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Ian Simon
Researcher at Google
Publications - 46
Citations - 5176
Ian Simon is an academic researcher from Google. The author has contributed to research in topics: Computer science & Transformer (machine learning model). The author has an hindex of 21, co-authored 40 publications receiving 4404 citations. Previous affiliations of Ian Simon include University of Washington & Microsoft.
Papers
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Proceedings ArticleDOI
Building Rome in a day
TL;DR: A system that can match and reconstruct 3D scenes from extremely large collections of photographs such as those found by searching for a given city on Internet photo sharing sites and is designed to scale gracefully with both the size of the problem and the amount of available computation.
Journal ArticleDOI
Building Rome in a day
Sameer Agarwal,Yasutaka Furukawa,Noah Snavely,Ian Simon,Brian Curless,Steven M. Seitz,Richard Szeliski +6 more
TL;DR: A system that can match and reconstruct 3D scenes from extremely large collections of photographs such as those found by searching for a given city on Internet photo sharing sites and is designed to scale gracefully with both the size of the problem and the amount of available computation.
Proceedings ArticleDOI
Scene Summarization for Online Image Collections
TL;DR: This work proposes a solution to the problem of scene summarization by examining the distribution of images in the collection to select a set of canonical views to form the scene summary, using clustering techniques on visual features.
Proceedings Article
Music Transformer: Generating Music with Long-Term Structure
Cheng-Zhi Anna Huang,Ashish Vaswani,Jakob Uszkoreit,Noam Shazeer,Ian Simon,Curtis Hawthorne,Andrew M. Dai,Matthew W. Hoffman,Monica Dinculescu,Douglas Eck +9 more
TL;DR: It is demonstrated that a Transformer with the modified relative attention mechanism can generate minutelong compositions with compelling structure, generate continuations that coherently elaborate on a given motif, and in a seq2seq setup generate accompaniments conditioned on melodies.
Posted Content
Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset.
Curtis Hawthorne,Andriy Stasyuk,Adam Roberts,Ian Simon,Cheng-Zhi Anna Huang,Sander Dieleman,Erich Elsen,Jesse Engel,Douglas Eck +8 more
TL;DR: By using notes as an intermediate representation, a suite of models capable of transcribing, composing, and synthesizing audio waveforms with coherent musical structure on timescales spanning six orders of magnitude are trained, a process the authors call Wave2Midi2Wave.