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Efficiently computes derivatives of numpy code.
Spearmint Bayesian optimization codebase
Convolutional nets which can take molecular graphs of arbitrary size as input.
Library of common tools for machine learning research.
Kayak is a library for automatic differentiation with applications to deep neural networks.
A project to enable optimization of molecules by transforming them to and from a continuous representation.
Dependent multinomials made easy: stick-breaking with the Pólya-gamma augmentation
Implementation in C and Theano of the method Probabilistic Backpropagation for scalable Bayesian inference in deep neural networks.
Implementation of an algorithm for Markov chain Monte Carlo with data subsampling
MCMC for the Dark Energy Spectroscopic Instrument
Fastidious accounting of entropy streams into and out of optimization and sampling algorithms.
Exploring differentiation with respect to hyperparameters
Gradient-based variational autoencoders to generate class-conditional natural images.
Linefeed-delimited pickle for Unix-style piping of arbitrary Python data
Website for viewing a git repo as a lab notebook. Figures and text files can be included with markdown-like syntax.
A Numpy wrapper that adds a gpufloat32 dtype to Numpy.
A python framework for fitting biophysical models to optically recorded neural signals.
LaTeX package for randomizing author order based on a public seed.
A simple abstraction layer for matrix computations in Python, making it easy to switch between CPU and NVIDIA or Intel coprocessors.
Github page for Harvard Intelligent Probabilistic Systems Group
Code for performing Bayesian regression with structured sparsity from a Gaussian field.