15.1k citations · 36.9k across the 152 of their papers we have counts for
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Efficient Neural PDE-Solvers using Quantization Aware Training
Winfried van den Dool, Tijmen Blankevoort, Max Welling +1
In the past years, the application of neural networks as an alternative to classical numerical methods to solve Partial Differential Equations has emerged as a potential paradigm s…
Rotating Features for Object Discovery
Sindy Löwe, Phillip Lippe, Francesco Locatello +1
The binding problem in human cognition, concerning how the brain represents and connects objects within a fixed network of neural connections, remains a subject of intense debate.…
Latent Traversals in Generative Models as Potential Flows
Yue Song, T. Anderson Keller, Nicu Sebe +1
Despite the significant recent progress in deep generative models, the underlying structure of their latent spaces is still poorly understood, thereby making the task of performing…
Geometric Clifford Algebra Networks
David Ruhe, Jayesh K. Gupta, Steven de Keninck +2
We propose Geometric Clifford Algebra Networks (GCANs) for modeling dynamical systems. GCANs are based on symmetry group transformations using geometric (Clifford) algebras. We fir…
Clifford Neural Layers for PDE Modeling
Johannes Brandstetter, Rianne van den Berg, Max Welling +1
Partial differential equations (PDEs) see widespread use in sciences and engineering to describe simulation of physical processes as scalar and vector fields interacting and coevol…
Bayesian Optimization for Macro Placement
Changyong Oh, Roberto Bondesan, Dana Kianfar +6
Macro placement is the problem of placing memory blocks on a chip canvas. It can be formulated as a combinatorial optimization problem over sequence pairs, a representation which d…