R
Ron Weiss
Researcher at Massachusetts Institute of Technology
Publications - 301
Citations - 110805
Ron Weiss is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Synthetic biology & Speech synthesis. The author has an hindex of 82, co-authored 292 publications receiving 89189 citations. Previous affiliations of Ron Weiss include French Institute for Research in Computer Science and Automation & Google.
Papers
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Proceedings ArticleDOI
Estimating Single-Channel Source Separation Masks: Relevance Vector Machine Classifiers vs. Pitch-Based Masking
Ron Weiss,Daniel P. W. Ellis +1 more
TL;DR: This work trains a Relevance Vector Machine (a probabilistic relative of the Support Vector Machine) to perform the classification problem of source separation, and compares the performance of this classifier both against SVMs, and against a traditional Computational Auditory Scene Analysis technique based on a noise-robust pitch tracker, which the RVM outperforms significantly.
Posted Content
Unsupervised Sound Separation Using Mixture Invariant Training
TL;DR: This paper proposes a completely unsupervised method, mixture invariant training (MixIT), that requires only single-channel acoustic mixtures and shows that MixIT can achieve competitive performance compared to supervised methods on speech separation.
Book ChapterDOI
Model checking genetic regulatory networks with parameter uncertainty
TL;DR: A method for the analysis of genetic regulatory networks with parameter uncertainty based on piecewise-multiaffine differential equations, dynamical properties expressed in temporal logic, and intervals for the values of uncertain parameters is proposed.
Proceedings ArticleDOI
Learning to rank recommendations with the k-order statistic loss
TL;DR: This work presents a new variant that more accurately optimizes precision at k, and a novel procedure of optimizing the mean maximum rank, which it is hypothesized is useful to more accurately cover all of the user's tastes.
Journal ArticleDOI
Accurate predictions of genetic circuit behavior from part characterization and modular composition.
Noah Davidsohn,Jacob Beal,Samira Kiani,Aaron Adler,Fusun Yaman,Yinqing Li,Zhen Xie,Ron Weiss +7 more
TL;DR: EQuIP's precision at predicting distributions of cell behaviors for six transcriptional cascades and three feed-forward circuits in mammalian cells is demonstrated and such accurate predictions will foster reliable forward engineering of complex biological circuits from libraries of standardized devices.