pwc-archive/papers-with-abstracts huggingface dataset:
Analyzing noise in autoencoders and deep networks
Authors: Ben Poole, Jascha Sohl-Dickstein, Surya Ganguli
Date: 2014-06-06 00:00:00
Tasks: Denoising

Autoencoders have emerged as a useful framework for unsupervised learning of
internal representations, and a wide variety of apparently conceptually
disparate regularization techniques have been proposed to generate useful
features. Here we extend existing denoising autoencoders to additionally inject
noise before the nonlinearity, and at the hidden unit activations. We show that
a wide variety of previous methods, including denoising, contractive, and
sparse autoencoders, as well as dropout can be interpreted using this
framework. This noise injection framework reaps practical benefits by providing
a unified strategy to develop new internal representations by designing the
nature of the injected noise. We show that noisy autoencoders outperform
denoising autoencoders at the very task of denoising, and are competitive with
other single-layer techniques on MNIST, and CIFAR-10. We also show that types
of noise other than dropout improve performance in a deep network through
sparsifying, decorrelating, and spreading information across representations.
A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations
Authors: Holger R. Roth, Le Lu, Ari Seff, Kevin M. Cherry, Joanne Hoffman, Shijun Wang, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
Date: 2014-06-06 00:00:00
Tasks: Computed Tomography (CT), Diagnostic, Sensitivity

Automated Lymph Node (LN) detection is an important clinical diagnostic task
but very challenging due to the low contrast of surrounding structures in
Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely
distributed locations. State-of-the-art studies show the performance range of
52.9% sensitivity at 3.1 false-positives per volume (FP/vol.), or 60.9% at 6.1
FP/vol. for mediastinal LN, by one-shot boosting on 3D HAAR features. In this
paper, we first operate a preliminary candidate generation stage, towards 100%
sensitivity at the cost of high FP levels (40 per patient), to harvest volumes
of interest (VOI). Our 2.5D approach consequently decomposes any 3D VOI by
resampling 2D reformatted orthogonal views N times, via scale, random
translations, and rotations with respect to the VOI centroid coordinates. These
random views are then used to train a deep Convolutional Neural Network (CNN)
classifier. In testing, the CNN is employed to assign LN probabilities for all
N random views that can be simply averaged (as a set) to compute the final
classification probability per VOI. We validate the approach on two datasets:
90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs.
We achieve sensitivities of 70%/83% at 3 FP/vol. and 84%/90% at 6 FP/vol. in
mediastinum and abdomen respectively, which drastically improves over the
previous state-of-the-art work.
Advances in Learning Bayesian Networks of Bounded Treewidth
Authors: Siqi Nie, Denis Deratani Maua, Cassio Polpo de Campos, Qiang Ji
Date: 2014-06-05 00:00:00
Proceeding: NeurIPS 2014 12

This work presents novel algorithms for learning Bayesian network structures
with bounded treewidth. Both exact and approximate methods are developed. The
exact method combines mixed-integer linear programming formulations for
structure learning and treewidth computation. The approximate method consists
in uniformly sampling $k$-trees (maximal graphs of treewidth $k$), and
subsequently selecting, exactly or approximately, the best structure whose
moral graph is a subgraph of that $k$-tree. Some properties of these methods
are discussed and proven. The approaches are empirically compared to each other
and to a state-of-the-art method for learning bounded treewidth structures on a
collection of public data sets with up to 100 variables. The experiments show
that our exact algorithm outperforms the state of the art, and that the
approximate approach is fairly accurate.
Conjunction and Negation of Natural Concepts: A Quantum-theoretic Modeling
Authors: Sandro Sozzo
Date: 2014-06-06 00:00:00
Tasks: Negation

We perform two experiments with the aim to investigate the effects of
negation on the combination of natural concepts. In the first experiment, we
test the membership weights of a list of exemplars with respect to two
concepts, e.g., {\it Fruits} and {\it Vegetables}, and their conjunction {\it
Fruits And Vegetables}. In the second experiment, we test the membership
weights of the same list of exemplars with respect to the same two concepts,
but negating the second, e.g., {\it Fruits} and {\it Not Vegetables}, and again
their conjunction {\it Fruits And Not Vegetables}. The collected data confirm
existing results on conceptual combination, namely, they show dramatic
deviations from the predictions of classical (fuzzy set) logic and probability
theory. More precisely, they exhibit conceptual vagueness, gradeness of
membership, overextension and double overextension of membership weights with
respect to the given conjunctions. Then, we show that the quantum probability
model in Fock space recently elaborated to model Hampton's data on concept
conjunction (Hampton, 1988a) and disjunction (Hampton, 1988b) faithfully
accords with the collected data. Our quantum-theoretic modeling enables to
describe these non-classical effects in terms of genuine quantum effects,
namely `contextuality', `superposition', `interference' and `emergence'. The
obtained results confirm and strenghten the analysis in Aerts (2009a) and Sozzo
(2014) on the identification of quantum aspects in experiments on conceptual
vagueness. Our results can be inserted within the general research on the
identification of quantum structures in cognitive and decision processes.
Computational role of eccentricity dependent cortical magnification
Authors: Tomaso Poggio, Jim Mutch, Leyla Isik
Date: 2014-06-06 00:00:00
Tasks: ARC, Translation

We develop a sampling extension of M-theory focused on invariance to scale
and translation. Quite surprisingly, the theory predicts an architecture of
early vision with increasing receptive field sizes and a high resolution fovea
-- in agreement with data about the cortical magnification factor, V1 and the
retina. From the slope of the inverse of the magnification factor, M-theory
predicts a cortical "fovea" in V1 in the order of $40$ by $40$ basic units at
each receptive field size -- corresponding to a foveola of size around $26$
minutes of arc at the highest resolution, $\approx 6$ degrees at the lowest
resolution. It also predicts uniform scale invariance over a fixed range of
scales independently of eccentricity, while translation invariance should
depend linearly on spatial frequency. Bouma's law of crowding follows in the
theory as an effect of cortical area-by-cortical area pooling; the Bouma
constant is the value expected if the signature responsible for recognition in
the crowding experiments originates in V2. From a broader perspective, the
emerging picture suggests that visual recognition under natural conditions
takes place by composing information from a set of fixations, with each
fixation providing recognition from a space-scale image fragment -- that is an
image patch represented at a set of increasing sizes and decreasing
resolutions.
Linguistic Analysis of Requirements of a Space Project and their Conformity with the Recommendations Proposed by a Controlled Natural Language
Authors: Anne Condamines, Maxime Warnier
Date: 2014-06-06 00:00:00

The long term aim of the project carried out by the French National Space
Agency (CNES) is to design a writing guide based on the real and regular
writing of requirements. As a first step in the project, this paper proposes a
lin-guistic analysis of requirements written in French by CNES engineers. The
aim is to determine to what extent they conform to two rules laid down in
INCOSE, a recent guide for writing requirements. Although CNES engineers are
not obliged to follow any Controlled Natural Language in their writing of
requirements, we believe that language regularities are likely to emerge from
this task, mainly due to the writers' experience. The issue is approached using
natural language processing tools to identify sentences that do not comply with
INCOSE rules. We further review these sentences to understand why the
recommendations cannot (or should not) always be applied when specifying
large-scale projects.
Multiscale probability transformation of basic probability assignment
Authors: Meizhu Li, Qi Zhang, Yong Deng
Date: 2014-06-06 00:00:00
Tasks: Decision Making

Decision making is still an open issue in the application of Dempster-Shafer
evidence theory. A lot of works have been presented for it. In the transferable
belief model (TBM), pignistic probabilities based on the basic probability as-
signments are used for decision making. In this paper, multiscale probability
transformation of basic probability assignment based on the belief function and
the plausibility function is proposed, which is a generalization of the
pignistic probability transformation. In the multiscale probability function, a
factor q based on the Tsallis entropy is used to make the multiscale prob-
abilities diversified. An example is shown that the multiscale probability
transformation is more reasonable in the decision making.
Towards a Better Understanding of the Local Attractor in Particle Swarm Optimization: Speed and Solution Quality
Authors: Vanessa Lange, Manuel Schmitt, Rolf Wanka
Date: 2014-06-06 00:00:00

Particle Swarm Optimization (PSO) is a popular nature-inspired meta-heuristic
for solving continuous optimization problems. Although this technique is widely
used, the understanding of the mechanisms that make swarms so successful is
still limited. We present the first substantial experimental investigation of
the influence of the local attractor on the quality of exploration and
exploitation. We compare in detail classical PSO with the social-only variant
where local attractors are ignored. To measure the exploration capabilities, we
determine how frequently both variants return results in the neighborhood of
the global optimum. We measure the quality of exploitation by considering only
function values from runs that reached a search point sufficiently close to the
global optimum and then comparing in how many digits such values still deviate
from the global minimum value. It turns out that the local attractor
significantly improves the exploration, but sometimes reduces the quality of
the exploitation. As a compromise, we propose and evaluate a hybrid PSO which
switches off its local attractors at a certain point in time. The effects
mentioned can also be observed by measuring the potential of the swarm.
Online Clustering of Bandits
Authors: Claudio Gentile, Shuai Li, Giovanni Zappella
Date: 2014-01-31 00:00:00
Tasks: Clustering, Online Clustering

We introduce a novel algorithmic approach to content recommendation based on
adaptive clustering of exploration-exploitation ("bandit") strategies. We
provide a sharp regret analysis of this algorithm in a standard stochastic
noise setting, demonstrate its scalability properties, and prove its
effectiveness on a number of artificial and real-world datasets. Our
experiments show a significant increase in prediction performance over
state-of-the-art methods for bandit problems.
Automated Generation of Geometric Theorems from Images of Diagrams
Authors: Xiaoyu Chen, Dan Song, Dongming Wang
Date: 2014-06-06 00:00:00
Tasks: Management

We propose an approach to generate geometric theorems from electronic images
of diagrams automatically. The approach makes use of techniques of Hough
transform to recognize geometric objects and their labels and of numeric
verification to mine basic geometric relations. Candidate propositions are
generated from the retrieved information by using six strategies and geometric
theorems are obtained from the candidates via algebraic computation.
Experiments with a preliminary implementation illustrate the effectiveness and
efficiency of the proposed approach for generating nontrivial theorems from
images of diagrams. This work demonstrates the feasibility of automated
discovery of profound geometric knowledge from simple image data and has
potential applications in geometric knowledge management and education.
Ant Colony Optimization for Inferring Key Gene Interactions
Authors: Khalid Raza, Mahish Kohli
Date: 2014-06-06 00:00:00
Tasks: Drug Design

Inferring gene interaction network from gene expression data is an important
task in systems biology research. The gene interaction network, especially key
interactions, plays an important role in identifying biomarkers for disease
that further helps in drug design. Ant colony optimization is an optimization
algorithm based on natural evolution and has been used in many optimization
problems. In this paper, we applied ant colony optimization algorithm for
inferring the key gene interactions from gene expression data. The algorithm
has been tested on two different kinds of benchmark datasets and observed that
it successfully identify some key gene interactions.
Separable Cosparse Analysis Operator Learning
Authors: Matthias Seibert, Julian Wörmann, Rémi Gribonval, Martin Kleinsteuber
Date: 2014-06-06 00:00:00
Tasks: Operator learning

The ability of having a sparse representation for a certain class of signals
has many applications in data analysis, image processing, and other research
fields. Among sparse representations, the cosparse analysis model has recently
gained increasing interest. Many signals exhibit a multidimensional structure,
e.g. images or three-dimensional MRI scans. Most data analysis and learning
algorithms use vectorized signals and thereby do not account for this
underlying structure. The drawback of not taking the inherent structure into
account is a dramatic increase in computational cost. We propose an algorithm
for learning a cosparse Analysis Operator that adheres to the preexisting
structure of the data, and thus allows for a very efficient implementation.
This is achieved by enforcing a separable structure on the learned operator.
Our learning algorithm is able to deal with multidimensional data of arbitrary
order. We evaluate our method on volumetric data at the example of
three-dimensional MRI scans.
Machine learning approach for text and document mining
Authors: Vishwanath Bijalwan, Pinki Kumari, Jordan Pascual, Vijay Bhaskar Semwal
Date: 2014-06-06 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Information Retrieval, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Retrieval, Text Categorization, text-classification, Text Classification

Text Categorization (TC), also known as Text Classification, is the task of
automatically classifying a set of text documents into different categories
from a predefined set. If a document belongs to exactly one of the categories,
it is a single-label classification task; otherwise, it is a multi-label
classification task. TC uses several tools from Information Retrieval (IR) and
Machine Learning (ML) and has received much attention in the last years from
both researchers in the academia and industry developers. In this paper, we
first categorize the documents using KNN based machine learning approach and
then return the most relevant documents.
Initial Experiments with TPTP-style Automated Theorem Provers on ACL2 Problems
Authors: Sebastiaan Joosten, Cezary Kaliszyk, Josef Urban
Date: 2014-06-06 00:00:00

This paper reports our initial experiments with using external ATP on some
corpora built with the ACL2 system. This is intended to provide the first
estimate about the usefulness of such external reasoning and AI systems for
solving ACL2 problems.
Enhancements to ACL2 in Versions 6.2, 6.3, and 6.4
Authors: Matt Kaufmann, J Strother Moore
Date: 2014-06-06 00:00:00

We report on improvements to ACL2 made since the 2013 ACL2 Workshop.
Consistent procedures for cluster tree estimation and pruning
Authors: Kamalika Chaudhuri, Sanjoy Dasgupta, Samory Kpotufe, Ulrike Von Luxburg
Date: 2014-06-05 00:00:00
Tasks: Clustering

For a density $f$ on ${\mathbb R}^d$, a {\it high-density cluster} is any
connected component of $\{x: f(x) \geq \lambda\}$, for some $\lambda > 0$. The
set of all high-density clusters forms a hierarchy called the {\it cluster
tree} of $f$. We present two procedures for estimating the cluster tree given
samples from $f$. The first is a robust variant of the single linkage algorithm
for hierarchical clustering. The second is based on the $k$-nearest neighbor
graph of the samples. We give finite-sample convergence rates for these
algorithms which also imply consistency, and we derive lower bounds on the
sample complexity of cluster tree estimation. Finally, we study a tree pruning
procedure that guarantees, under milder conditions than usual, to remove
clusters that are spurious while recovering those that are salient.
Towards building a Crowd-Sourced Sky Map
Authors: Dustin Lang, David W. Hogg, Bernhard Scholkopf
Date: 2014-06-05 00:00:00
Tasks: Tone Mapping

We describe a system that builds a high dynamic-range and wide-angle image of
the night sky by combining a large set of input images. The method makes use of
pixel-rank information in the individual input images to improve a "consensus"
pixel rank in the combined image. Because it only makes use of ranks and the
complexity of the algorithm is linear in the number of images, the method is
useful for large sets of uncalibrated images that might have undergone unknown
non-linear tone mapping transformations for visualization or aesthetic reasons.
We apply the method to images of the night sky (of unknown provenance)
discovered on the Web. The method permits discovery of astronomical objects or
features that are not visible in any of the input images taken individually.
More importantly, however, it permits scientific exploitation of a huge source
of astronomical images that would not be available to astronomical research
without our automatic system.
A Continuous Max-Flow Approach to Multi-Labeling Problems under Arbitrary Region Regularization
Authors: John S. H. Baxter, Martin Rajchl, Jing Yuan, Terry M. Peters
Date: 2014-05-05 00:00:00
Tasks: Segmentation

The incorporation of region regularization into max-flow segmentation has
traditionally focused on ordering and part-whole relationships. A side effect
of the development of such models is that it constrained regularization only to
those cases, rather than allowing for arbitrary region regularization. Directed
Acyclic Graphical Max-Flow (DAGMF) segmentation overcomes these limitations by
allowing for the algorithm designer to specify an arbitrary directed acyclic
graph to structure a max-flow segmentation. This allows for individual 'parts'
to be a member of multiple distinct 'wholes.'
A Continuous Max-Flow Approach to General Hierarchical Multi-Labeling Problems
Authors: John S. H. Baxter, Martin Rajchl, Jing Yuan, Terry M. Peters
Date: 2014-04-01 00:00:00
Tasks: Clustering, Segmentation

Multi-region segmentation algorithms often have the onus of incorporating
complex anatomical knowledge representing spatial or geometric relationships
between objects, and general-purpose methods of addressing this knowledge in an
optimization-based manner have thus been lacking. This paper presents
Generalized Hierarchical Max-Flow (GHMF) segmentation, which captures simple
anatomical part-whole relationships in the form of an unconstrained hierarchy.
Regularization can then be applied to both parts and wholes independently,
allowing for spatial grouping and clustering of labels in a globally optimal
convex optimization framework. For the purposes of ready integration into a
variety of segmentation tasks, the hierarchies can be presented in run-time,
allowing for the segmentation problem to be readily specified and alternatives
explored without undue programming effort or recompilation.
Generating Sequences With Recurrent Neural Networks
Authors: Alex Graves
Date: 2013-08-04 00:00:00
Tasks: Language Modelling, Text Generation

This paper shows how Long Short-term Memory recurrent neural networks can be
used to generate complex sequences with long-range structure, simply by
predicting one data point at a time. The approach is demonstrated for text
(where the data are discrete) and online handwriting (where the data are
real-valued). It is then extended to handwriting synthesis by allowing the
network to condition its predictions on a text sequence. The resulting system
is able to generate highly realistic cursive handwriting in a wide variety of
styles.
An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage
Authors: Michael R. Smith, Andrew White, Christophe Giraud-Carrier, Tony Martinez
Date: 2014-05-28 00:00:00
Tasks: BIG-bench Machine Learning, Meta-Learning

The results from most machine learning experiments are used for a specific
purpose and then discarded. This results in a significant loss of information
and requires rerunning experiments to compare learning algorithms. This also
requires implementation of another algorithm for comparison, that may not
always be correctly implemented. By storing the results from previous
experiments, machine learning algorithms can be compared easily and the
knowledge gained from them can be used to improve their performance. The
purpose of this work is to provide easy access to previous experimental results
for learning and comparison. These stored results are comprehensive -- storing
the prediction for each test instance as well as the learning algorithm,
hyperparameters, and training set that were used. Previous results are
particularly important for meta-learning, which, in a broad sense, is the
process of learning from previous machine learning results such that the
learning process is improved. While other experiment databases do exist, one of
our focuses is on easy access to the data. We provide meta-learning data sets
that are ready to be downloaded for meta-learning experiments. In addition,
queries to the underlying database can be made if specific information is
desired. We also differ from previous experiment databases in that our
databases is designed at the instance level, where an instance is an example in
a data set. We store the predictions of a learning algorithm trained on a
specific training set for each instance in the test set. Data set level
information can then be obtained by aggregating the results from the instances.
The instance level information can be used for many tasks such as determining
the diversity of a classifier or algorithmically determining the optimal subset
of training instances for a learning algorithm.