mblondel

# Mathieu Blondel, September 2010
# License: BSD 3 clause

import numpy as np
from numpy import linalg
import cvxopt
import cvxopt.solvers

def linear_kernel(x1, x2):
    return np.dot(x1, x2)

halpo

Building a beamer presentation with knitr.

Introduction

The documents included are the input for knitr. In addition you need to have the tool pandoc installed. I also use a custom beamer template to add the University of Utah \institute command to the template. It also changes the indentation some.

Steps

1. knit document with

debasishg

1. Feature Learning

• Learning Feature Representations with K-means by Adam Coates and Andrew Y. Ng
• The devil is in the details: an evaluation of recent feature encoding methods by Chatfield et. al.
• Emergence of Object-Selective Features in Unsupervised Feature Learning by Coates, Ng
• Scaling Learning Algorithms towards AI Benjio & LeCun
• A Theory of Feature Learning by Brendan van Rooyen, Robert C. Williamson

2. Deep Learning

• Dropout: A Simple Way to Prevent Neural Networks from Overfitting by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov
• [Understanding

vene

""" Poisson-loss Factorization Machines with Numba

Follows the vanilla FM model from:
Steffen Rendle (2012): Factorization Machines with libFM.
In: ACM Trans. Intell. Syst. Technol., 3(3), May.
http://doi.acm.org/10.1145/2168752.2168771

See also: https://github.com/coreylynch/pyFM
"""

anj1

import types
import tensorflow as tf 
import numpy as np

# Expressions are represented as lists of lists,
# in lisp style -- the symbol name is the head (first element)
# of the list, and the arguments follow.

# add an expression to an expression list, recursively if necessary.
def add_expr_to_list(exprlist, expr):