pwc-archive/papers-with-abstracts huggingface dataset:
GANGs: Generative Adversarial Network Games
Authors: Frans A. Oliehoek, Rahul Savani, Jose Gallego-Posada, Elise van der Pol, Edwin D. de Jong, Roderich Gross
Date: 2017-12-02 00:00:00
Tasks: Generative Adversarial Network

Generative Adversarial Networks (GAN) have become one of the most successful
frameworks for unsupervised generative modeling. As GANs are difficult to train
much research has focused on this. However, very little of this research has
directly exploited game-theoretic techniques. We introduce Generative
Adversarial Network Games (GANGs), which explicitly model a finite zero-sum
game between a generator ($G$) and classifier ($C$) that use mixed strategies.
The size of these games precludes exact solution methods, therefore we define
resource-bounded best responses (RBBRs), and a resource-bounded Nash
Equilibrium (RB-NE) as a pair of mixed strategies such that neither $G$ or $C$
can find a better RBBR. The RB-NE solution concept is richer than the notion of
`local Nash equilibria' in that it captures not only failures of escaping local
optima of gradient descent, but applies to any approximate best response
computations, including methods with random restarts. To validate our approach,
we solve GANGs with the Parallel Nash Memory algorithm, which provably
monotonically converges to an RB-NE. We compare our results to standard GAN
setups, and demonstrate that our method deals well with typical GAN problems
such as mode collapse, partial mode coverage and forgetting.
Towards a Deep Reinforcement Learning Approach for Tower Line Wars
Authors: Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Date: 2017-12-17 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft, Starcraft II

There have been numerous breakthroughs with reinforcement learning in the
recent years, perhaps most notably on Deep Reinforcement Learning successfully
playing and winning relatively advanced computer games. There is undoubtedly an
anticipation that Deep Reinforcement Learning will play a major role when the
first AI masters the complicated game plays needed to beat a professional
Real-Time Strategy game player. For this to be possible, there needs to be a
game environment that targets and fosters AI research, and specifically Deep
Reinforcement Learning. Some game environments already exist, however, these
are either overly simplistic such as Atari 2600 or complex such as Starcraft II
from Blizzard Entertainment. We propose a game environment in between Atari
2600 and Starcraft II, particularly targeting Deep Reinforcement Learning
algorithm research. The environment is a variant of Tower Line Wars from
Warcraft III, Blizzard Entertainment. Further, as a proof of concept that the
environment can harbor Deep Reinforcement algorithms, we propose and apply a
Deep Q-Reinforcement architecture. The architecture simplifies the state space
so that it is applicable to Q-learning, and in turn improves performance
compared to current state-of-the-art methods. Our experiments show that the
proposed architecture can learn to play the environment well, and score 33%
better than standard Deep Q-learning which in turn proves the usefulness of the
game environment.
A Pitfall of Unsupervised Pre-Training
Authors: Michele Alberti, Mathias Seuret, Rolf Ingold, Marcus Liwicki
Date: 2017-11-23 00:00:00
Tasks: Classification, General Classification, Unsupervised Pre-training

The point of this paper is to question typical assumptions in deep learning
and suggest alternatives. A particular contribution is to prove that even if a
Stacked Convolutional Auto-Encoder is good at reconstructing pictures, it is
not necessarily good at discriminating their classes. When using Auto-Encoders,
intuitively one assumes that features which are good for reconstruction will
also lead to high classification accuracy. Indeed, it became research practice
and is a suggested strategy by introductory books. However, we prove that this
is not always the case. We thoroughly investigate the quality of features
produced by Stacked Convolutional Auto-Encoders when trained to reconstruct
their input. In particular, we analyze the relation between the reconstruction
and classification capabilities of the network, if we were to use the same
features for both tasks. Experimental results suggest that in fact, there is no
correlation between the reconstruction score and the quality of features for a
classification task. This means, more formally, that the sub-dimension
representation space learned from the Stacked Convolutional Auto-Encoder (while
being trained for input reconstruction) is not necessarily better separable
than the initial input space. Furthermore, we show that the reconstruction
error is not a good metric to assess the quality of features, because it is
biased by the decoder quality. We do not question the usefulness of
pre-training, but we conclude that aiming for the lowest reconstruction error
is not necessarily a good idea if afterwards one performs a classification
task.
A Pitfall of Unsupervised Pre-Training
Authors: Michele Alberti, Mathias Seuret, Rolf Ingold, Marcus Liwicki
Date: 2017-03-13 00:00:00
Tasks: Classification, General Classification, Unsupervised Pre-training

The point of this paper is to question typical assumptions in deep learning
and suggest alternatives. A particular contribution is to prove that even if a
Stacked Convolutional Auto-Encoder is good at reconstructing pictures, it is
not necessarily good at discriminating their classes. When using Auto-Encoders,
intuitively one assumes that features which are good for reconstruction will
also lead to high classification accuracy. Indeed, it became research practice
and is a suggested strategy by introductory books. However, we prove that this
is not always the case. We thoroughly investigate the quality of features
produced by Stacked Convolutional Auto-Encoders when trained to reconstruct
their input. In particular, we analyze the relation between the reconstruction
and classification capabilities of the network, if we were to use the same
features for both tasks. Experimental results suggest that in fact, there is no
correlation between the reconstruction score and the quality of features for a
classification task. This means, more formally, that the sub-dimension
representation space learned from the Stacked Convolutional Auto-Encoder (while
being trained for input reconstruction) is not necessarily better separable
than the initial input space. Furthermore, we show that the reconstruction
error is not a good metric to assess the quality of features, because it is
biased by the decoder quality. We do not question the usefulness of
pre-training, but we conclude that aiming for the lowest reconstruction error
is not necessarily a good idea if afterwards one performs a classification
task.
Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study
Authors: Matteo Fischetti, Jason Jo
Date: 2017-12-17 00:00:00

Deep Neural Networks (DNNs) are very popular these days, and are the subject
of a very intense investigation. A DNN is made by layers of internal units (or
neurons), each of which computes an affine combination of the output of the
units in the previous layer, applies a nonlinear operator, and outputs the
corresponding value (also known as activation). A commonly-used nonlinear
operator is the so-called rectified linear unit (ReLU), whose output is just
the maximum between its input value and zero. In this (and other similar cases
like max pooling, where the max operation involves more than one input value),
one can model the DNN as a 0-1 Mixed Integer Linear Program (0-1 MILP) where
the continuous variables correspond to the output values of each unit, and a
binary variable is associated with each ReLU to model its yes/no nature. In
this paper we discuss the peculiarity of this kind of 0-1 MILP models, and
describe an effective bound-tightening technique intended to ease its solution.
We also present possible applications of the 0-1 MILP model arising in feature
visualization and in the construction of adversarial examples. Preliminary
computational results are reported, aimed at investigating (on small DNNs) the
computational performance of a state-of-the-art MILP solver when applied to a
known test case, namely, hand-written digit recognition.
Towards a science of human stories: using sentiment analysis and emotional arcs to understand the building blocks of complex social systems
Authors: Andrew J. Reagan
Date: 2017-12-17 00:00:00
Tasks: General Classification, Sentiment Analysis

Given the growing assortment of sentiment measuring instruments, it is
imperative to understand which aspects of sentiment dictionaries contribute to
both their classification accuracy and their ability to provide richer
understanding of texts. Here, we perform detailed, quantitative tests and
qualitative assessments of 6 dictionary-based methods applied, and briefly
examine a further 20 methods. We show that while inappropriate for sentences,
dictionary-based methods are generally robust in their classification accuracy
for longer texts.
  Stories often following distinct emotional trajectories, forming patterns
that are meaningful to us. By classifying the emotional arcs for a filtered
subset of 4,803 stories from Project Gutenberg's fiction collection, we find a
set of six core trajectories which form the building blocks of complex
narratives. Of profound scientific interest will be the degree to which we can
eventually understand the full landscape of human stories, and data driven
approaches will play a crucial role.
  Finally, we utilize web-scale data from Twitter to study the limits of what
social data can tell us about public health, mental illness, discourse around
the protest movement of #BlackLivesMatter, discourse around climate change, and
hidden networks. We conclude with a review of published works in complex
systems that separately analyze charitable donations, the happiness of words in
10 languages, 100 years of daily temperature data across the United States, and
Australian Rules Football games.
DeepNorm-A Deep Learning Approach to Text Normalization
Authors: Maryam Zare, Shaurya Rohatgi
Date: 2017-12-17 00:00:00
Tasks: Deep Learning, General Classification, GPU, Machine Translation, Text Normalization, Translation

This paper presents an simple yet sophisticated approach to the challenge by
Sproat and Jaitly (2016)- given a large corpus of written text aligned to its
normalized spoken form, train an RNN to learn the correct normalization
function. Text normalization for a token seems very straightforward without
it's context. But given the context of the used token and then normalizing
becomes tricky for some classes. We present a novel approach in which the
prediction of our classification algorithm is used by our sequence to sequence
model to predict the normalized text of the input token. Our approach takes
very less time to learn and perform well unlike what has been reported by
Google (5 days on their GPU cluster). We have achieved an accuracy of 97.62
which is impressive given the resources we use. Our approach is using the best
of both worlds, gradient boosting - state of the art in most classification
tasks and sequence to sequence learning - state of the art in machine
translation. We present our experiments and report results with various
parameter settings.
Generating and designing DNA with deep generative models
Authors: Nathan Killoran, Leo J. Lee, Andrew Delong, David Duvenaud, Brendan J. Frey
Date: 2017-12-17 00:00:00
Tasks: Generative Adversarial Network

We propose generative neural network methods to generate DNA sequences and
tune them to have desired properties. We present three approaches: creating
synthetic DNA sequences using a generative adversarial network; a DNA-based
variant of the activation maximization ("deep dream") design method; and a
joint procedure which combines these two approaches together. We show that
these tools capture important structures of the data and, when applied to
designing probes for protein binding microarrays, allow us to generate new
sequences whose properties are estimated to be superior to those found in the
training data. We believe that these results open the door for applying deep
generative models to advance genomics research.
Dynamic Boltzmann Machines for Second Order Moments and Generalized Gaussian Distributions
Authors: Rudy Raymond, Takayuki Osogami, Sakyasingha Dasgupta
Date: 2017-12-17 00:00:00
Tasks: Time Series, Time Series Analysis

Dynamic Boltzmann Machine (DyBM) has been shown highly efficient to predict
time-series data. Gaussian DyBM is a DyBM that assumes the predicted data is
generated by a Gaussian distribution whose first-order moment (mean)
dynamically changes over time but its second-order moment (variance) is fixed.
However, in many financial applications, the assumption is quite limiting in
two aspects. First, even when the data follows a Gaussian distribution, its
variance may change over time. Such variance is also related to important
temporal economic indicators such as the market volatility. Second, financial
time-series data often requires learning datasets generated by the generalized
Gaussian distribution with an additional shape parameter that is important to
approximate heavy-tailed distributions. Addressing those aspects, we show how
to extend DyBM that results in significant performance improvement in
predicting financial time-series data.
Super-sparse Learning in Similarity Spaces
Authors: Ambra Demontis, Marco Melis, Battista Biggio, Giorgio Fumera, Fabio Roli
Date: 2017-12-17 00:00:00
Tasks: General Classification, Sparse Learning

In several applications, input samples are more naturally represented in
terms of similarities between each other, rather than in terms of feature
vectors. In these settings, machine-learning algorithms can become very
computationally demanding, as they may require matching the test samples
against a very large set of reference prototypes. To mitigate this issue,
different approaches have been developed to reduce the number of required
reference prototypes. Current reduction approaches select a small subset of
representative prototypes in the space induced by the similarity measure, and
then separately train the classification function on the reduced subset.
However, decoupling these two steps may not allow reducing the number of
prototypes effectively without compromising accuracy. We overcome this
limitation by jointly learning the classification function along with an
optimal set of virtual prototypes, whose number can be either fixed a priori or
optimized according to application-specific criteria. Creating a super-sparse
set of virtual prototypes provides much sparser solutions, drastically reducing
complexity at test time, at the expense of a slightly increased complexity
during training. A much smaller set of prototypes also results in
easier-to-interpret decisions. We empirically show that our approach can reduce
up to ten times the complexity of Support Vector Machines, LASSO and ridge
regression at test time, without almost affecting their classification
accuracy.
Hypothesis Testing for High-Dimensional Multinomials: A Selective Review
Authors: Sivaraman Balakrishnan, Larry Wasserman
Date: 2017-12-17 00:00:00
Tasks: Two-sample testing, Vocal Bursts Intensity Prediction

The statistical analysis of discrete data has been the subject of extensive
statistical research dating back to the work of Pearson. In this survey we
review some recently developed methods for testing hypotheses about
high-dimensional multinomials. Traditional tests like the $\chi^2$ test and the
likelihood ratio test can have poor power in the high-dimensional setting. Much
of the research in this area has focused on finding tests with asymptotically
Normal limits and developing (stringent) conditions under which tests have
Normal limits. We argue that this perspective suffers from a significant
deficiency: it can exclude many high-dimensional cases when - despite having
non Normal null distributions - carefully designed tests can have high power.
Finally, we illustrate that taking a minimax perspective and considering
refinements of this perspective can lead naturally to powerful and practical
tests.
Integral Equations and Machine Learning
Authors: Alexander Keller, Ken Dahm
Date: 2017-12-17 00:00:00
Tasks: BIG-bench Machine Learning, Image Generation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

As both light transport simulation and reinforcement learning are ruled by
the same Fredholm integral equation of the second kind, reinforcement learning
techniques may be used for photorealistic image synthesis: Efficiency may be
dramatically improved by guiding light transport paths by an approximate
solution of the integral equation that is learned during rendering. In the
light of the recent advances in reinforcement learning for playing games, we
investigate the representation of an approximate solution of an integral
equation by artificial neural networks and derive a loss function for that
purpose. The resulting Monte Carlo and quasi-Monte Carlo methods train neural
networks with standard information instead of linear information and naturally
are able to generate an arbitrary number of training samples. The methods are
demonstrated for applications in light transport simulation.
Railway Track Specific Traffic Signal Selection Using Deep Learning
Authors: S. Ritika, Shruti Mittal, Dattaraj Rao
Date: 2017-12-17 00:00:00
Tasks: Deep Learning

With the railway transportation Industry moving actively towards automation,
accurate location and inventory of wayside track assets like traffic signals,
crossings, switches, mileposts, etc. is of extreme importance. With the new
Positive Train Control (PTC) regulation coming into effect, many railway safety
rules will be tied directly to location of assets like mileposts and signals.
Newer speed regulations will be enforced based on location of the Train with
respect to a wayside asset. Hence it is essential for the railroads to have an
accurate database of the types and locations of these assets. This paper talks
about a real-world use-case of detecting railway signals from a camera mounted
on a moving locomotive and tracking their locations. The camera is engineered
to withstand the environment factors on a moving train and provide a consistent
steady image at around 30 frames per second. Using advanced image analysis and
deep learning techniques, signals are detected in these camera images and a
database of their locations is created. Railway signals differ a lot from road
signals in terms of shapes and rules for placement with respect to track. Due
to space constraint and traffic densities in urban areas signals are not placed
on the same side of the track and multiple lines can run in parallel. Hence
there is need to associate signal detected with the track on which the train
runs. We present a method to associate the signals to the specific track they
belong to using a video feed from the front facing camera mounted on the lead
locomotive. A pipeline of track detection, region of interest selection, signal
detection has been implemented which gives an overall accuracy of 94.7% on a
route covering 150km with 247 signals.
Query-Based Abstractive Summarization Using Neural Networks
Authors: Johan Hasselqvist, Niklas Helmertz, Mikael Kågebäck
Date: 2017-12-17 00:00:00
Tasks: Abstractive Text Summarization

In this paper, we present a model for generating summaries of text documents
with respect to a query. This is known as query-based summarization. We adapt
an existing dataset of news article summaries for the task and train a
pointer-generator model using this dataset. The generated summaries are
evaluated by measuring similarity to reference summaries. Our results show that
a neural network summarization model, similar to existing neural network models
for abstractive summarization, can be constructed to make use of queries to
produce targeted summaries.
Can Machines Think in Radio Language?
Authors: Yujian Li
Date: 2017-10-07 00:00:00

People can think in auditory, visual and tactile forms of language, so can
machines principally. But is it possible for them to think in radio language?
According to a first principle presented for general intelligence, i.e. the
principle of language's relativity, the answer may give an exceptional solution
for robot astronauts to talk with each other in space exploration.
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning
Authors: Anubhav Ashok, Nicholas Rhinehart, Fares Beainy, Kris M. Kitani
Date: 2017-09-18 00:00:00
Proceeding: ICLR 2018 1
Tasks: Model Compression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

While bigger and deeper neural network architectures continue to advance the
state-of-the-art for many computer vision tasks, real-world adoption of these
networks is impeded by hardware and speed constraints. Conventional model
compression methods attempt to address this problem by modifying the
architecture manually or using pre-defined heuristics. Since the space of all
reduced architectures is very large, modifying the architecture of a deep
neural network in this way is a difficult task. In this paper, we tackle this
issue by introducing a principled method for learning reduced network
architectures in a data-driven way using reinforcement learning. Our approach
takes a larger `teacher' network as input and outputs a compressed `student'
network derived from the `teacher' network. In the first stage of our method, a
recurrent policy network aggressively removes layers from the large `teacher'
model. In the second stage, another recurrent policy network carefully reduces
the size of each remaining layer. The resulting network is then evaluated to
obtain a reward -- a score based on the accuracy and compression of the
network. Our approach uses this reward signal with policy gradients to train
the policies to find a locally optimal student network. Our experiments show
that we can achieve compression rates of more than 10x for models such as
ResNet-34 while maintaining similar performance to the input `teacher' network.
We also present a valuable transfer learning result which shows that policies
which are pre-trained on smaller `teacher' networks can be used to rapidly
speed up training on larger `teacher' networks.
"Zero-Shot" Super-Resolution using Deep Internal Learning
Authors: Assaf Shocher, Nadav Cohen, Michal Irani
Date: 2017-12-17 00:00:00
Tasks: Image Compression, Image Super-Resolution, Super-Resolution

Deep Learning has led to a dramatic leap in Super-Resolution (SR) performance
in the past few years. However, being supervised, these SR methods are
restricted to specific training data, where the acquisition of the
low-resolution (LR) images from their high-resolution (HR) counterparts is
predetermined (e.g., bicubic downscaling), without any distracting artifacts
(e.g., sensor noise, image compression, non-ideal PSF, etc). Real LR images,
however, rarely obey these restrictions, resulting in poor SR results by SotA
(State of the Art) methods. In this paper we introduce "Zero-Shot" SR, which
exploits the power of Deep Learning, but does not rely on prior training. We
exploit the internal recurrence of information inside a single image, and train
a small image-specific CNN at test time, on examples extracted solely from the
input image itself. As such, it can adapt itself to different settings per
image. This allows to perform SR of real old photos, noisy images, biological
data, and other images where the acquisition process is unknown or non-ideal.
On such images, our method outperforms SotA CNN-based SR methods, as well as
previous unsupervised SR methods. To the best of our knowledge, this is the
first unsupervised CNN-based SR method.