arturbekasov

• Deep Subspace Clustering Network, http://arxiv.org/abs/1709.02508v1
• Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization, http://arxiv.org/abs/1707.06468v2
• Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models, http://arxiv.org/abs/1606.06841v4
• Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning, http://arxiv.org/abs/1704.02882v2
• Parametric Simplex Method for Sparse Learning, http://arxiv.org/abs/1704.01079v1
• Group Sparse Additive Machine, http://arxiv.org/abs/1206.4673v1
• The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings, http://arxiv.org/abs/1703.00864v2
• Inferring Generative Model Structure with Static Analysis, http://arxiv.org/abs/1709.02477v1
• On Structured Prediction Theory with Calibrated Convex Surrogate Losses, http://arxiv.org/abs/1703.02403v2
• Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model

kmjjacobs

#%% (0) Important libraries
import tensorflow as tf
import numpy as np
from numpy import random
import matplotlib.pyplot as plt
from IPython import display
% matplotlib inline

#%% (1) Dataset creation.

killeent

A Tour of PyTorch Internals (Part I)

The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:

1. How does PyTorch extend the Python interpreter to define a Tensor type that can be manipulated from Python code?
2. How does PyTorch wrap the C libraries that actually define the Tensor's properties and methods?
3. How does PyTorch cwrap work to generate code for Tensor methods?
4. How does PyTorch's build system take all of these components to compile and generate a workable application?

Extending the Python Interpreter

PyTorch defines a new package torch. In this post we will consider the ._C module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor) and to call C/C++ functions.

lampts

# required tensorflow 0.12
# required gensim 0.13.3+ for new api model.wv.index2word or just use model.index2word

from gensim.models import Word2Vec
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector

# loading your gensim
model = Word2Vec.load("YOUR-MODEL")

siemanko

"""Short and sweet LSTM implementation in Tensorflow.

Motivation:
When Tensorflow was released, adding RNNs was a bit of a hack - it required
building separate graphs for every number of timesteps and was a bit obscure
to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
Currently the APIs are decent, but all the tutorials that I am aware of are not
making the best use of the new APIs.

Advantages of this implementation:

dansileshi

import tensorflow as tf
import numpy as np

FC_SIZE = 1024
DTYPE = tf.float32


def _weight_variable(name, shape):
    return tf.get_variable(name, shape, DTYPE, tf.truncated_normal_initializer(stddev=0.1))

albertomontesg

yosemitebandit

yosemitebandit

vsubhashini

##Sequence to Sequence -- Video to Text

Paper : ICCV 2015 PDF

Download Model: S2VT_VGG_RGB_MODEL (333MB)

Project Page

Description