7 citations · 8 across the 6 of their papers we have counts for
7 papers
Recurrent Equivariant Constraint Modulation: Learning Per-Layer Symmetry Relaxation from Data
Stefanos Pertigkiozoglou, Mircea Petrache, Shubhendu Trivedi +1
Equivariant neural networks exploit underlying task symmetries to improve generalization, but strict equivariance constraints can induce more complex optimization dynamics that can…
Symmetries-enhanced Multi-Agent Reinforcement Learning
Nikolaos Bousias, Stefanos Pertigkiozoglou, Kostas Daniilidis +1
Multi-agent reinforcement learning has emerged as a powerful framework for enabling agents to learn complex, coordinated behaviors but faces persistent challenges regarding its gen…
BiEquiFormer: Bi-Equivariant Representations for Global Point Cloud Registration
Stefanos Pertigkiozoglou, Evangelos Chatzipantazis, Kostas Daniilidis
The goal of this paper is to address the problem of global point cloud registration (PCR) i.e., finding the optimal alignment between point clouds irrespective of the initial poses…
Improving Equivariant Model Training via Constraint Relaxation
Stefanos Pertigkiozoglou, Evangelos Chatzipantazis, Shubhendu Trivedi +1
Equivariant neural networks have been widely used in a variety of applications due to their ability to generalize well in tasks where the underlying data symmetries are known. Desp…
SE(3)-Equivariant Attention Networks for Shape Reconstruction in Function Space
Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Edgar Dobriban +1
We propose a method for 3D shape reconstruction from unoriented point clouds. Our method consists of a novel SE(3)-equivariant coordinate-based network (TF-ONet), that parametrizes…
Learning Augmentation Distributions using Transformed Risk Minimization
Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Kostas Daniilidis +1
We propose a new \emph{Transformed Risk Minimization} (TRM) framework as an extension of classical risk minimization. In TRM, we optimize not only over predictive models, but also…