Tim Vieira timvieira
-
Johns Hopkins University
- Baltimore, MD
- http://timvieira.github.com
View Exponential-jumps.ipynb

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View Notes-on-scipy.special.digamma.ipynb

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View lagrangeprop.py
# -*- coding: utf-8 -*- | |
""" | |
Backprop as the method of Lagrange multiplers (and even the implicit function | |
theorem). | |
""" | |
from __future__ import division | |
import numpy as np | |
from arsenal.alphabet import Alphabet | |
from arsenal.math.checkgrad import finite_difference |
View simple-backprop.py
""" | |
Simple example of manually performing "automatic" differentiation | |
""" | |
import numpy as np | |
from numpy import exp, sin, cos | |
def f(x, with_grad=False): | |
# Need to cache intermediates from forward pass (might not use all of them). | |
a = exp(x) |
View squishing_multiclass.py
# Efficient passive aggressive updates for multi-class classification | |
# | |
# Original article: | |
# "Column squishing for multiclass updates" | |
# https://nlpers.blogspot.com/2017/08/column-squishing-for-multiclass-updates.html | |
from __future__ import division | |
import numpy as np | |
import scipy.optimize |
View memory-efficient-backprop.py
""" | |
Memory-efficient backpropagation in an RNN. | |
Accompanies blog post: | |
http://timvieira.github.io/blog/post/2016/10/01/reversing-a-sequence-with-sublinear-space/ | |
""" | |
import numpy as np | |
from arsenal.math.checkgrad import fdcheck |
View memory-efficient-backprop.py
""" | |
Memory-efficient backpropagation in an RNN. | |
Accompanies blog post: | |
http://timvieira.github.io/blog/post/2016/10/01/reversing-a-sequence-with-sublinear-space/ | |
""" | |
import numpy as np | |
from arsenal.math.checkgrad import fdcheck |
View jiawei.py
""" | |
Cartoon version of Jiawei's optimization problem. | |
Created [2017-02-17 Fri] | |
""" | |
import numpy as np | |
from scipy.optimize import fmin_bfgs | |
import autograd |
View counterfactual-demo.ipynb

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View heap-sample.py
import numpy as np | |
from numpy.random import uniform | |
def update(S, k, v): | |
"Update value position `k` in time O(log n)." | |
d = S.shape[0] | |
i = d//2 + k | |
S[i] = v | |
while i > 0: |
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