no code implementations • 23 Feb 2024 • Martin Benfeghoul, Umais Zahid, Qinghai Guo, Zafeirios Fountas
In an unfamiliar setting, a model-based reinforcement learning agent can be limited by the accuracy of its world model.
no code implementations • 22 Nov 2023 • Umais Zahid, Qinghai Guo, Zafeirios Fountas
We present a novel algorithm for parameter learning in generic deep generative models that builds upon the predictive coding (PC) framework of computational neuroscience.
no code implementations • 5 Apr 2023 • Umais Zahid, Qinghai Guo, Zafeirios Fountas
Backpropagation has rapidly become the workhorse credit assignment algorithm for modern deep learning methods.
no code implementations • 9 Mar 2023 • Umais Zahid, Qinghai Guo, Karl Friston, Zafeirios Fountas
In part, this has been due to the poor performance of models trained with PC when evaluated by both sample quality and marginal likelihood.
no code implementations • 29 Dec 2022 • Alexey Zakharov, Qinghai Guo, Zafeirios Fountas
The task of video prediction and generation is known to be notoriously difficult, with the research in this area largely limited to short-term predictions.
no code implementations • 25 Jul 2022 • Noor Sajid, Panagiotis Tigas, Zafeirios Fountas, Qinghai Guo, Alexey Zakharov, Lancelot Da Costa
These memories are selectively attended to, using attention and gating blocks, to update agent's preferences.
no code implementations • 14 Jan 2022 • Zafeirios Fountas, Alexey Zakharov
Enquiries concerning the underlying mechanisms and the emergent properties of a biological brain have a long history of theoretical postulates and experimental findings.
no code implementations • ICLR 2022 • Alexey Zakharov, Qinghai Guo, Zafeirios Fountas
Discovery and learning of an underlying spatiotemporal hierarchy in sequential data is an important topic for machine learning.
no code implementations • ICML Workshop URL 2021 • Alexey Zakharov, Matthew Crosby, Zafeirios Fountas
Planning in complex environments requires reasoning over multi-step timescales.
no code implementations • ICML Workshop URL 2021 • Noor Sajid, Panagiotis Tigas, Alexey Zakharov, Zafeirios Fountas, Karl Friston
In this paper, we pursue the notion that this learnt behaviour can be a consequence of reward-free preference learning that ensures an appropriate trade-off between exploration and preference satisfaction.
no code implementations • 3 Oct 2020 • Alexey Zakharov, Matthew Crosby, Zafeirios Fountas
In model-based learning, an agent's model is commonly defined over transitions between consecutive states of an environment even though planning often requires reasoning over multi-step timescales, with intermediate states either unnecessary, or worse, accumulating prediction error.
1 code implementation • NeurIPS 2020 • Zafeirios Fountas, Noor Sajid, Pedro A. M. Mediano, Karl Friston
In a more complex Animal-AI environment, our agents (using the same neural architecture) are able to simulate future state transitions and actions (i. e., plan), to evince reward-directed navigation - despite temporary suspension of visual input.
no code implementations • 26 Jan 2020 • Cong Bao, Zafeirios Fountas, Temitayo Olugbade, Nadia Bianchi-Berthouze
We propose a novel neural network architecture, named the Global Workspace Network (GWN), which addresses the challenge of dynamic and unspecified uncertainties in multimodal data fusion.