Standard accounts of memory consolidation emphasise the stabilisation of stored representations, but struggle to explain representational drift, semanticisation, or the necessity o…
A Brain-like Synergistic Core in LLMs Drives Behaviour and Learning
Pedro Urbina-Rodriguez, Zafeirios Fountas, Fernando E. Rosas +5
The independent evolution of intelligence in biological and artificial systems offers a unique opportunity to identify its fundamental computational principles. Here we show that l…
Emergent Bayesian Behaviour and Optimal Cue Combination in LLMs
Julian Ma, Jun Wang, Zafeirios Fountas
Large language models (LLMs) excel at explicit reasoning, but their implicit computational strategies remain underexplored. Decades of psychophysics research show that humans intui…
SuRe: Surprise-Driven Prioritised Replay for Continual LLM Learning
Hugo Hazard, Zafeirios Fountas, Martin A. Benfeghoul +3
Continual learning, one's ability to adapt to a sequence of tasks without forgetting previously acquired knowledge, remains a major challenge in machine learning and a key gap betw…
Subjective Depth and Timescale Transformers: Learning Where and When to Compute
Frederico Wieser, Martin Benfeghoul, Haitham Bou Ammar +2
The rigid, uniform allocation of computation in standard Transformer (TF) architectures can limit their efficiency and scalability, particularly for large-scale models and long seq…
Untangling Component Imbalance in Hybrid Linear Attention Conversion Methods
Martin Benfeghoul, Teresa Delgado, Adnan Oomerjee +3
Transformers' quadratic computational complexity limits their scalability despite remarkable performance. While linear attention reduces this to linear complexity, pre-training suc…