2 citations · 5 across the 10 of their papers we have counts for
4 papers · 1 filter
Diffusion Twigs with Loop Guidance for Conditional Graph Generation
Giangiacomo Mercatali, Yogesh Verma, Andre Freitas +1
We introduce a novel score-based diffusion framework named Twigs that incorporates multiple co-evolving flows for enriching conditional generation tasks. Specifically, a central or…
Topological Neural Networks go Persistent, Equivariant, and Continuous
Yogesh Verma, Amauri H Souza, Vikas Garg
Topological Neural Networks (TNNs) incorporate higher-order relational information beyond pairwise interactions, enabling richer representations than Graph Neural Networks (GNNs).…
ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs
Yogesh Verma, Markus Heinonen, Vikas Garg
Climate and weather prediction traditionally relies on complex numerical simulations of atmospheric physics. Deep learning approaches, such as transformers, have recently challenge…
E(3)-equivariant models cannot learn chirality: Field-based molecular generation
Alexandru Dumitrescu, Dani Korpela, Markus Heinonen +4
Obtaining the desired effect of drugs is highly dependent on their molecular geometries. Thus, the current prevailing paradigm focuses on 3D point-cloud atom representations, utili…