arXiv AI
Generative AI for Validating Physics Laws
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Generative Learner for Distributional Causal Effects
arXiv:2503.17894v4 Announce Type: replace-cross Abstract: We propose a generative learner for estimating conditional average treatment effects and characterizing the full distribution of these effect...
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Optimal Mixture-of-Experts Model Averaging for Conditional Generative Models
arXiv:2607. 04360v1 Announce Type: cross Abstract: Conditional generative models have emerged as powerful tools for sampling from target conditional distributions, driving substantial advances across a wide range of scientific and applied domains.
Hugging Face Trending Papers
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Optimal Mixture-of-Experts Model Averaging for Conditional Generative Models
Conditional generative models have emerged as powerful tools for sampling from target conditional distributions, driving substantial advances across a wide range of scientific and applied domains. As these models proliferate, practitioners often face multiple plausible generators whose performance can vary with the task, data, or input condition.
arXiv Machine Learning
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Kastor: An efficient fine-tuning strategy for generative emulation of PDE simulations
arXiv:2608. 06107v1 Announce Type: new Abstract: Machine learning offers a promising avenue to accelerate physical simulations by replacing computationally expensive traditional Partial Differential Equation (PDE) solvers with fast, differentiable surrogate models.
arXiv Machine Learning
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Causal Variational Deep Embedding: A Family of Interventional Generators for Confounded Images
arXiv:2606. 21806v2 Announce Type: replace Abstract: Deep generative models reproduce the observational distribution of their training data, inheriting any spurious associations it contains.
arXiv Machine Learning
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MIST: Mutual Information Estimation Via Supervised Training
arXiv:2511. 18945v4 Announce Type: replace Abstract: We propose a fully data-driven approach to designing mutual information (MI) estimators.