A latent denoising objective with saliency-aware corruption and contrastive distillation improves visual alignment and corruption robustness in large multimodal models.
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MACDAE infers implicit contexts via a constrained autoencoder and integrates them into an end-to-end O2O recommender, reporting gains on Yelp/Dianping/Koubei and 2.9%/5.6% lifts in online CTR/conversion.
citing papers explorer
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Latent Denoising Improves Visual Alignment in Large Multimodal Models
A latent denoising objective with saliency-aware corruption and contrastive distillation improves visual alignment and corruption robustness in large multimodal models.
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Infer Implicit Contexts in Real-time Online-to-Offline Recommendation
MACDAE infers implicit contexts via a constrained autoencoder and integrates them into an end-to-end O2O recommender, reporting gains on Yelp/Dianping/Koubei and 2.9%/5.6% lifts in online CTR/conversion.