Faculty profile
David Duvenaud
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Research
Latest papers
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.
ACS central science · 2018
Compositional inductive biases in function learning.
Cognitive psychology · 2017
Latest funding
- $225,000
Generative Models
NSERC · 2021 · Principal investigator
- $196,000
Efficient Inference in Compositional Generative Models
NSERC · 2017 · Principal investigator
- $120,000
Generative Modeling
NSERC · 2016 · Principal investigator
2 publications.
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.
Gómez-Bombarelli R, Wei JN, Duvenaud D, Hernández-Lobato JM, Sánchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel TD, Adams RP, Aspuru-Guzik A
Compositional inductive biases in function learning.
Schulz E, Tenenbaum JB, Duvenaud D, Speekenbrink M, Gershman SJ
Generative Models
Principal investigators: Duvenaud, David
Keywords: machine learning; generative models; deep learning; neural networks; approximate inference; differential equations
Efficient Inference in Compositional Generative Models
Principal investigators: Duvenaud, David
Generative Modeling
Principal investigators: Duvenaud, David
Generative Modeling
Principal investigators: Duvenaud, David
Generative Modeling
Principal investigators: Duvenaud, David
From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.
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