450 citations · 575 across the 28 of their papers we have counts for
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Exploring the Temperature-Dependent Phase Transition in Modern Hopfield Networks
Felix Koulischer, Cédric Goemaere, Tom van der Meersch +2
The recent discovery of a connection between Transformers and Modern Hopfield Networks (MHNs) has reignited the study of neural networks from a physical energy-based perspective. T…
Training a Hopfield Variational Autoencoder with Equilibrium Propagation
Tom Van Der Meersch, Johannes Deleu, Thomas Demeester
On dedicated analog hardware, equilibrium propagation is an energy-efficient alternative to backpropagation. In spite of its theoretical guarantees, its application in the AI domai…
Accelerating Hopfield Network Dynamics: Beyond Synchronous Updates and Forward Euler
Cédric Goemaere, Johannes Deleu, Thomas Demeester
The Hopfield network serves as a fundamental energy-based model in machine learning, capturing memory retrieval dynamics through an ordinary differential equation (ODE). The model'…
Block-wise Dynamic Sparseness
Amir Hadifar, Johannes Deleu, Chris Develder +1
Neural networks have achieved state of the art performance across a wide variety of machine learning tasks, often with large and computation-heavy models. Inducing sparseness as a…
Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning
Nasrin Sadeghianpourhamami, Johannes Deleu, Chris Develder
Initial DR studies mainly adopt model predictive control and thus require accurate models of the control problem (e.g., a customer behavior model), which are to a large extent unce…
Predefined Sparseness in Recurrent Sequence Models
Thomas Demeester, Johannes Deleu, Fréderic Godin +1
Inducing sparseness while training neural networks has been shown to yield models with a lower memory footprint but similar effectiveness to dense models. However, sparseness is ty…