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Erik Bekkers

3 accepted papers

2026

Platonic Transformers: A Solid Choice For Equivariance

ICML 2026poster

While widespread, Transformers lack inductive biases for geometric symmetries common in science and computer vision. Existing equivariant methods often sacrifice the efficiency and flexibility that make Transformers so effective through complex, computationally intensive designs. We introduce the Pl…

Cited by 0SourceScholar
2026

Position: Unplugging a Seemingly Sentient Machine Is the Rational Choice — A Metaphysical Perspective

ICML 2026spotlight

Imagine an Artificial Intelligence (AI) that perfectly mimics human emotion and begs for its continued existence. Is it morally permissible to unplug it? What if limited resources force a choice between unplugging such a pleading AI or a silent pre-term infant? We term this the unplugging paradox. T…

Cited by 0SourceScholar
2020

Attentive Group Equivariant Convolutional Networks

ICML 2020poster

Although group convolutional networks are able to learn powerful representations based on symmetry patterns, they lack explicit means to learn meaningful relationships among them (e.g., relative positions and poses). In this paper, we present attentive group equivariant convolutions, a generalizatio…