Roy Henha Eyono

Roy Henha Eyono, Daniel Levenstein, Arna Ghosh et al.

Normalization is a critical operation in neural circuits. In the brain, there is evidence that normalization is implemented via inhibitory interneurons and allows neural populations to adjust to changes in the distribution of their inputs. In artificial neural networks (ANNs), normalization is used to improve learning in tasks that involve complex input distributions. However, it is unclear whether inhibition-mediated normalization in biological neural circuits also improves learning. Here, we explore this possibility using ANNs with separate excitatory and inhibitory populations trained on an image recognition task with variable luminosity. We find that inhibition-mediated normalization does not improve learning if normalization is applied only during inference. However, when this normalization is extended to include back-propagated errors, performance improves significantly. These results suggest that if inhibition-mediated normalization improves learning in the brain, it additionally requires the normalization of learning signals.

Roy Henha Eyono, Fabio Maria Carlucci, Pedro M Esperança et al.

State-of-the-art results in deep learning have been improving steadily, in good part due to the use of larger models. However, widespread use is constrained by device hardware limitations, resulting in a substantial performance gap between state-of-the-art models and those that can be effectively deployed on small devices. While Knowledge Distillation (KD) theoretically enables small student models to emulate larger teacher models, in practice selecting a good student architecture requires considerable human expertise. Neural Architecture Search (NAS) appears as a natural solution to this problem but most approaches can be inefficient, as most of the computation is spent comparing architectures sampled from the same distribution, with negligible differences in performance. In this paper, we propose to instead search for a family of student architectures sharing the property of being good at learning from a given teacher. Our approach AutoKD, powered by Bayesian Optimization, explores a flexible graph-based search space, enabling us to automatically learn the optimal student architecture distribution and KD parameters, while being 20x more sample efficient compared to existing state-of-the-art. We evaluate our method on 3 datasets; on large images specifically, we reach the teacher performance while using 3x less memory and 10x less parameters. Finally, while AutoKD uses the traditional KD loss, it outperforms more advanced KD variants using hand-designed students.

Luke Y. Prince, Roy Henha Eyono, Ellen Boven et al.

We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them with the efficiency of gradient-based learning in recurrent neural networks. The key issues discussed in this review include: synaptic plasticity, neural circuits, theory-experiment divide, and objective functions. We conclude with recommendations for both theoretical and experimental neuroscientists when designing new studies that could help bring clarity to these issues.

Roy Henha Eyono

This paper investigates how to effectively stop an attacker from using compromised user credentials to gain authorized entry to systems that they are otherwise not authorised to access. The proposed solution extends previous work to move beyond a risk-based multi-factor authentication system. It adds a behavioural analytics component that uses keystroke dynamics to grant or deny users access. Given the increasing number of compromised user credential stores, we make the assumption that criminals already know the user credentials. Hence, to test our solution, users were given authentic user credentials and asked to login to our proof-of-concept. Despite the fact that all illegitimate users in our test cases were given the correct user credentials for legitimate users, none of these were granted access by the system. This demonstrates zero- tolerance to false positives. The results demonstrate the uniqueness of keystroke dynamics and its use to prevent users with stolen credentials from accessing systems they are not authorized to access.