feat: joint embedding scvi#3884
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Add JointEmbeddingSCVI, a self-supervised SCVI variant that augments the standard ELBO with a cross-correlation objective (CCO) between the embedding of the data and a binomially-thinned view of it, encouraging embeddings that are robust to count dropout/noise. A VICReg-style variance term guards against dimension collapse. The joint-embedding loss is active only during training; evaluation (get_elbo / get_marginal_ll / get_reconstruction_error) falls back to the standard reweighted ELBO, and so does any minibatch with fewer than two cells. Contents: - src/scvi/module/_joint_embedding_utils.py: binomial thinning, per-cell thinning-probability sampling, cross-correlation and variance losses - src/scvi/module/_joint_embedding_vae.py: JointEmbeddingVAE module - src/scvi/model/_joint_embedding_scvi.py: JointEmbeddingSCVI model - tests/model/test_joint_embedding_scvi.py, tests/module/test_joint_embedding_utils.py - docs/api (user + developer) and CHANGELOG entries Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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## main #3884 +/- ##
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+ Coverage 88.45% 88.50% +0.04%
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Files 227 231 +4
Lines 22940 23035 +95
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+ Hits 20292 20386 +94
- Misses 2648 2649 +1
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This was referenced Jun 28, 2026
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