pwc-archive/papers-with-abstracts huggingface dataset:
A hybrid supervised/unsupervised machine learning approach to solar flare prediction
Authors: Federico Benvenuto, Michele Piana, Cristina Campi, Anna Maria Massone
Date: 2017-06-21 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Feature Importance, Solar Flare Prediction

We introduce a hybrid approach to solar flare prediction, whereby a
supervised regularization method is used to realize feature importance and an
unsupervised clustering method is used to realize the binary flare/no-flare
decision. The approach is validated against NOAA SWPC data.
The energy landscape of a simple neural network
Authors: Anthony Collins Gamst, Alden Walker
Date: 2017-06-21 00:00:00

We explore the energy landscape of a simple neural network. In particular, we
expand upon previous work demonstrating that the empirical complexity of fitted
neural networks is vastly less than a naive parameter count would suggest and
that this implicit regularization is actually beneficial for generalization
from fitted models.
Constrained Bayesian Optimization with Noisy Experiments
Authors: Benjamin Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy
Date: 2017-06-21 00:00:00
Tasks: Bayesian Optimization

Randomized experiments are the gold standard for evaluating the effects of
changes to real-world systems. Data in these tests may be difficult to collect
and outcomes may have high variance, resulting in potentially large measurement
error. Bayesian optimization is a promising technique for efficiently
optimizing multiple continuous parameters, but existing approaches degrade in
performance when the noise level is high, limiting its applicability to many
randomized experiments. We derive an expression for expected improvement under
greedy batch optimization with noisy observations and noisy constraints, and
develop a quasi-Monte Carlo approximation that allows it to be efficiently
optimized. Simulations with synthetic functions show that optimization
performance on noisy, constrained problems outperforms existing methods. We
further demonstrate the effectiveness of the method with two real-world
experiments conducted at Facebook: optimizing a ranking system, and optimizing
server compiler flags.
Identifying Condition-Action Statements in Medical Guidelines Using Domain-Independent Features
Authors: Hossein Hematialam, Wlodek Zadrozny
Date: 2017-06-13 00:00:00
Tasks: BIG-bench Machine Learning

This paper advances the state of the art in text understanding of medical
guidelines by releasing two new annotated clinical guidelines datasets, and
establishing baselines for using machine learning to extract condition-action
pairs. In contrast to prior work that relies on manually created rules, we
report experiment with several supervised machine learning techniques to
classify sentences as to whether they express conditions and actions. We show
the limitations and possible extensions of this work on text mining of medical
guidelines.
CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms
Authors: Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, Marian Mazzone
Date: 2017-06-21 00:00:00

We propose a new system for generating art. The system generates art by
looking at art and learning about style; and becomes creative by increasing the
arousal potential of the generated art by deviating from the learned styles. We
build over Generative Adversarial Networks (GAN), which have shown the ability
to learn to generate novel images simulating a given distribution. We argue
that such networks are limited in their ability to generate creative products
in their original design. We propose modifications to its objective to make it
capable of generating creative art by maximizing deviation from established
styles and minimizing deviation from art distribution. We conducted experiments
to compare the response of human subjects to the generated art with their
response to art created by artists. The results show that human subjects could
not distinguish art generated by the proposed system from art generated by
contemporary artists and shown in top art fairs. Human subjects even rated the
generated images higher on various scales.
Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction
Authors: Chen-Hsuan Lin, Chen Kong, Simon Lucey
Date: 2017-06-21 00:00:00
Tasks: 3D Object Reconstruction, Object, Object Reconstruction, Point Cloud Generation

Conventional methods of 3D object generative modeling learn volumetric
predictions using deep networks with 3D convolutional operations, which are
direct analogies to classical 2D ones. However, these methods are
computationally wasteful in attempt to predict 3D shapes, where information is
rich only on the surfaces. In this paper, we propose a novel 3D generative
modeling framework to efficiently generate object shapes in the form of dense
point clouds. We use 2D convolutional operations to predict the 3D structure
from multiple viewpoints and jointly apply geometric reasoning with 2D
projection optimization. We introduce the pseudo-renderer, a differentiable
module to approximate the true rendering operation, to synthesize novel depth
maps for optimization. Experimental results for single-image 3D object
reconstruction tasks show that we outperforms state-of-the-art methods in terms
of shape similarity and prediction density.
Spectral Graph Convolutions for Population-based Disease Prediction
Authors: Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante, Matthew Lee, Ricardo Guerrerro Moreno, Ben Glocker, Daniel Rueckert
Date: 2017-03-08 00:00:00
Tasks: Disease Prediction, Prediction

Exploiting the wealth of imaging and non-imaging information for disease
prediction tasks requires models capable of representing, at the same time,
individual features as well as data associations between subjects from
potentially large populations. Graphs provide a natural framework for such
tasks, yet previous graph-based approaches focus on pairwise similarities
without modelling the subjects' individual characteristics and features. On the
other hand, relying solely on subject-specific imaging feature vectors fails to
model the interaction and similarity between subjects, which can reduce
performance. In this paper, we introduce the novel concept of Graph
Convolutional Networks (GCN) for brain analysis in populations, combining
imaging and non-imaging data. We represent populations as a sparse graph where
its vertices are associated with image-based feature vectors and the edges
encode phenotypic information. This structure was used to train a GCN model on
partially labelled graphs, aiming to infer the classes of unlabelled nodes from
the node features and pairwise associations between subjects. We demonstrate
the potential of the method on the challenging ADNI and ABIDE databases, as a
proof of concept of the benefit from integrating contextual information in
classification tasks. This has a clear impact on the quality of the
predictions, leading to 69.5% accuracy for ABIDE (outperforming the current
state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion,
significantly outperforming standard linear classifiers where only individual
features are considered.
Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy
Authors: S. Moccia, S. J. Wirkert, H. Kenngott, A. S. Vemuri, M. Apitz, B. Mayer, E. De Momi, L. S. Mattos, L. Maier-Hein
Date: 2017-06-21 00:00:00
Tasks: Classification, General Classification

Objective: Surgical data science is evolving into a research field that aims
to observe everything occurring within and around the treatment process to
provide situation-aware data-driven assistance. In the context of endoscopic
video analysis, the accurate classification of organs in the field of view of
the camera proffers a technical challenge. Herein, we propose a new approach to
anatomical structure classification and image tagging that features an
intrinsic measure of confidence to estimate its own performance with high
reliability and which can be applied to both RGB and multispectral imaging (MI)
data. Methods: Organ recognition is performed using a superpixel classification
strategy based on textural and reflectance information. Classification
confidence is estimated by analyzing the dispersion of class probabilities.
Assessment of the proposed technology is performed through a comprehensive in
vivo study with seven pigs. Results: When applied to image tagging, mean
accuracy in our experiments increased from 65% (RGB) and 80% (MI) to 90% (RGB)
and 96% (MI) with the confidence measure. Conclusion: Results showed that the
confidence measure had a significant influence on the classification accuracy,
and MI data are better suited for anatomical structure labeling than RGB data.
Significance: This work significantly enhances the state of art in automatic
labeling of endoscopic videos by introducing the use of the confidence metric,
and by being the first study to use MI data for in vivo laparoscopic tissue
classification. The data of our experiments will be released as the first in
vivo MI dataset upon publication of this paper.
A Generative Model of Group Conversation
Authors: Hannah Morrison, Chris Martens
Date: 2017-06-21 00:00:00
Tasks: model

Conversations with non-player characters (NPCs) in games are typically
confined to dialogue between a human player and a virtual agent, where the
conversation is initiated and controlled by the player. To create richer, more
believable environments for players, we need conversational behavior to reflect
initiative on the part of the NPCs, including conversations that include
multiple NPCs who interact with one another as well as the player. We describe
a generative computational model of group conversation between agents, an
abstract simulation of discussion in a small group setting. We define
conversational interactions in terms of rules for turn taking and interruption,
as well as belief change, sentiment change, and emotional response, all of
which are dependent on agent personality, context, and relationships. We
evaluate our model using a parameterized expressive range analysis, observing
correlations between simulation parameters and features of the resulting
conversations. This analysis confirms, for example, that character
personalities will predict how often they speak, and that heterogeneous groups
of characters will generate more belief change.
The Theory is Predictive, but is it Complete? An Application to Human Perception of Randomness
Authors: Jon Kleinberg, Annie Liang, Sendhil Mullainathan
Date: 2017-06-21 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Sequential Decision Making

When we test a theory using data, it is common to focus on correctness: do
the predictions of the theory match what we see in the data? But we also care
about completeness: how much of the predictable variation in the data is
captured by the theory? This question is difficult to answer, because in
general we do not know how much "predictable variation" there is in the
problem. In this paper, we consider approaches motivated by machine learning
algorithms as a means of constructing a benchmark for the best attainable level
of prediction.
  We illustrate our methods on the task of predicting human-generated random
sequences. Relative to an atheoretical machine learning algorithm benchmark, we
find that existing behavioral models explain roughly 15 percent of the
predictable variation in this problem. This fraction is robust across several
variations on the problem. We also consider a version of this approach for
analyzing field data from domains in which human perception and generation of
randomness has been used as a conceptual framework; these include sequential
decision-making and repeated zero-sum games. In these domains, our framework
for testing the completeness of theories provides a way of assessing their
effectiveness over different contexts; we find that despite some differences,
the existing theories are fairly stable across our field domains in their
performance relative to the benchmark. Overall, our results indicate that (i)
there is a significant amount of structure in this problem that existing models
have yet to capture and (ii) there are rich domains in which machine learning
may provide a viable approach to testing completeness.
Ensembles of phalanxes across assessment metrics for robust ranking of homologous proteins
Authors: Jabed H. Tomal, William J. Welch, Ruben H. Zamar
Date: 2017-06-21 00:00:00
Tasks: Binary Classification, General Classification

Two proteins are homologous if they have a common evolutionary origin, and the binary classification problem is to identify proteins in a candidate set that are homologous to a particular native protein. The feature (explanatory) variables available for classification are various measures of similarity of proteins. There are multiple classification problems of this type for different native proteins and their respective candidate sets. Homologous proteins are rare in a single candidate set, giving a highly unbalanced two-class problem. The goal is to rank proteins in a candidate set according to the probability of being homologous to the set's native protein. An ideal classifier will place all the homologous proteins at the head of such a list. Our approach uses an ensemble of models in a classifier and an ensemble of assessment metrics. For a given metric a classifier combines models, each based on a subset of the available feature variables which we call phalanxes. The proposed ensemble of phalanxes identifies strong and diverse subsets of feature variables. A second phase of ensembling aggregates classifiers based on diverse evaluation metrics. The overall result is called an ensemble of phalanxes and metrics. It provide robustness against both close and distant homologues.
Comparing deep neural networks against humans: object recognition when the signal gets weaker
Authors: Robert Geirhos, David H. J. Janssen, Heiko H. Schütt, Jonas Rauber, Matthias Bethge, Felix A. Wichmann
Date: 2017-06-21 00:00:00
Tasks: General Classification, Object, Object Recognition

Human visual object recognition is typically rapid and seemingly effortless,
as well as largely independent of viewpoint and object orientation. Until very
recently, animate visual systems were the only ones capable of this remarkable
computational feat. This has changed with the rise of a class of computer
vision algorithms called deep neural networks (DNNs) that achieve human-level
classification performance on object recognition tasks. Furthermore, a growing
number of studies report similarities in the way DNNs and the human visual
system process objects, suggesting that current DNNs may be good models of
human visual object recognition. Yet there clearly exist important
architectural and processing differences between state-of-the-art DNNs and the
primate visual system. The potential behavioural consequences of these
differences are not well understood. We aim to address this issue by comparing
human and DNN generalisation abilities towards image degradations. We find the
human visual system to be more robust to image manipulations like contrast
reduction, additive noise or novel eidolon-distortions. In addition, we find
progressively diverging classification error-patterns between humans and DNNs
when the signal gets weaker, indicating that there may still be marked
differences in the way humans and current DNNs perform visual object
recognition. We envision that our findings as well as our carefully measured
and freely available behavioural datasets provide a new useful benchmark for
the computer vision community to improve the robustness of DNNs and a
motivation for neuroscientists to search for mechanisms in the brain that could
facilitate this robustness.
Ensemble Framework for Real-time Decision Making
Authors: Philip Rodgers, John Levine
Date: 2017-06-21 00:00:00
Tasks: Decision Making

This paper introduces a new framework for real-time decision making in video
games. An Ensemble agent is a compound agent composed of multiple agents, each
with its own tasks or goals to achieve. Usually when dealing with real-time
decision making, reactive agents are used; that is agents that return a
decision based on the current state. While reactive agents are very fast, most
games require more than just a rule-based agent to achieve good results.
Deliberative agents---agents that use a forward model to search future
states---are very useful in games with no hard time limit, such as Go or
Backgammon, but generally take too long for real-time games. The Ensemble
framework addresses this issue by allowing the agent to be both deliberative
and reactive at the same time. This is achieved by breaking up the game-play
into logical roles and having highly focused components for each role, with
each component disregarding anything outwith its own role. Reactive agents can
be used where a reactive agent is suited to the role, and where a deliberative
approach is required, branching is kept to a minimum by the removal of all
extraneous factors, enabling an informed decision to be made within a much
smaller time-frame. An Arbiter is used to combine the component results,
allowing high performing agents to be created from simple, efficient
components.
Graphcut Texture Synthesis for Single-Image Superresolution
Authors: Douglas Summers-Stay
Date: 2017-06-21 00:00:00
Tasks: Form, Texture Synthesis

Texture synthesis has proven successful at imitating a wide variety of
textures. Adding additional constraints (in the form of a low-resolution
version of the texture to be synthesized) makes it possible to use texture
synthesis methods for texture superresolution.
Exact Learning of Juntas from Membership Queries
Authors: Nader H. Bshouty, Areej Costa
Date: 2017-06-21 00:00:00

In this paper, we study adaptive and non-adaptive exact learning of Juntas
from membership queries. We use new techniques to find new bounds, narrow some
of the gaps between the lower bounds and upper bounds and find new
deterministic and randomized algorithms with small query and time complexities.
  Some of the bounds are tight in the sense that finding better ones either
gives a breakthrough result in some long-standing combinatorial open problem or
needs a new technique that is beyond the existing ones.
Faster Monte-Carlo Algorithms for Fixation Probability of the Moran Process on Undirected Graphs
Authors: Krishnendu Chatterjee, Rasmus Ibsen-Jensen, Martin A. Nowak
Date: 2017-06-21 00:00:00

Evolutionary graph theory studies the evolutionary dynamics in a population
structure given as a connected graph. Each node of the graph represents an
individual of the population, and edges determine how offspring are placed. We
consider the classical birth-death Moran process where there are two types of
individuals, namely, the residents with fitness 1 and mutants with fitness r.
The fitness indicates the reproductive strength. The evolutionary dynamics
happens as follows: in the initial step, in a population of all resident
individuals a mutant is introduced, and then at each step, an individual is
chosen proportional to the fitness of its type to reproduce, and the offspring
replaces a neighbor uniformly at random. The process stops when all individuals
are either residents or mutants. The probability that all individuals in the
end are mutants is called the fixation probability. We present faster
polynomial-time Monte-Carlo algorithms for finidng the fixation probability on
undirected graphs. Our algorithms are always at least a factor O(n^2/log n)
faster as compared to the previous algorithms, where n is the number of nodes,
and is polynomial even if r is given in binary. We also present lower bounds
showing that the upper bound on the expected number of effective steps we
present is asymptotically tight for undirected graphs.
Combined Task and Motion Planning as Classical AI Planning
Authors: Jonathan Ferrer-Mestres, Guillem Francès, Hector Geffner
Date: 2017-06-21 00:00:00
Tasks: Motion Planning, Task and Motion Planning, valid

Planning in robotics is often split into task and motion planning. The
high-level, symbolic task planner decides what needs to be done, while the
motion planner checks feasibility and fills up geometric detail. It is known
however that such a decomposition is not effective in general as the symbolic
and geometrical components are not independent. In this work, we show that it
is possible to compile task and motion planning problems into classical AI
planning problems; i.e., planning problems over finite and discrete state
spaces with a known initial state, deterministic actions, and goal states to be
reached. The compilation is sound, meaning that classical plans are valid robot
plans, and probabilistically complete, meaning that valid robot plans are
classical plans when a sufficient number of configurations is sampled. In this
approach, motion planners and collision checkers are used for the compilation,
but not at planning time. The key elements that make the approach effective are
1) expressive classical AI planning languages for representing the compiled
problems in compact form, that unlike PDDL make use of functions and state
constraints, and 2) general width-based search algorithms capable of finding
plans over huge combinatorial spaces using weak heuristics only. Empirical
results are presented for a PR2 robot manipulating tens of objects, for which
long plans are required.
cGAN-based Manga Colorization Using a Single Training Image
Authors: Paulina Hensman, Kiyoharu Aizawa
Date: 2017-06-21 00:00:00
Tasks: Colorization

The Japanese comic format known as Manga is popular all over the world. It is
traditionally produced in black and white, and colorization is time consuming
and costly. Automatic colorization methods generally rely on greyscale values,
which are not present in manga. Furthermore, due to copyright protection,
colorized manga available for training is scarce. We propose a manga
colorization method based on conditional Generative Adversarial Networks
(cGAN). Unlike previous cGAN approaches that use many hundreds or thousands of
training images, our method requires only a single colorized reference image
for training, avoiding the need of a large dataset. Colorizing manga using
cGANs can produce blurry results with artifacts, and the resolution is limited.
We therefore also propose a method of segmentation and color-correction to
mitigate these issues. The final results are sharp, clear, and in high
resolution, and stay true to the character's original color scheme.
Class-specific image denoising using importance sampling
Authors: Milad Niknejad, Jose M. Bioucas-Dias, Mario A. T. Figueiredo
Date: 2017-06-21 00:00:00
Tasks: Denoising, Image Denoising

In this paper, we propose a new image denoising method, tailored to specific
classes of images, assuming that a dataset of clean images of the same class is
available. Similarly to the non-local means (NLM) algorithm, the proposed
method computes a weighted average of non-local patches, which we interpret
under the importance sampling framework. This viewpoint introduces flexibility
regarding the adopted priors, the noise statistics, and the computation of
Bayesian estimates. The importance sampling viewpoint is exploited to
approximate the minimum mean squared error (MMSE) patch estimates, using the
true underlying prior on image patches. The estimates thus obtained converge to
the true MMSE estimates, as the number of samples approaches infinity.
Experimental results provide evidence that the proposed denoiser outperforms
the state-of-the-art in the specific classes of face and text images.