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Michael Shvartsman (mshvartsman)

mshvartsman

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Company:@facebookresearch

Location:Seattle, WA

Home Page:http://mshvartsman.github.io/

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Michael Shvartsman's repositories

symbolic-mat-diff

Symbolic matrix differentiation using sympy

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vae-tf-bootcamp

VAE+Tensorflow bootcamp

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wfpt_py

Expressions relating to wiener first passage times (aka "diffusion decision model" in psychology)

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hyperopt

Distributed Asynchronous Hyperparameter Optimization in Python

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axcpt-psiturk-coffeescript

Implementation of AX-CPT in Coffeescript (with psiTurk)

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cddm

Code for a theory of decision making under dynamic context

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aepsych

AEPsych is a tool for adaptive experimentation in psychophysics and perception research, built on top of gpytorch and botorch.

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Ax

Adaptive Experimentation Platform

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brainiak

Brain Imaging Analysis Kit

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fairseq2

FAIR Sequence Modeling Toolkit 2

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gpytorch

A highly efficient and modular implementation of Gaussian Processes in PyTorch

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hddm

HDDM is a python module that implements Hierarchical Bayesian parameter estimation of Drift Diffusion Models (via PyMC).

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hydra

Hydra is a framework for elegantly configuring complex applications

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matnormal_aistats2018

experiments for matnormal paper AISTATS2018

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mshvartsman.github.io

Mike's website

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numpy

Numpy main repository

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optunity

optimization routines for hyperparameter tuning

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PNISpock-split-run-combine

Wrapper scripts for typical embarrasingly parallel workflows on PNI's spock cluster

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pyEPABC

A Python implementation of EP-ABC for likelihood-free, probabilistic inference.

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pymanopt

Python toolbox for optimization on manifolds with support for automatic differentiation

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pymdptoolbox

Markov Decision Process (MDP) Toolbox for Python

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pystan-cache

A PyStan wrapper that caches compiled models

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qmk_firmware

Open-source keyboard firmware for Atmel AVR and Arm USB families

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rand_tensor

This code package generates random tensors with user specified marginal means and covariances from one of two optional distributions. The first is the maximum-entropy-distribution with the specified marginal means and covariances. The second is the tensor-normal-distribution with the specified means and covariances.

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rPython

:exclamation: This is a read-only mirror of the CRAN R package repository. rPython — Package Allowing R to Call Python. Homepage: http://rpython.r-forge.r-project.org/

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RunDEMC

Python library for running DEMC on hierarchical models.

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RWiener

Clone of RWiener repository on SF: https://sourceforge.net/projects/rwiener/

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SRM

Shared Response Model (SRM) of NIPS 2015

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stan

Stan development repository (home page is linked below). The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.

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whoops_yall

Whoops ya'll is a psiTurk compatible experiment for paying people when an experiment goes badly for some reason. You enter the workerIds of people who you owe money to and can reject all others. Payment is handled quickly and easily via psiTurk's command line features. When you make a whoops, use "whoops ya'll"

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