4 citations · 7 across the 8 of their papers we have counts for
5 papers · 1 filter
Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction
Makoto Takamoto, Daniel Oñoro-Rubio, Wiem Ben Rim +2
Knowledge graph embedding (KGE) models encode the structural information of knowledge graphs to predicting new links. Effective training of these models requires distinguishing bet…
Geometric Kolmogorov-Arnold Superposition Theorem
Francesco Alesiani, Takashi Maruyama, Henrik Christiansen +1
The Kolmogorov-Arnold Theorem (KAT), or more generally, the Kolmogorov Superposition Theorem (KST), establishes that any non-linear multivariate function can be exactly represented…
Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing
Viktor Zaverkin, Francesco Alesiani, Takashi Maruyama +5
The ability to perform fast and accurate atomistic simulations is crucial for advancing the chemical sciences. By learning from high-quality data, machine-learned interatomic poten…
Compositional Generative Inverse Design
Tailin Wu, Takashi Maruyama, Long Wei +4
Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem that arises across fields such as mechanical…
Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching
Federico Errica, Henrik Christiansen, Viktor Zaverkin +3
Long-range interactions are essential for the correct description of complex systems in many scientific fields. The price to pay for including them in the calculations, however, is…