PEIRA: Learning Predictive Encoders through Inter-View Regressor Alignment
Non-contrastive self-supervised learning (SSL) is an effective framework for predictive representation learning, but popular (and in practice effective) methods such as SimSiam, BYOL, I-JEPA or DINO, which rely on a form of self-distillation to train a teacher-student network, remain poorly understood as they typically do not minimize a well-defined objective. We analyze the dynamics of a variant of the Joint Embedding Predictive Architecture (JEPA) using a regularized linear regressor to predic
Record details
Published: 17 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Children & education
Retrieved: 14 July 2026
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ethics.ai (17 May 2026), “PEIRA: Learning Predictive Encoders through Inter-View Regressor Alignment,” evidence record 4169, https://ethics.ai/record/4169 (originally published by arXiv).
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