{"paper":{"title":"Surfing: Iterative optimization over incrementally trained deep networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ganlin Song, John Lafferty, Zhou Fan","submitted_at":"2019-07-19T19:16:04Z","abstract_excerpt":"We investigate a sequential optimization procedure to minimize the empirical risk functional $f_{\\hat\\theta}(x) = \\frac{1}{2}\\|G_{\\hat\\theta}(x) - y\\|^2$ for certain families of deep networks $G_{\\theta}(x)$. The approach is to optimize a sequence of objective functions that use network parameters obtained during different stages of the training process. When initialized with random parameters $\\theta_0$, we show that the objective $f_{\\theta_0}(x)$ is \"nice'' and easy to optimize with gradient descent. As learning is carried out, we obtain a sequence of generative networks $x \\mapsto G_{\\thet"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.08653","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}