pwc-archive/papers-with-abstracts huggingface dataset:
On the Equivalence of CoCoA+ and DisDCA
Authors: Ching-pei Lee
Date: 2015-06-13 00:00:00

In this document, we show that the algorithm CoCoA+ (Ma et al., ICML, 2015)
under the setting used in their experiments, which is also the best setting
suggested by the authors that proposed this algorithm, is equivalent to the
practical variant of DisDCA (Yang, NIPS, 2013).
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
Authors: Holger R. Roth, Le Lu, Amal Farag, Hoo-chang Shin, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
Date: 2015-06-22 00:00:00
Tasks: Automated Pancreas Segmentation, Computed Tomography (CT), Medical Image Analysis, Organ Segmentation, Pancreas Segmentation, Segmentation, Superpixels

Automatic organ segmentation is an important yet challenging problem for
medical image analysis. The pancreas is an abdominal organ with very high
anatomical variability. This inhibits previous segmentation methods from
achieving high accuracies, especially compared to other organs such as the
liver, heart or kidneys. In this paper, we present a probabilistic bottom-up
approach for pancreas segmentation in abdominal computed tomography (CT) scans,
using multi-level deep convolutional networks (ConvNets). We propose and
evaluate several variations of deep ConvNets in the context of hierarchical,
coarse-to-fine classification on image patches and regions, i.e. superpixels.
We first present a dense labeling of local image patches via
$P{-}\mathrm{ConvNet}$ and nearest neighbor fusion. Then we describe a regional
ConvNet ($R_1{-}\mathrm{ConvNet}$) that samples a set of bounding boxes around
each image superpixel at different scales of contexts in a "zoom-out" fashion.
Our ConvNets learn to assign class probabilities for each superpixel region of
being pancreas. Last, we study a stacked $R_2{-}\mathrm{ConvNet}$ leveraging
the joint space of CT intensities and the $P{-}\mathrm{ConvNet}$ dense
probability maps. Both 3D Gaussian smoothing and 2D conditional random fields
are exploited as structured predictions for post-processing. We evaluate on CT
images of 82 patients in 4-fold cross-validation. We achieve a Dice Similarity
Coefficient of 83.6$\pm$6.3% in training and 71.8$\pm$10.7% in testing.
Deep Convolutional Inverse Graphics Network
Authors: Tejas D. Kulkarni, Will Whitney, Pushmeet Kohli, Joshua B. Tenenbaum
Date: 2015-03-11 00:00:00
Proceeding: NeurIPS 2015 12

This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a
model that learns an interpretable representation of images. This
representation is disentangled with respect to transformations such as
out-of-plane rotations and lighting variations. The DC-IGN model is composed of
multiple layers of convolution and de-convolution operators and is trained
using the Stochastic Gradient Variational Bayes (SGVB) algorithm. We propose a
training procedure to encourage neurons in the graphics code layer to represent
a specific transformation (e.g. pose or light). Given a single input image, our
model can generate new images of the same object with variations in pose and
lighting. We present qualitative and quantitative results of the model's
efficacy at learning a 3D rendering engine.
Extreme Extraction: Only One Hour per Relation
Authors: Raphael Hoffmann, Luke Zettlemoyer, Daniel S. Weld
Date: 2015-06-21 00:00:00
Tasks: Relation

Information Extraction (IE) aims to automatically generate a large knowledge
base from natural language text, but progress remains slow. Supervised learning
requires copious human annotation, while unsupervised and weakly supervised
approaches do not deliver competitive accuracy. As a result, most fielded
applications of IE, as well as the leading TAC-KBP systems, rely on significant
amounts of manual engineering. Even "Extreme" methods, such as those reported
in Freedman et al. 2011, require about 10 hours of expert labor per relation.
  This paper shows how to reduce that effort by an order of magnitude. We
present a novel system, InstaRead, that streamlines authoring with an ensemble
of methods: 1) encoding extraction rules in an expressive and compositional
representation, 2) guiding the user to promising rules based on corpus
statistics and mined resources, and 3) introducing a new interactive
development cycle that provides immediate feedback --- even on large datasets.
Experiments show that experts can create quality extractors in under an hour
and even NLP novices can author good extractors. These extractors equal or
outperform ones obtained by comparably supervised and state-of-the-art
distantly supervised approaches.
Puzzle Imaging: Using Large-scale Dimensionality Reduction Algorithms for Localization
Authors: Joshua I. Glaser, Bradley M. Zamft, George M. Church, Konrad P. Kording
Date: 2015-02-27 00:00:00
Tasks: Dimensionality Reduction

Current high-resolution imaging techniques require an intact sample that
preserves spatial relationships. We here present a novel approach, "puzzle
imaging," that allows imaging a spatially scrambled sample. This technique
takes many spatially disordered samples, and then pieces them back together
using local properties embedded within the sample. We show that puzzle imaging
can efficiently produce high-resolution images using dimensionality reduction
algorithms. We demonstrate the theoretical capabilities of puzzle imaging in
three biological scenarios, showing that (1) relatively precise 3-dimensional
brain imaging is possible; (2) the physical structure of a neural network can
often be recovered based only on the neural connectivity matrix; and (3) a
chemical map could be reproduced using bacteria with chemosensitive DNA and
conjugative transfer. The ability to reconstruct scrambled images promises to
enable imaging based on DNA sequencing of homogenized tissue samples.
The concept of free will as an infinite metatheoretic recursion
Authors: Hanaan Hashim, R. Srikanth
Date: 2015-03-22 00:00:00
Tasks: Philosophy

It is argued that the concept of free will, like the concept of truth in
formal languages, requires a separation between an object level and a
meta-level for being consistently defined. The Jamesian two-stage model, which
deconstructs free will into the causally open "free" stage with its closure in
the "will" stage, is implicitly a move in this direction. However, to avoid the
dilemma of determinism, free will additionally requires an infinite regress of
causal meta-stages, making free choice a hypertask. We use this model to define
free will of the rationalist-compatibilist type. This is shown to provide a
natural three-way distinction between quantum indeterminism, freedom and free
will, applicable respectively to artificial intelligence (AI), animal agents
and human agents. We propose that the causal hierarchy in our model corresponds
to a hierarchy of Turing uncomputability. Possible neurobiological and
behavioral tests to demonstrate free will experimentally are suggested.
Ramifications of the model for physics, evolutionary biology, neuroscience,
neuropathological medicine and moral philosophy are briefly outlined.
A Novel Method for Stock Forecasting based on Fuzzy Time Series Combined with the Longest Common/Repeated Sub-sequence
Authors: He-Wen Chen, Zih-Ci Wang, Shu-Yu Kuo, Yao-Hsin Chou
Date: 2015-06-21 00:00:00
Tasks: Time Series, Time Series Analysis

Stock price forecasting is an important issue for investors since extreme
accuracy in forecasting can bring about high profits. Fuzzy Time Series (FTS)
and Longest Common/Repeated Sub-sequence (LCS/LRS) are two important issues for
forecasting prices. However, to the best of our knowledge, there are no
significant studies using LCS/LRS to predict stock prices. It is impossible
that prices stay exactly the same as historic prices. Therefore, this paper
proposes a state-of-the-art method which combines FTS and LCS/LRS to predict
stock prices. This method is based on the principle that history will repeat
itself. It uses different interval lengths in FTS to fuzzify the prices, and
LCS/LRS to look for the same pattern in the historical prices to predict future
stock prices. In the experiment, we examine various intervals of fuzzy time
sets in order to achieve high prediction accuracy. The proposed method
outperforms traditional methods in terms of prediction accuracy and,
furthermore, it is easy to implement.
Soft-Deep Boltzmann Machines
Authors: Taichi Kiwaki
Date: 2015-05-11 00:00:00

We present a layered Boltzmann machine (BM) that can better exploit the
advantages of a distributed representation. It is widely believed that deep BMs
(DBMs) have far greater representational power than its shallow counterpart,
restricted Boltzmann machines (RBMs). However, this expectation on the
supremacy of DBMs over RBMs has not ever been validated in a theoretical
fashion. In this paper, we provide both theoretical and empirical evidences
that the representational power of DBMs can be actually rather limited in
taking advantages of distributed representations. We propose an approximate
measure for the representational power of a BM regarding to the efficiency of a
distributed representation. With this measure, we show a surprising fact that
DBMs can make inefficient use of distributed representations. Based on these
observations, we propose an alternative BM architecture, which we dub soft-deep
BMs (sDBMs). We show that sDBMs can more efficiently exploit the distributed
representations in terms of the measure. Experiments demonstrate that sDBMs
outperform several state-of-the-art models, including DBMs, in generative tasks
on binarized MNIST and Caltech-101 silhouettes.
Metacarpal Bones Localization in X-ray Imagery Using Particle Filter Segmentation
Authors: Z. Bardosi, D. Granata, G. Lugos, A. P. Tafti, S. Saxena
Date: 2014-12-28 00:00:00
Tasks: Segmentation

Statistical methods such as sequential Monte Carlo Methods were proposed for
detection, segmentation and tracking of objects in digital images. A similar
approach, called Shape Particle Filters was introduced for the segmentation of
vertebra, lungs and hearts [1]. In this contribution, a global shape and a
local appearance model are derived from specific object annotated X-ray images
of the metacarpal bones. In the test data a unique labeling of the bone
boundary and the background points and a manual annotation is given. Using a
set of local features (Haar-like) in the neighborhood of each pixel a
probabilistic pixel classifier is built using the random forest algorithm. To
fit the shape model to a new image, a label probability map is extracted and
then the optimal shape is obtained by maximizing the probability of each
landmark with the Differential Evolution algorithm.
Understanding image representations by measuring their equivariance and equivalence
Authors: Karel Lenc, Andrea Vedaldi
Date: 2014-11-21 00:00:00
Proceeding: CVPR 2015 6

Despite the importance of image representations such as histograms of
oriented gradients and deep Convolutional Neural Networks (CNN), our
theoretical understanding of them remains limited. Aiming at filling this gap,
we investigate three key mathematical properties of representations:
equivariance, invariance, and equivalence. Equivariance studies how
transformations of the input image are encoded by the representation,
invariance being a special case where a transformation has no effect.
Equivalence studies whether two representations, for example two different
parametrisations of a CNN, capture the same visual information or not. A number
of methods to establish these properties empirically are proposed, including
introducing transformation and stitching layers in CNNs. These methods are then
applied to popular representations to reveal insightful aspects of their
structure, including clarifying at which layers in a CNN certain geometric
invariances are achieved. While the focus of the paper is theoretical, direct
applications to structured-output regression are demonstrated too.
Pose Estimation Based on 3D Models
Authors: Chuiwen Ma, Hao Su, Liang Shi
Date: 2015-06-20 00:00:00
Tasks: General Classification, Multi-class Classification, Pose Estimation

In this paper, we proposed a pose estimation system based on rendered image
training set, which predicts the pose of objects in real image, with knowledge
of object category and tight bounding box. We developed a patch-based
multi-class classification algorithm, and an iterative approach to improve the
accuracy. We achieved state-of-the-art performance on pose estimation task.
3D Reconstruction from Full-view Fisheye Camera
Authors: Chuiwen Ma, Liang Shi, Hanlu Huang, Mengyuan Yan
Date: 2015-06-20 00:00:00
Tasks: 3D Reconstruction

In this report, we proposed a 3D reconstruction method for the full-view
fisheye camera. The camera we used is Ricoh Theta, which captures spherical
images and has a wide field of view (FOV). The conventional stereo apporach
based on perspective camera model cannot be directly applied and instead we
used a spherical camera model to depict the relation between 3D point and its
corresponding observation in the image. We implemented a system that can
reconstruct the 3D scene using captures from two or more cameras. A GUI is also
created to allow users to control the view perspective and obtain a better
intuition of how the scene is rebuilt. Experiments showed that our
reconstruction results well preserved the structure of the scene in the real
world.
Aligning where to see and what to tell: image caption with region-based attention and scene factorization
Authors: Junqi Jin, Kun fu, Runpeng Cui, Fei Sha, Chang-Shui Zhang
Date: 2015-06-20 00:00:00
Tasks: Image Captioning

Recent progress on automatic generation of image captions has shown that it
is possible to describe the most salient information conveyed by images with
accurate and meaningful sentences. In this paper, we propose an image caption
system that exploits the parallel structures between images and sentences. In
our model, the process of generating the next word, given the previously
generated ones, is aligned with the visual perception experience where the
attention shifting among the visual regions imposes a thread of visual
ordering. This alignment characterizes the flow of "abstract meaning", encoding
what is semantically shared by both the visual scene and the text description.
Our system also makes another novel modeling contribution by introducing
scene-specific contexts that capture higher-level semantic information encoded
in an image. The contexts adapt language models for word generation to specific
scene types. We benchmark our system and contrast to published results on
several popular datasets. We show that using either region-based attention or
scene-specific contexts improves systems without those components. Furthermore,
combining these two modeling ingredients attains the state-of-the-art
performance.
Can deep learning help you find the perfect match?
Authors: Harm de Vries, Jason Yosinski
Date: 2015-05-02 00:00:00
Tasks: Deep Learning

Is he/she my type or not? The answer to this question depends on the personal
preferences of the one asking it. The individual process of obtaining a full
answer may generally be difficult and time consuming, but often an approximate
answer can be obtained simply by looking at a photo of the potential match.
Such approximate answers based on visual cues can be produced in a fraction of
a second, a phenomenon that has led to a series of recently successful dating
apps in which users rate others positively or negatively using primarily a
single photo. In this paper we explore using convolutional networks to create a
model of an individual's personal preferences based on rated photos. This
introduced task is difficult due to the large number of variations in profile
pictures and the noise in attractiveness labels. Toward this task we collect a
dataset comprised of $9364$ pictures and binary labels for each. We compare
performance of convolutional models trained in three ways: first directly on
the collected dataset, second with features transferred from a network trained
to predict gender, and third with features transferred from a network trained
on ImageNet. Our findings show that ImageNet features transfer best, producing
a model that attains $68.1\%$ accuracy on the test set and is moderately
successful at predicting matches.
Collective Mind, Part II: Towards Performance- and Cost-Aware Software Engineering as a Natural Science
Authors: Grigori Fursin, Abdul Memon, Christophe Guillon, Anton Lokhmotov
Date: 2015-06-20 00:00:00

Nowadays, engineers have to develop software often without even knowing which
hardware it will eventually run on in numerous mobile phones, tablets,
desktops, laptops, data centers, supercomputers and cloud services.
Unfortunately, optimizing compilers are not keeping pace with ever increasing
complexity of computer systems anymore and may produce severely underperforming
executable codes while wasting expensive resources and energy.
  We present our practical and collaborative solution to this problem via
light-weight wrappers around any software piece when more than one
implementation or optimization choice available. These wrappers are connected
with a public Collective Mind autotuning infrastructure and repository of
knowledge (c-mind.org/repo) to continuously monitor various important
characteristics of these pieces (computational species) across numerous
existing hardware configurations together with randomly selected optimizations.
Similar to natural sciences, we can now continuously track winning solutions
(optimizations for a given hardware) that minimize all costs of a computation
(execution time, energy spent, code size, failures, memory and storage
footprint, optimization time, faults, contentions, inaccuracy and so on) of a
given species on a Pareto frontier along with any unexpected behavior. The
community can then collaboratively classify solutions, prune redundant ones,
and correlate them with various features of software, its inputs (data sets)
and used hardware either manually or using powerful predictive analytics
techniques. Our approach can then help create a large, realistic, diverse,
representative, and continuously evolving benchmark with related optimization
knowledge while gradually covering all possible software and hardware to be
able to predict best optimizations and improve compilers and hardware depending
on usage scenarios and requirements.
Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations
Authors: Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, James M. Robins
Date: 2014-11-17 00:00:00
Tasks: BIG-bench Machine Learning

We propose and analyze estimators for statistical functionals of one or more
distributions under nonparametric assumptions. Our estimators are based on the
theory of influence functions, which appear in the semiparametric statistics
literature. We show that estimators based either on data-splitting or a
leave-one-out technique enjoy fast rates of convergence and other favorable
theoretical properties. We apply this framework to derive estimators for
several popular information theoretic quantities, and via empirical evaluation,
show the advantage of this approach over existing estimators.
Detectability thresholds and optimal algorithms for community structure in dynamic networks
Authors: Amir Ghasemian, Pan Zhang, Aaron Clauset, Cristopher Moore, Leto Peel
Date: 2015-06-19 00:00:00
Tasks: Clustering

We study the fundamental limits on learning latent community structure in
dynamic networks. Specifically, we study dynamic stochastic block models where
nodes change their community membership over time, but where edges are
generated independently at each time step. In this setting (which is a special
case of several existing models), we are able to derive the detectability
threshold exactly, as a function of the rate of change and the strength of the
communities. Below this threshold, we claim that no algorithm can identify the
communities better than chance. We then give two algorithms that are optimal in
the sense that they succeed all the way down to this limit. The first uses
belief propagation (BP), which gives asymptotically optimal accuracy, and the
second is a fast spectral clustering algorithm, based on linearizing the BP
equations. We verify our analytic and algorithmic results via numerical
simulation, and close with a brief discussion of extensions and open questions.
Representative Selection in Non Metric Datasets
Authors: Elad Liebman, Benny Chor, Peter Stone
Date: 2015-02-26 00:00:00
Tasks: Clustering

This paper considers the problem of representative selection: choosing a
subset of data points from a dataset that best represents its overall set of
elements. This subset needs to inherently reflect the type of information
contained in the entire set, while minimizing redundancy. For such purposes,
clustering may seem like a natural approach. However, existing clustering
methods are not ideally suited for representative selection, especially when
dealing with non-metric data, where only a pairwise similarity measure exists.
In this paper we propose $\delta$-medoids, a novel approach that can be viewed
as an extension to the $k$-medoids algorithm and is specifically suited for
sample representative selection from non-metric data. We empirically validate
$\delta$-medoids in two domains, namely music analysis and motion analysis. We
also show some theoretical bounds on the performance of $\delta$-medoids and
the hardness of representative selection in general.
Multi-Armed Bandits for Intelligent Tutoring Systems
Authors: Benjamin Clement, Didier Roy, Pierre-Yves Oudeyer, Manuel Lopes
Date: 2013-10-11 00:00:00
Tasks: Multi-Armed Bandits

We present an approach to Intelligent Tutoring Systems which adaptively
personalizes sequences of learning activities to maximize skills acquired by
students, taking into account the limited time and motivational resources. At a
given point in time, the system proposes to the students the activity which
makes them progress faster. We introduce two algorithms that rely on the
empirical estimation of the learning progress, RiARiT that uses information
about the difficulty of each exercise and ZPDES that uses much less knowledge
about the problem.
  The system is based on the combination of three approaches. First, it
leverages recent models of intrinsically motivated learning by transposing them
to active teaching, relying on empirical estimation of learning progress
provided by specific activities to particular students. Second, it uses
state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the
exploration/exploitation challenge of this optimization process. Third, it
leverages expert knowledge to constrain and bootstrap initial exploration of
the MAB, while requiring only coarse guidance information of the expert and
allowing the system to deal with didactic gaps in its knowledge. The system is
evaluated in a scenario where 7-8 year old schoolchildren learn how to
decompose numbers while manipulating money. Systematic experiments are
presented with simulated students, followed by results of a user study across a
population of 400 school children.
Structured Training for Neural Network Transition-Based Parsing
Authors: David Weiss, Chris Alberti, Michael Collins, Slav Petrov
Date: 2015-06-19 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Dependency Parsing, Transition-Based Dependency Parsing

We present structured perceptron training for neural network transition-based
dependency parsing. We learn the neural network representation using a gold
corpus augmented by a large number of automatically parsed sentences. Given
this fixed network representation, we learn a final layer using the structured
perceptron with beam-search decoding. On the Penn Treebank, our parser reaches
94.26% unlabeled and 92.41% labeled attachment accuracy, which to our knowledge
is the best accuracy on Stanford Dependencies to date. We also provide in-depth
ablative analysis to determine which aspects of our model provide the largest
gains in accuracy.