Var-JEPA: A Variational Formulation of the Joint-Embedding Predictive Architecture -- Bridging Predictive and Generative Self-Supervised Learning

2026-03-20

AI Summary

This work addresses the disconnect between Joint Embedding Predictive Architectures (JEPA) and probabilistic generative modeling, as well as JEPA’s reliance on heuristic regularization to prevent representation collapse. From a variational inference perspective, we reinterpret JEPA as a deterministic special case of a coupled latent variable model and, for the first time, integrate it into a variational autoencoding framework, yielding Var-JEPA. This approach introduces an explicit generative structure that unifies predictive and generative self-supervised learning, enabling meaningful representations without heuristic anti-collapse regularizers and supporting uncertainty quantification in the latent space. Leveraging ELBO optimization and a context–target prediction architecture, we instantiate Var-T-JEPA for tabular data, which significantly outperforms T-JEPA on downstream tasks and matches the performance of strong baseline methods using raw features.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Reasoning under Uncertainty: Relational Probabilistic Models
• Computer Vision: Representation Learning for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The Joint-Embedding Predictive Architecture (JEPA) is often seen as a non-generative alternative to likelihood-based self-supervised learning, emphasizing prediction in representation space rather than reconstruction in observation space. We argue that the resulting separation from probabilistic generative modeling is largely rhetorical rather than structural: the canonical JEPA design, coupled encoders with a context-to-target predictor, mirrors the variational posteriors and learned conditional priors obtained when variational inference is applied to a particular class of coupled latent-variable models, and standard JEPA can be viewed as a deterministic specialization in which regularization is imposed via architectural and training heuristics rather than an explicit likelihood. Building on this view, we derive the Variational JEPA (Var-JEPA), which makes the latent generative structure explicit by optimizing a single Evidence Lower Bound (ELBO). This yields meaningful representations without ad-hoc anti-collapse regularizers and allows principled uncertainty quantification in the latent space. We instantiate the framework for tabular data (Var-T-JEPA) and achieve strong representation learning and downstream performance, consistently improving over T-JEPA while remaining competitive with strong raw-feature baselines.

Problem

• Joint-Embedding Predictive Architecture
• self-supervised learning
• generative modeling
• variational inference
• representation learning

Innovation

• Variational JEPA
• Joint-Embedding Predictive Architecture
• Evidence Lower Bound
• Self-Supervised Learning
• Latent Variable Models