neubig

"""
DyNet implementation of a sequence labeler (POS taggger).
This is a translation of this tagger in PyTorch: https://gist.github.com/hal3/8c170c4400576eb8d0a8bd94ab231232

Basic architecture:
 - take words
 - run though bidirectional GRU
 - predict labels one word at a time (left to right), using a recurrent neural network "decoder"
The decoder updates hidden state based on:
 - most recent word

kyunghyuncho

"""
PyTorch implementation of a sequence labeler (POS taggger).

Basic architecture:
 - take words
 - run though bidirectional GRU
 - predict labels one word at a time (left to right), using a recurrent neural network "decoder"

The decoder updates hidden state based on:
 - most recent word

kkweon

"""
Simple policy gradient in Keras

"""
import gym
import numpy as np

from keras import layers
from keras.models import Model
from keras import backend as K

mokemokechicken

# coding: utf8
"""
* VAT: https://arxiv.org/abs/1507.00677

# 参考にしたCode
Original: https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py
VAT: https://github.com/musyoku/vat/blob/master/vat.py

results example
---------------

mbollmann

from keras import backend as K
from keras.layers.recurrent import LSTM

class HiddenStateLSTM(LSTM):
    """LSTM with input/output capabilities for its hidden state.

    This layer behaves just like an LSTM, except that it accepts further inputs
    to be used as its initial states, and returns additional outputs,
    representing the layer's final states.

fmassa

-- suppose you have a model called model
lrs_model = model:clone()
lrs = lrs_model:getParameters()
lrs:fill(1) -- setting the base learning rate to 1

-- now lets set the learning rate factor of the bias of module 5 to 2
lrs_model:get(5).bias:fill(2)
-- same thing for the weights of module 2, let's set them to 3
lrs_model:get(2).weight:fill(3)

yuyay

#! encoding=UTF-8
"""
kernel canonical correlation analysis
"""

import numpy as np
from scipy.linalg import svd
from sklearn.metrics.pairwise import pairwise_kernels, euclidean_distances

class KCCA(object):

alexland

import time
import numpy as NP
from redis import StrictRedis as redis

# a 2D array to serialize
A = 10 * NP.random.randn(10000).reshape(1000, 10)

# flatten the 2D NumPy array and save it as a binary string
array_dtype = str(A.dtype)

sangeeths

Go to Bitbucket and create a new repository (its better to have an empty repo)
    git clone [email protected]:abc/myforkedrepo.git
    cd myforkedrepo

Now add Github repo as a new remote in Bitbucket called "sync"
    git remote add sync [email protected]:def/originalrepo.git

Verify what are the remotes currently being setup for "myforkedrepo". This following command should show "fetch" and "push" for two remotes i.e. "origin" and "sync"
    git remote -v

bwhite

"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np