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Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications
Julien Brandoit, Arthur Fyon, Damien Ernst +1
Sequence learning is dominated by Transformers and parallelizable recurrent neural networks (RNNs) such as state-space models, yet learning long-term dependencies remains challengi…
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning
Asad Bakija, Florent De Geeter, Julien Brandoit +2
In reinforcement learning (RL), agents acting in partially observable Markov decision processes (POMDPs) must rely on memory, typically encoded in a recurrent neural network (RNN),…
Context-dependent manifold learning: A neuromodulated constrained autoencoder approach
Jérôme Adriaens, Gustave Bainier, Guillaume Drion +1
Many physical systems exhibit a low-dimensional structure that varies with external parameters: link lengths in a robot, forcing constants in a fluid, or Reynolds numbers in a flow…
Parallelizable memory recurrent units
Florent De Geeter, Gaspard Lambrechts, Damien Ernst +1
With the emergence of massively parallel processing units, parallelization has become a desirable property for new sequence models. The ability to parallelize the processing of seq…
Introducing Neuromodulation in Deep Neural Networks to Learn Adaptive Behaviours
Nicolas Vecoven, Damien Ernst, Antoine Wehenkel +1
Animals excel at adapting their intentions, attention, and actions to the environment, making them remarkably efficient at interacting with a rich, unpredictable and ever-changing…