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ericmjl
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Bayesian Deep Learning: A Survey
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Notebooks about Bayesian methods for machine learning
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A PyTorch implementation of the NIPS 2017 paper "Dynamic Routing Between Capsules".
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Lightweight models for real-time semantic segmentationon PyTorch (include SQNet, LinkNet, SegNet, UNet, ENet, ERFNet, EDANet, ESPNet, ESPNetv2, LEDNet, ESNet, FSSNet, CG…
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Implementation of LambdaNetworks, a new approach to image recognition that reaches SOTA with less compute
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Bayesian modeling with PyMC 5: priors, likelihood, NUTS/ADVI sampling, diagnostics (R-hat, ESS), LOO/WAIC comparison, prediction. Hierarchical, logistic, GP variants; pr…
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Download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.
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T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
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A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.
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Deep and online learning with spiking neural networks in Python
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✨Fast Coreference Resolution in spaCy with Neural Networks