Bayesian
Neural Processes
Marta Garnelo et al., 2018, arXiv Keyword: Bayesian, Process Problem: Data inefficiency, hard to train multiple datasets in one.
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Bayesian
Conditional Neural Processes
Marta Garnelo et al., 2018, arXiv Keyword: Bayesian, Process Problem: Weakness of knowledge sharing and data inefficiency of classical supervised learning Solution: Stochastic Process + NN Benefits: Data efficient, prior sharing Contribution: Encapsulation of parameterized NN function family.
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Portfolio
Work list
Skills Languages : Python : C++ [git+revsic/cpp-obfuscator, git+utilForever/RosettaStone] ML Framework : Tensorflow [git+revsic/tf-glow-tts, git+revsic/tf-diffwave] : PyTorch [git+revsic/torch-nansypp, git+revsic/torch-flow-models] : Jax/Flax [git+revsic/jax-variational-diffwave]
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Hi, I’m Young Joong Kim, a Master’s Student at Seoul National University, VGILab, and a Freelance Researcher at Theori.
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