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Two papers released on arXiv, "Operator Variational Inference" and "Model Criticism for Bayesian Causal Inference" | Dustin T...
| | proceedings.neurips.cc
Hierarchical Implicit Models and Likelihood-Free Variational Inference
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| | resources.paperdigest.org
Paper Digest: NIPS 2018 Highlights - Resources | Paper Digest
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| | Download NIPS-2018-Paper-Digests.pdf- highlights of all NIPS-2018 papers. The Conference on Neural Information Processing Systems (NIPS) is one of the top machine learning conferences in the world. In 2018, it is to be held in Montreal, Canada. There were ~4,800 paper submissions, of which 1009
| | tiao.io
Batch Bayesian Optimisation via Density-ratio Estimation with Guarantees | Louis Tiao
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| | We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial attacks. We formulate a joint probabilistic model that considers a prior distribution over graphs along with a GCN-based likelihood and develop a stochastic variational inference algorithm to estimate the graph posterior and the GCN parameters jointly. To address the problem of propagating gradients through latent variables draw...
| | zserge.com
AI or ain't: Neural Networks
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| Neural network and deep learning introduction for those who skipped the math class but wants to follow the trend