Hector Garcia Rodriguez

I am a research engineer at Huawei Technologies, where I do research on efficient deep learning.

I graduated with an MSc Machine Learning from University College London, where I was advised by Timoleon Moraitis and Pontus Stenetorp during my dissertation "A new recurrent unit with synaptic short-term plasticity". I was included in the Dean's List (Top 5%) and awarded a Distiction. Previously, I interned as a Software Development Engineer in Amazon Web Services, and obtained a BSc Theoretical Physics from University College London with First Class Honours.

LinkedIn  /  Twitter  /  Github

I'm interested in multimodal deep learning: improving efficiency using adaptable networks, and using more contextualised representations for sequential decision making tasks.

We train deep ConvNets with a Hebbian soft winner-take-all algorithm, multilayer SoftHebb. With an added linear probe, it achieves competitive results in image classification in CIFAR-10, STL-10 and ImageNet when compared to other biologically plausible baselines. SoftHebb increases biological compatibility, parallelisation and performance of state-of-the-art bio-plausible learning.

STPN is a recurrent neural network that improves Supervised and Reinforcement Learning by meta-learning to adapt its weights to the recent context, inspired by computational neuroscience. Additionally, STPN shows higher energy efficiency in a simulated neuromorphic implementation due to its optimised explicit forgetting mechanism.

Template