pwc-archive/papers-with-abstracts huggingface dataset:
Learning with Latent Language
Authors: Jacob Andreas, Dan Klein, Sergey Levine
Date: 2017-11-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: image-classification, Image Classification, Navigate, Reinforcement Learning

The named concepts and compositional operators present in natural language
provide a rich source of information about the kinds of abstractions humans use
to navigate the world. Can this linguistic background knowledge improve the
generality and efficiency of learned classifiers and control policies? This
paper aims to show that using the space of natural language strings as a
parameter space is an effective way to capture natural task structure. In a
pretraining phase, we learn a language interpretation model that transforms
inputs (e.g. images) into outputs (e.g. labels) given natural language
descriptions. To learn a new concept (e.g. a classifier), we search directly in
the space of descriptions to minimize the interpreter's loss on training
examples. Crucially, our models do not require language data to learn these
concepts: language is used only in pretraining to impose structure on
subsequent learning. Results on image classification, text editing, and
reinforcement learning show that, in all settings, models with a linguistic
parameterization outperform those without.
How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics
Authors: Anirudha Majumdar, Marco Pavone
Date: 2017-10-30 00:00:00
Tasks: Decision Making, Sequential Decision Making

Endowing robots with the capability of assessing risk and making risk-aware
decisions is widely considered a key step toward ensuring safety for robots
operating under uncertainty. But, how should a robot quantify risk? A natural
and common approach is to consider the framework whereby costs are assigned to
stochastic outcomes - an assignment captured by a cost random variable.
Quantifying risk then corresponds to evaluating a risk metric, i.e., a mapping
from the cost random variable to a real number. Yet, the question of what
constitutes a "good" risk metric has received little attention within the
robotics community. The goal of this paper is to explore and partially address
this question by advocating axioms that risk metrics in robotics applications
should satisfy in order to be employed as rational assessments of risk. We
discuss general representation theorems that precisely characterize the class
of metrics that satisfy these axioms (referred to as distortion risk metrics),
and provide instantiations that can be used in applications. We further discuss
pitfalls of commonly used risk metrics in robotics, and discuss additional
properties that one must consider in sequential decision making tasks. Our hope
is that the ideas presented here will lead to a foundational framework for
quantifying risk (and hence safety) in robotics applications.
Almost instant brain atlas segmentation for large-scale studies
Authors: Alex Fedorov, Eswar Damaraju, Vince Calhoun, Sergey Plis
Date: 2017-11-01 00:00:00

Large scale studies of group differences in healthy controls and patients and
screenings for early stage disease prevention programs require processing and
analysis of extensive multisubject datasets. Complexity of the task increases
even further when segmenting structural MRI of the brain into an atlas with
more than 50 regions. Current automatic approaches are time-consuming and
hardly scalable; they often involve many error prone intermediate steps and
don't utilize other available modalities. To alleviate these problems, we
propose a feedforward fully convolutional neural network trained on the output
produced by the state of the art models. Incredible speed due to available
powerful GPUs neural network makes this analysis much easier and faster (from
$>10$ hours to a minute). The proposed model is more than two orders of
magnitudes faster than the state of the art and yet as accurate. We have
evaluated the network's performance by comparing it with the state of the art
in the task of differentiating region volumes of healthy controls and patients
with schizophrenia on a dataset with 311 subjects. This comparison provides a
strong evidence that speed did not harm the accuracy. The overall quality may
also be increased by utilizing multi-modal datasets (not an easy task for other
models) by simple adding more modalities as an input. Our model will be useful
in large-scale studies as well as in clinical care solutions, where it can
significantly reduce delay between the patient screening and the result.
Sampling and multilevel coarsening algorithms for fast matrix approximations
Authors: Shashanka Ubaru, Yousef Saad
Date: 2017-11-01 00:00:00
Tasks: Dimensionality Reduction

This paper addresses matrix approximation problems for matrices that are
large, sparse and/or that are representations of large graphs. To tackle these
problems, we consider algorithms that are based primarily on coarsening
techniques, possibly combined with random sampling. A multilevel coarsening
technique is proposed which utilizes a hypergraph associated with the data
matrix and a graph coarsening strategy based on column matching. Theoretical
results are established that characterize the quality of the dimension
reduction achieved by a coarsening step, when a proper column matching strategy
is employed. We consider a number of standard applications of this technique as
well as a few new ones. Among the standard applications we first consider the
problem of computing the partial SVD for which a combination of sampling and
coarsening yields significantly improved SVD results relative to sampling
alone. We also consider the Column subset selection problem, a popular low rank
approximation method used in data related applications, and show how multilevel
coarsening can be adapted for this problem. Similarly, we consider the problem
of graph sparsification and show how coarsening techniques can be employed to
solve it. Numerical experiments illustrate the performances of the methods in
various applications.
Efficient Low-Order Approximation of First-Passage Time Distributions
Authors: David Schnoerr, Botond Cseke, Ramon Grima, Guido Sanguinetti
Date: 2017-06-01 00:00:00
Tasks: Bayesian Inference, Sequential Bayesian Inference

We consider the problem of computing first-passage time distributions for
reaction processes modelled by master equations. We show that this generally
intractable class of problems is equivalent to a sequential Bayesian inference
problem for an auxiliary observation process. The solution can be approximated
efficiently by solving a closed set of coupled ordinary differential equations
(for the low-order moments of the process) whose size scales with the number of
species. We apply it to an epidemic model and a trimerisation process, and show
good agreement with stochastic simulations.
Intelligent Parameter Tuning in Optimization-based Iterative CT Reconstruction via Deep Reinforcement Learning
Authors: Chenyang Shen, Yesenia Gonzalez, Liyuan Chen, Steve B. Jiang, Xun Jia
Date: 2017-11-01 00:00:00
Tasks: CT Reconstruction, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A number of image-processing problems can be formulated as optimization
problems. The objective function typically contains several terms specifically
designed for different purposes. Parameters in front of these terms are used to
control the relative weights among them. It is of critical importance to tune
these parameters, as quality of the solution depends on their values. Tuning
parameter is a relatively straightforward task for a human, as one can
intelligently determine the direction of parameter adjustment based on the
solution quality. Yet manual parameter tuning is not only tedious in many
cases, but becomes impractical when a number of parameters exist in a problem.
Aiming at solving this problem, this paper proposes an approach that employs
deep reinforcement learning to train a system that can automatically adjust
parameters in a human-like manner. We demonstrate our idea in an example
problem of optimization-based iterative CT reconstruction with a pixel-wise
total-variation regularization term. We set up a parameter tuning policy
network (PTPN), which maps an CT image patch to an output that specifies the
direction and amplitude by which the parameter at the patch center is adjusted.
We train the PTPN via an end-to-end reinforcement learning procedure. We
demonstrate that under the guidance of the trained PTPN for parameter tuning at
each pixel, reconstructed CT images attain quality similar or better than in
those reconstructed with manually tuned parameters.
Knowledge Representation in Bicategories of Relations
Authors: Evan Patterson
Date: 2017-06-02 00:00:00

We introduce the relational ontology log, or relational olog, a knowledge
representation system based on the category of sets and relations. It is
inspired by Spivak and Kent's olog, a recent categorical framework for
knowledge representation. Relational ologs interpolate between ologs and
description logic, the dominant formalism for knowledge representation today.
In this paper, we investigate relational ologs both for their own sake and to
gain insight into the relationship between the algebraic and logical approaches
to knowledge representation. On a practical level, we show by example that
relational ologs have a friendly and intuitive--yet fully precise--graphical
syntax, derived from the string diagrams of monoidal categories. We explain
several other useful features of relational ologs not possessed by most
description logics, such as a type system and a rich, flexible notion of
instance data. In a more theoretical vein, we draw on categorical logic to show
how relational ologs can be translated to and from logical theories in a
fragment of first-order logic. Although we make extensive use of categorical
language, this paper is designed to be self-contained and has considerable
expository content. The only prerequisites are knowledge of first-order logic
and the rudiments of category theory.
Building Data-driven Models with Microstructural Images: Generalization and Interpretability
Authors: Julia Ling, Maxwell Hutchinson, Erin Antono, Brian DeCost, Elizabeth A. Holm, Bryce Meredig
Date: 2017-11-01 00:00:00
Tasks: BIG-bench Machine Learning

As data-driven methods rise in popularity in materials science applications,
a key question is how these machine learning models can be used to understand
microstructure. Given the importance of process-structure-property relations
throughout materials science, it seems logical that models that can leverage
microstructural data would be more capable of predicting property information.
While there have been some recent attempts to use convolutional neural networks
to understand microstructural images, these early studies have focused only on
which featurizations yield the highest machine learning model accuracy for a
single data set. This paper explores the use of convolutional neural networks
for classifying microstructure with a more holistic set of objectives in mind:
generalization between data sets, number of features required, and
interpretability.
VIGAN: Missing View Imputation with Generative Adversarial Networks
Authors: Chao Shang, Aaron Palmer, Jiangwen Sun, Ko-Shin Chen, Jin Lu, Jinbo Bi
Date: 2017-08-22 00:00:00
Tasks: Denoising, Imputation, Matrix Completion

In an era when big data are becoming the norm, there is less concern with the
quantity but more with the quality and completeness of the data. In many
disciplines, data are collected from heterogeneous sources, resulting in
multi-view or multi-modal datasets. The missing data problem has been
challenging to address in multi-view data analysis. Especially, when certain
samples miss an entire view of data, it creates the missing view problem.
Classic multiple imputations or matrix completion methods are hardly effective
here when no information can be based on in the specific view to impute data
for such samples. The commonly-used simple method of removing samples with a
missing view can dramatically reduce sample size, thus diminishing the
statistical power of a subsequent analysis. In this paper, we propose a novel
approach for view imputation via generative adversarial networks (GANs), which
we name by VIGAN. This approach first treats each view as a separate domain and
identifies domain-to-domain mappings via a GAN using randomly-sampled data from
each view, and then employs a multi-modal denoising autoencoder (DAE) to
reconstruct the missing view from the GAN outputs based on paired data across
the views. Then, by optimizing the GAN and DAE jointly, our model enables the
knowledge integration for domain mappings and view correspondences to
effectively recover the missing view. Empirical results on benchmark datasets
validate the VIGAN approach by comparing against the state of the art. The
evaluation of VIGAN in a genetic study of substance use disorders further
proves the effectiveness and usability of this approach in life science.
Minimal Exploration in Structured Stochastic Bandits
Authors: Richard Combes, Stefan Magureanu, Alexandre Proutiere
Date: 2017-11-01 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Thompson Sampling

This paper introduces and addresses a wide class of stochastic bandit
problems where the function mapping the arm to the corresponding reward
exhibits some known structural properties. Most existing structures (e.g.
linear, Lipschitz, unimodal, combinatorial, dueling, ...) are covered by our
framework. We derive an asymptotic instance-specific regret lower bound for
these problems, and develop OSSB, an algorithm whose regret matches this
fundamental limit. OSSB is not based on the classical principle of "optimism in
the face of uncertainty" or on Thompson sampling, and rather aims at matching
the minimal exploration rates of sub-optimal arms as characterized in the
derivation of the regret lower bound. We illustrate the efficiency of OSSB
using numerical experiments in the case of the linear bandit problem and show
that OSSB outperforms existing algorithms, including Thompson sampling.
Active Tolerant Testing
Authors: Avrim Blum, Lunjia Hu
Date: 2017-11-01 00:00:00

In this work, we give the first algorithms for tolerant testing of nontrivial
classes in the active model: estimating the distance of a target function to a
hypothesis class C with respect to some arbitrary distribution D, using only a
small number of label queries to a polynomial-sized pool of unlabeled examples
drawn from D. Specifically, we show that for the class D of unions of d
intervals on the line, we can estimate the error rate of the best hypothesis in
the class to an additive error epsilon from only $O(\frac{1}{\epsilon^6}\log
\frac{1}{\epsilon})$ label queries to an unlabeled pool of size
$O(\frac{d}{\epsilon^2}\log \frac{1}{\epsilon})$. The key point here is the
number of labels needed is independent of the VC-dimension of the class. This
extends the work of Balcan et al. [2012] who solved the non-tolerant testing
problem for this class (distinguishing the zero-error case from the case that
the best hypothesis in the class has error greater than epsilon).
  We also consider the related problem of estimating the performance of a given
learning algorithm A in this setting. That is, given a large pool of unlabeled
examples drawn from distribution D, can we, from only a few label queries,
estimate how well A would perform if the entire dataset were labeled? We focus
on k-Nearest Neighbor style algorithms, and also show how our results can be
applied to the problem of hyperparameter tuning (selecting the best value of k
for the given learning problem).
Active Exploration for Learning Symbolic Representations
Authors: Garrett Andersen, George Konidaris
Date: 2017-09-05 00:00:00
Proceeding: NeurIPS 2017 12

We introduce an online active exploration algorithm for data-efficiently
learning an abstract symbolic model of an environment. Our algorithm is divided
into two parts: the first part quickly generates an intermediate Bayesian
symbolic model from the data that the agent has collected so far, which the
agent can then use along with the second part to guide its future exploration
towards regions of the state space that the model is uncertain about. We show
that our algorithm outperforms random and greedy exploration policies on two
different computer game domains. The first domain is an Asteroids-inspired game
with complex dynamics but basic logical structure. The second is the Treasure
Game, with simpler dynamics but more complex logical structure.
Complex-valued image denosing based on group-wise complex-domain sparsity
Authors: Vladimir Katkovnik, Mykola Ponomarenko, Karen Egiazarian
Date: 2017-11-01 00:00:00

Phase imaging and wavefront reconstruction from noisy observations of complex
exponent is a topic of this paper. It is a highly non-linear problem because
the exponent is a 2{\pi}-periodic function of phase. The reconstruction of
phase and amplitude is difficult. Even with an additive Gaussian noise in
observations distributions of noisy components in phase and amplitude are
signal dependent and non-Gaussian. Additional difficulties follow from a prior
unknown correlation of phase and amplitude in real life scenarios. In this
paper, we propose a new class of non-iterative and iterative complex domain
filters based on group-wise sparsity in complex domain. This sparsity is based
on the techniques implemented in Block-Matching 3D filtering (BM3D) and 3D/4D
High-Order Singular Decomposition (HOSVD) exploited for spectrum design,
analysis and filtering. The introduced algorithms are a generalization of the
ideas used in the CD-BM3D algorithms presented in our previous publications.
The algorithms are implemented as a MATLAB Toolbox. The efficiency of the
algorithms is demonstrated by simulation tests.
Fast mixing for Latent Dirichlet allocation
Authors: Johan Jonasson
Date: 2017-01-11 00:00:00
Tasks: text-classification, Text Classification, Text Generation

Markov chain Monte Carlo (MCMC) algorithms are ubiquitous in probability
theory in general and in machine learning in particular. A Markov chain is
devised so that its stationary distribution is some probability distribution of
interest. Then one samples from the given distribution by running the Markov
chain for a "long time" until it appears to be stationary and then collects the
sample. However these chains are often very complex and there are no
theoretical guarantees that stationarity is actually reached. In this paper we
study the Gibbs sampler of the posterior distribution of a very simple case of
Latent Dirichlet Allocation, the arguably most well known Bayesian unsupervised
learning model for text generation and text classification. It is shown that
when the corpus consists of two long documents of equal length $m$ and the
vocabulary consists of only two different words, the mixing time is at most of
order $m^2\log m$ (which corresponds to $m\log m$ rounds over the corpus). It
will be apparent from our analysis that it seems very likely that the mixing
time is not much worse in the more relevant case when the number of documents
and the size of the vocabulary are also large as long as each word is
represented a large number in each document, even though the computations
involved may be intractable.
Robust Saliency Detection via Fusing Foreground and Background Priors
Authors: Kan Huang, Chunbiao Zhu, Ge Li
Date: 2017-11-01 00:00:00
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

Automatic Salient object detection has received tremendous attention from
research community and has been an increasingly important tool in many computer
vision tasks. This paper proposes a novel bottom-up salient object detection
framework which considers both foreground and background cues. First, A series
of background and foreground seeds are selected from an image reliably, and
then used for calculation of saliency map separately. Next, a combination of
foreground and background saliency map is performed. Last, a refinement step
based on geodesic distance is utilized to enhance salient regions, thus
deriving the final saliency map. Particularly we provide a robust scheme for
seeds selection which contributes a lot to accuracy improvement in saliency
detection. Extensive experimental evaluations demonstrate the effectiveness of
our proposed method against other outstanding methods.
Improving Neural Machine Translation through Phrase-based Forced Decoding
Authors: Jingyi Zhang, Masao Utiyama, Eiichro Sumita, Graham Neubig, Satoshi Nakamura
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Machine Translation, NMT, Translation

Compared to traditional statistical machine translation (SMT), neural machine
translation (NMT) often sacrifices adequacy for the sake of fluency. We propose
a method to combine the advantages of traditional SMT and NMT by exploiting an
existing phrase-based SMT model to compute the phrase-based decoding cost for
an NMT output and then using this cost to rerank the n-best NMT outputs. The
main challenge in implementing this approach is that NMT outputs may not be in
the search space of the standard phrase-based decoding algorithm, because the
search space of phrase-based SMT is limited by the phrase-based translation
rule table. We propose a soft forced decoding algorithm, which can always
successfully find a decoding path for any NMT output. We show that using the
forced decoding cost to rerank the NMT outputs can successfully improve
translation quality on four different language pairs.
Multi-View Data Generation Without View Supervision
Authors: Mickaël Chen, Ludovic Denoyer, Thierry Artières
Date: 2017-11-01 00:00:00
Proceeding: ICLR 2018 1

The development of high-dimensional generative models has recently gained a
great surge of interest with the introduction of variational auto-encoders and
generative adversarial neural networks. Different variants have been proposed
where the underlying latent space is structured, for example, based on
attributes describing the data to generate. We focus on a particular problem
where one aims at generating samples corresponding to a number of objects under
various views. We assume that the distribution of the data is driven by two
independent latent factors: the content, which represents the intrinsic
features of an object, and the view, which stands for the settings of a
particular observation of that object. Therefore, we propose a generative model
and a conditional variant built on such a disentangled latent space. This
approach allows us to generate realistic samples corresponding to various
objects in a high variety of views. Unlike many multi-view approaches, our
model doesn't need any supervision on the views but only on the content.
Compared to other conditional generation approaches that are mostly based on
binary or categorical attributes, we make no such assumption about the factors
of variations. Our model can be used on problems with a huge, potentially
infinite, number of categories. We experiment it on four image datasets on
which we demonstrate the effectiveness of the model and its ability to
generalize.