pwc-archive/papers-with-abstracts huggingface dataset:
Asynchronous Methods for Deep Reinforcement Learning
Authors: Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu
Date: 2016-02-04 00:00:00
Tasks: Atari Games, CPU, Deep Reinforcement Learning, GPU, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a conceptually simple and lightweight framework for deep
reinforcement learning that uses asynchronous gradient descent for optimization
of deep neural network controllers. We present asynchronous variants of four
standard reinforcement learning algorithms and show that parallel
actor-learners have a stabilizing effect on training allowing all four methods
to successfully train neural network controllers. The best performing method,
an asynchronous variant of actor-critic, surpasses the current state-of-the-art
on the Atari domain while training for half the time on a single multi-core CPU
instead of a GPU. Furthermore, we show that asynchronous actor-critic succeeds
on a wide variety of continuous motor control problems as well as on a new task
of navigating random 3D mazes using a visual input.
Automatic Text Scoring Using Neural Networks
Authors: Dimitrios Alikaniotis, Helen Yannakoudakis, Marek Rei
Date: 2016-06-14 00:00:00
Proceeding: ACL 2016 8

Automated Text Scoring (ATS) provides a cost-effective and consistent
alternative to human marking. However, in order to achieve good performance,
the predictive features of the system need to be manually engineered by human
experts. We introduce a model that forms word representations by learning the
extent to which specific words contribute to the text's score. Using Long-Short
Term Memory networks to represent the meaning of texts, we demonstrate that a
fully automated framework is able to achieve excellent results over similar
approaches. In an attempt to make our results more interpretable, and inspired
by recent advances in visualizing neural networks, we introduce a novel method
for identifying the regions of the text that the model has found more
discriminative.
The Mondrian Kernel
Authors: Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh
Date: 2016-06-16 00:00:00

We introduce the Mondrian kernel, a fast random feature approximation to the
Laplace kernel. It is suitable for both batch and online learning, and admits a
fast kernel-width-selection procedure as the random features can be re-used
efficiently for all kernel widths. The features are constructed by sampling
trees via a Mondrian process [Roy and Teh, 2009], and we highlight the
connection to Mondrian forests [Lakshminarayanan et al., 2014], where trees are
also sampled via a Mondrian process, but fit independently. This link provides
a new insight into the relationship between kernel methods and random forests.
Estimation of matrix trace using machine learning
Authors: Boram Yoon
Date: 2016-06-16 00:00:00
Tasks: BIG-bench Machine Learning, Form

We present a new trace estimator of the matrix whose explicit form is not
given but its matrix multiplication to a vector is available. The form of the
estimator is similar to the Hutchison stochastic trace estimator, but instead
of the random noise vectors in Hutchison estimator, we use small number of
probing vectors determined by machine learning. Evaluation of the quality of
estimates and bias correction are discussed. An unbiased estimator is proposed
for the calculation of the expectation value of a function of traces. In the
numerical experiments with random matrices, it is shown that the precision of
trace estimates with $\mathcal{O}(10)$ probing vectors determined by the
machine learning is similar to that with $\mathcal{O}(10000)$ random noise
vectors.
Learning feed-forward one-shot learners
Authors: Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip H. S. Torr, Andrea Vedaldi
Date: 2016-06-16 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Object Tracking, One-Shot Learning, Visual Object Tracking

One-shot learning is usually tackled by using generative models or
discriminative embeddings. Discriminative methods based on deep learning, which
are very effective in other learning scenarios, are ill-suited for one-shot
learning as they need large amounts of training data. In this paper, we propose
a method to learn the parameters of a deep model in one shot. We construct the
learner as a second deep network, called a learnet, which predicts the
parameters of a pupil network from a single exemplar. In this manner we obtain
an efficient feed-forward one-shot learner, trained end-to-end by minimizing a
one-shot classification objective in a learning to learn formulation. In order
to make the construction feasible, we propose a number of factorizations of the
parameters of the pupil network. We demonstrate encouraging results by learning
characters from single exemplars in Omniglot, and by tracking visual objects
from a single initial exemplar in the Visual Object Tracking benchmark.
How many faces can be recognized? Performance extrapolation for multi-class classification
Authors: Charles Y. Zheng, Rakesh Achanta, Yuval Benjamini
Date: 2016-06-16 00:00:00
Tasks: General Classification, Multi-class Classification, Optical Character Recognition, Optical Character Recognition (OCR)

The difficulty of multi-class classification generally increases with the
number of classes. Using data from a subset of the classes, can we predict how
well a classifier will scale with an increased number of classes? Under the
assumption that the classes are sampled exchangeably, and under the assumption
that the classifier is generative (e.g. QDA or Naive Bayes), we show that the
expected accuracy when the classifier is trained on $k$ classes is the $k-1$st
moment of a \emph{conditional accuracy distribution}, which can be estimated
from data. This provides the theoretical foundation for performance
extrapolation based on pseudolikelihood, unbiased estimation, and
high-dimensional asymptotics. We investigate the robustness of our methods to
non-generative classifiers in simulations and one optical character recognition
example.
False Discovery Rate Control and Statistical Quality Assessment of Annotators in Crowdsourced Ranking
Authors: Qianqian Xu, Jiechao Xiong, Xiaochun Cao, Yuan YAO
Date: 2016-05-19 00:00:00
Tasks: Position, Sociology

With the rapid growth of crowdsourcing platforms it has become easy and
relatively inexpensive to collect a dataset labeled by multiple annotators in a
short time. However due to the lack of control over the quality of the
annotators, some abnormal annotators may be affected by position bias which can
potentially degrade the quality of the final consensus labels. In this paper we
introduce a statistical framework to model and detect annotator's position bias
in order to control the false discovery rate (FDR) without a prior knowledge on
the amount of biased annotators - the expected fraction of false discoveries
among all discoveries being not too high, in order to assure that most of the
discoveries are indeed true and replicable. The key technical development
relies on some new knockoff filters adapted to our problem and new algorithms
based on the Inverse Scale Space dynamics whose discretization is potentially
suitable for large scale crowdsourcing data analysis. Our studies are supported
by experiments with both simulated examples and real-world data. The proposed
framework provides us a useful tool for quantitatively studying annotator's
abnormal behavior in crowdsourcing data arising from machine learning,
sociology, computer vision, multimedia, etc.
Interactive algorithms: from pool to stream
Authors: Sivan Sabato, Tom Hess
Date: 2016-02-02 00:00:00
Tasks: Active Learning, Binary Classification, General Classification

We consider interactive algorithms in the pool-based setting, and in the
stream-based setting. Interactive algorithms observe suggested elements
(representing actions or queries), and interactively select some of them and
receive responses. Pool-based algorithms can select elements at any order,
while stream-based algorithms observe elements in sequence, and can only select
elements immediately after observing them. We assume that the suggested
elements are generated independently from some source distribution, and ask
what is the stream size required for emulating a pool algorithm with a given
pool size. We provide algorithms and matching lower bounds for general pool
algorithms, and for utility-based pool algorithms. We further show that a
maximal gap between the two settings exists also in the special case of active
learning for binary classification.
Deep Reinforcement Learning Discovers Internal Models
Authors: Nir Baram, Tom Zahavy, Shie Mannor
Date: 2016-06-16 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep Reinforcement Learning (DRL) is a trending field of research, showing
great promise in challenging problems such as playing Atari, solving Go and
controlling robots. While DRL agents perform well in practice we are still
lacking the tools to analayze their performance. In this work we present the
Semi-Aggregated MDP (SAMDP) model. A model best suited to describe policies
exhibiting both spatial and temporal hierarchies. We describe its advantages
for analyzing trained policies over other modeling approaches, and show that
under the right state representation, like that of DQN agents, SAMDP can help
to identify skills. We detail the automatic process of creating it from
recorded trajectories, up to presenting it on t-SNE maps. We explain how to
evaluate its fitness and show surprising results indicating high compatibility
with the policy at hand. We conclude by showing how using the SAMDP model, an
extra performance gain can be squeezed from the agent.
Learning from Non-Stationary Stream Data in Multiobjective Evolutionary Algorithm
Authors: Jianyong Sun, Hu Zhang, Aimin Zhou, Qingfu Zhang
Date: 2016-06-16 00:00:00
Tasks: Clustering, Evolutionary Algorithms

Evolutionary algorithms (EAs) have been well acknowledged as a promising
paradigm for solving optimisation problems with multiple conflicting objectives
in the sense that they are able to locate a set of diverse approximations of
Pareto optimal solutions in a single run. EAs drive the search for approximated
solutions through maintaining a diverse population of solutions and by
recombining promising solutions selected from the population. Combining machine
learning techniques has shown great potentials since the intrinsic structure of
the Pareto optimal solutions of an multiobjective optimisation problem can be
learned and used to guide for effective recombination. However, existing
multiobjective EAs (MOEAs) based on structure learning spend too much
computational resources on learning. To address this problem, we propose to use
an online learning scheme. Based on the fact that offsprings along evolution
are streamy, dependent and non-stationary (which implies that the intrinsic
structure, if any, is temporal and scale-variant), an online agglomerative
clustering algorithm is applied to adaptively discover the intrinsic structure
of the Pareto optimal solution set; and to guide effective offspring
recombination. Experimental results have shown significant improvement over
five state-of-the-art MOEAs on a set of well-known benchmark problems with
complicated Pareto sets and complex Pareto fronts.
On higher order computations, rewiring the connectome, and non-von Neumann computer architecture
Authors: Stanislaw Ambroszkiewicz
Date: 2016-03-07 00:00:00

Structural plasticity in the brain (i.e. rewiring the connectome) may be viewed as mechanisms for dynamic reconfiguration of neural circuits. First order computations in the brain are done by static neural circuits, whereas higher order computations are done by dynamic reconfigurations of the links (synapses) between the neural circuits. Static neural circuits correspond to first order computable functions. Synapse creation (activation) between them correspond to the mathematical notion of function composition. Functionals are higher order functions that take functions as their arguments. The construction of functionals is based on dynamic reconfigurations of function compositions. Perhaps the functionals correspond to rewiring mechanisms of the connectome. The architecture of human mind is different than the von Neumann computer architecture. Higher order computations in the human brain (based on functionals) may suggest a non-von Neumann computer architecture, a challenge posed by John Backus in 1977 \cite{Backus}. The presented work is a substantial extension and revision of the paper published in Proc. ICANN2016.
Monocular Urban Localization using Street View
Authors: Li Yu, Cyril Joly, Guillaume Bresson, Fabien Moutarde
Date: 2016-05-17 00:00:00
Tasks: Pose Estimation

This paper presents a metric global localization in the urban environment
only with a monocular camera and the Google Street View database. We fully
leverage the abundant sources from the Street View and benefits from its
topo-metric structure to build a coarse-to-fine positioning, namely a
topological place recognition process and then a metric pose estimation by
local bundle adjustment. Our method is tested on a 3 km urban environment and
demonstrates both sub-meter accuracy and robustness to viewpoint changes,
illumination and occlusion. To our knowledge, this is the first work that
studies the global urban localization simply with a single camera and Street
View.
Robust Active Perception via Data-association aware Belief Space planning
Authors: Shashank Pathak, Antony Thomas, Asaf Feniger, Vadim Indelman
Date: 2016-06-16 00:00:00
Tasks: Autonomous Navigation

We develop a belief space planning (BSP) approach that advances the state of
the art by incorporating reasoning about data association (DA) within planning,
while considering additional sources of uncertainty. Existing BSP approaches
typically assume data association is given and perfect, an assumption that can
be harder to justify while operating, in the presence of localization
uncertainty, in ambiguous and perceptually aliased environments. In contrast,
our data association aware belief space planning (DA-BSP) approach explicitly
reasons about DA within belief evolution, and as such can better accommodate
these challenging real world scenarios. In particular, we show that due to
perceptual aliasing, the posterior belief becomes a mixture of probability
distribution functions, and design cost functions that measure the expected
level of ambiguity and posterior uncertainty. Using these and standard costs
(e.g.~control penalty, distance to goal) within the objective function, yields
a general framework that reliably represents action impact, and in particular,
capable of active disambiguation. Our approach is thus applicable to robust
active perception and autonomous navigation in perceptually aliased
environments. We demonstrate key aspects in basic and realistic simulations.
LSTM Neural Reordering Feature for Statistical Machine Translation
Authors: Yiming Cui, Shijin Wang, Jianfeng Li
Date: 2015-12-01 00:00:00
Proceeding: NAACL 2016 6
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

Artificial neural networks are powerful models, which have been widely
applied into many aspects of machine translation, such as language modeling and
translation modeling. Though notable improvements have been made in these
areas, the reordering problem still remains a challenge in statistical machine
translations. In this paper, we present a novel neural reordering model that
directly models word pairs and alignment. By utilizing LSTM recurrent neural
networks, much longer context could be learned for reordering prediction.
Experimental results on NIST OpenMT12 Arabic-English and Chinese-English
1000-best rescoring task show that our LSTM neural reordering feature is robust
and achieves significant improvements over various baseline systems.
Approximated and User Steerable tSNE for Progressive Visual Analytics
Authors: Nicola Pezzotti, Boudewijn P. F. Lelieveldt, Laurens van der Maaten, Thomas Höllt, Elmar Eisemann, Anna Vilanova
Date: 2015-12-05 00:00:00
Tasks: Dimensionality Reduction

Progressive Visual Analytics aims at improving the interactivity in existing
analytics techniques by means of visualization as well as interaction with
intermediate results. One key method for data analysis is dimensionality
reduction, for example, to produce 2D embeddings that can be visualized and
analyzed efficiently. t-Distributed Stochastic Neighbor Embedding (tSNE) is a
well-suited technique for the visualization of several high-dimensional data.
tSNE can create meaningful intermediate results but suffers from a slow
initialization that constrains its application in Progressive Visual Analytics.
We introduce a controllable tSNE approximation (A-tSNE), which trades off speed
and accuracy, to enable interactive data exploration. We offer real-time
visualization techniques, including a density-based solution and a Magic Lens
to inspect the degree of approximation. With this feedback, the user can decide
on local refinements and steer the approximation level during the analysis. We
demonstrate our technique with several datasets, in a real-world research
scenario and for the real-time analysis of high-dimensional streams to
illustrate its effectiveness for interactive data analysis.
Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage
Authors: Elena Erdmann, Karin Boczek, Lars Koppers, Gerret von Nordheim, Christian Pölitz, Alejandro Molina, Katharina Morik, Henrik Müller, Jörg Rahnenführer, Kristian Kersting
Date: 2016-06-16 00:00:00
Tasks: BIG-bench Machine Learning, Position

Migration crisis, climate change or tax havens: Global challenges need global
solutions. But agreeing on a joint approach is difficult without a common
ground for discussion. Public spheres are highly segmented because news are
mainly produced and received on a national level. Gain- ing a global view on
international debates about important issues is hindered by the enormous
quantity of news and by language barriers. Media analysis usually focuses only
on qualitative re- search. In this position statement, we argue that it is
imperative to pool methods from machine learning, journalism studies and
statistics to help bridging the segmented data of the international public
sphere, using the Transatlantic Trade and Investment Partnership (TTIP) as a
case study.
Machine Learning Across Cultures: Modeling the Adoption of Financial Services for the Poor
Authors: Muhammad Raza Khan, Joshua E. Blumenstock
Date: 2016-06-16 00:00:00
Tasks: BIG-bench Machine Learning

Recently, mobile operators in many developing economies have launched "Mobile
Money" platforms that deliver basic financial services over the mobile phone
network. While many believe that these services can improve the lives of the
poor, a consistent difficulty has been identifying individuals most likely to
benefit from access to the new technology. Here, we combine terabyte-scale data
from three different mobile phone operators from Ghana, Pakistan, and Zambia,
to better understand the behavioral determinants of mobile money adoption. Our
supervised learning models provide insight into the best predictors of adoption
in three very distinct cultures. We find that models fit on one population fail
to generalize to another, and in general are highly context-dependent. These
findings highlight the need for a nuanced approach to understanding the role
and potential of financial services for the poor.
Local Uncertainty Sampling for Large-Scale Multi-Class Logistic Regression
Authors: Lei Han, Kean Ming Tan, Ting Yang, Tong Zhang
Date: 2016-04-27 00:00:00
Tasks: regression

A major challenge for building statistical models in the big data era is that
the available data volume far exceeds the computational capability. A common
approach for solving this problem is to employ a subsampled dataset that can be
handled by available computational resources. In this paper, we propose a
general subsampling scheme for large-scale multi-class logistic regression and
examine the variance of the resulting estimator. We show that asymptotically,
the proposed method always achieves a smaller variance than that of the uniform
random sampling. Moreover, when the classes are conditionally imbalanced,
significant improvement over uniform sampling can be achieved. Empirical
performance of the proposed method is compared to other methods on both
simulated and real-world datasets, and these results match and confirm our
theoretical analysis.
Adversarial Perturbations Against Deep Neural Networks for Malware Classification
Authors: Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, Patrick McDaniel
Date: 2016-06-14 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, image-classification, Image Classification, Malware Classification, Malware Detection

Deep neural networks, like many other machine learning models, have recently
been shown to lack robustness against adversarially crafted inputs. These
inputs are derived from regular inputs by minor yet carefully selected
perturbations that deceive machine learning models into desired
misclassifications. Existing work in this emerging field was largely specific
to the domain of image classification, since the high-entropy of images can be
conveniently manipulated without changing the images' overall visual
appearance. Yet, it remains unclear how such attacks translate to more
security-sensitive applications such as malware detection - which may pose
significant challenges in sample generation and arguably grave consequences for
failure.
  In this paper, we show how to construct highly-effective adversarial sample
crafting attacks for neural networks used as malware classifiers. The
application domain of malware classification introduces additional constraints
in the adversarial sample crafting problem when compared to the computer vision
domain: (i) continuous, differentiable input domains are replaced by discrete,
often binary inputs; and (ii) the loose condition of leaving visual appearance
unchanged is replaced by requiring equivalent functional behavior. We
demonstrate the feasibility of these attacks on many different instances of
malware classifiers that we trained using the DREBIN Android malware data set.
We furthermore evaluate to which extent potential defensive mechanisms against
adversarial crafting can be leveraged to the setting of malware classification.
While feature reduction did not prove to have a positive impact, distillation
and re-training on adversarially crafted samples show promising results.
No penalty no tears: Least squares in high-dimensional linear models
Authors: Xiangyu Wang, David Dunson, Chenlei Leng
Date: 2015-06-07 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction

Ordinary least squares (OLS) is the default method for fitting linear models,
but is not applicable for problems with dimensionality larger than the sample
size. For these problems, we advocate the use of a generalized version of OLS
motivated by ridge regression, and propose two novel three-step algorithms
involving least squares fitting and hard thresholding. The algorithms are
methodologically simple to understand intuitively, computationally easy to
implement efficiently, and theoretically appealing for choosing models
consistently. Numerical exercises comparing our methods with penalization-based
approaches in simulations and data analyses illustrate the great potential of
the proposed algorithms.