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Generative models as data-driven priors: how to learn them e ffi - PowerPoint PPT Presentation

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sha1_base64="+SX5ghEw4dhNqJNmctBJ1YeqMs=">AB6HicdVDJSgNBEO2JW4xb1KOXxiB4GmYy0UluQS8eEzALJEPo6dQkbXoWunuEMOQLvHhQxKuf5M2/sbMIKvqg4PFeFVX1/IQzqSzrw8itrW9sbuW3Czu7e/sHxcOjtoxTQaFYx6Lrk8kcBZBSzHFoZsIKHPoeNPrud+5x6EZHF0q6YJeCEZRSxglCgtNbuDYsky7bLlOja2zIpdc6qOJpe1C7fqYtu0FihFRqD4nt/GNM0hEhRTqTs2VaivIwIxSiHWaGfSkgInZAR9DSNSAjSyxaHzvCZVoY4iIWuSOGF+n0iI6GU09DXnSFRY/nbm4t/eb1UBVUvY1GSKojoclGQcqxiP8aD5kAqvhUE0IF07diOiaCUKWzKegQvj7F/5N2bQds9yslOpXqzjy6ASdonNkIxfV0Q1qoBaiCNADekLPxp3xaLwYr8vWnLGaOUY/YLx9Ai+gjTQ=</latexit> Generative models as data-driven priors: how to learn them e ffi ciently? Vincent Schellekens & Laurent Jacques UCLouvain P ∗ A X z X x i ... b P X ' b P θ b P Z θ A A ( b P θ ) P θ P z G θ 1

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