4 papers
On the Slow Convergence to Trivial Solutions of Algorithms for Hard Optimization Problems
Ali Hussaini Umar, Jean Barbier, Matthieu Jonckheere +1
Hard combinatorial optimization problems, many of which are NP-hard, present fundamental algorithmic challenges. Average-case analysis on random instances has emerged as a powerful…
BayesPrompt: human readable prompts that make sense
Franky Kevin Nando Tezoh, Ali Hussaini Umar, Alessandro Laio +2
Reconstructing prompts that can elicit a desired answer or behaviour in an LLM is an open and important research topic. Optimisation methods which aim at minimising the perplexity…
Signal-to-Noise Ratio and Sample Size Govern Representational Alignment in Neural Networks
Ali Hussaini Umar, Alessandro Laio
Neural networks are known to develop latent representations that are , namely structurally similar across networks trained with different architectures, training protocols…
The Effect of Label Noise on the Information Content of Neural Representations
Ali Hussaini Umar, Franky Kevin Nando Tezoh, Jean Barbier +2
In supervised classification tasks, models are trained to predict a label for each data point. In real-world datasets, these labels are often noisy due to annotation errors. While…