64 citations · 316 across the 72 of their papers we have counts for
5 papers · 2 filters
Data Augmentations in Deep Weight Spaces
Aviv Shamsian, David W. Zhang, Aviv Navon +10
Learning in weight spaces, where neural networks process the weights of other deep neural networks, has emerged as a promising research direction with applications in various field…
Latent Field Discovery In Interacting Dynamical Systems With Neural Fields
Miltiadis Kofinas, Erik J. Bekkers, Naveen Shankar Nagaraja +1
Systems of interacting objects often evolve under the influence of field effects that govern their dynamics, yet previous works have abstracted away from such effects, and assume t…
Learning Lie Group Symmetry Transformations with Neural Networks
Alex Gabel, Victoria Klein, Riccardo Valperga +4
The problem of detecting and quantifying the presence of symmetries in datasets is useful for model selection, generative modeling, and data analysis, amongst others. While existin…
BISCUIT: Causal Representation Learning from Binary Interactions
Phillip Lippe, Sara Magliacane, Sindy Löwe +3
Identifying the causal variables of an environment and how to intervene on them is of core value in applications such as robotics and embodied AI. While an agent can commonly inter…
Modulated Neural ODEs
Ilze Amanda Auzina, Çağatay Yıldız, Sara Magliacane +2
Neural ordinary differential equations (NODEs) have been proven useful for learning non-linear dynamics of arbitrary trajectories. However, current NODE methods capture variations…