Erfan Noury erfannoury

Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization	Francis Bach	https://papers.nips.cc/paper/7286-efficient-algorithms-for-non-convex-isotonic-regression-through-submodular-optimization
Structure-Aware Convolutional Neural Networks	Jianlong Chang	https://papers.nips.cc/paper/7287-structure-aware-convolutional-neural-networks
Kalman Normalization: Normalizing Internal Representations Across Network Layers	Guangrun Wang	https://papers.nips.cc/paper/7288-kalman-normalization-normalizing-internal-representations-across-network-layers
HOGWILD!-Gibbs can be PanAccurate	Constantinos Daskalakis	https://papers.nips.cc/paper/7289-hogwild-gibbs-can-be-panaccurate
Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language	Seonghyeon Nam	https://papers.nips.cc/paper/7290-text-adaptive-generative-adversarial-networks-manipulating-images-with-natural-language
IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis	Huaibo Huang	https

$ python script.py ps
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:200] Initialize GrpcChannelCache for job ps -> {0 -> localhost:9000}
I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:200] Initialize GrpcChannelCache

	import datetime
	import pytz
	from tensorflow.contrib.session_bundle import exporter
	from tensorflow.python.client import timeline
	import glob
	import json
	import time
	import math
	import numpy as np
	import os

	import numpy as np
	from keras.layers import GRU, initializations, K
	from collections import OrderedDict

	class GRULN(GRU):
	'''Gated Recurrent Unit with Layer Normalization
	Current impelemtation only works with consume_less = 'gpu' which is already
	set.

	# Arguments

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	import lasagne
	from lasagne.nonlinearities import rectify, softmax
	from lasagne.layers import InputLayer, DenseLayer, DropoutLayer, batch_norm, BatchNormLayer
	from lasagne.layers import ElemwiseSumLayer, NonlinearityLayer, GlobalPoolLayer
	from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer
	from lasagne.init import HeNormal

	def ResNet_FullPre_Wide(input_var=None, n=3, k=2):
	'''
	Adapted from https://github.com/Lasagne/Recipes/tree/master/papers/deep_residual_learning.