pwc-archive/papers-with-abstracts huggingface dataset:
Principles and Examples of Plausible Reasoning and Propositional Plausible Logic
Authors: David Billington
Date: 2017-03-06 00:00:00
Tasks: Formal Logic

Plausible reasoning concerns situations whose inherent lack of precision is
not quantified; that is, there are no degrees or levels of precision, and hence
no use of numbers like probabilities. A hopefully comprehensive set of
principles that clarifies what it means for a formal logic to do plausible
reasoning is presented. A new propositional logic, called Propositional
Plausible Logic (PPL), is defined and applied to some important examples. PPL
is the only non-numeric non-monotonic logic we know of that satisfies all the
principles and correctly reasons with all the examples. Some important results
about PPL are proved.
Finding Common Characteristics Among NBA Playoff and Championship Teams: A Machine Learning Approach
Authors: Ikjyot Singh Kohli
Date: 2016-04-18 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

In this paper, we employ machine learning techniques to analyze seventeen
seasons (1999-2000 to 2015-2016) of NBA regular season data from every team to
determine the common characteristics among NBA playoff teams. Each team was
characterized by 26 predictor variables and one binary response variable taking
on a value of "TRUE" if a team had made the playoffs, and value of "FALSE" if a
team had missed the playoffs. After fitting an initial classification tree to
this problem, this tree was then pruned which decreased the test error rate.
Further to this, a random forest of classification trees was grown which
provided a very accurate model from which a variable importance plot was
generated to determine which predictor variables had the greatest influence on
the response variable. The result of this work was the conclusion that the most
important factors in characterizing a team's playoff eligibility are a team's
opponent number of assists per game, a team's opponent number of made two point
shots per game, and a team's number of steals per game. This seems to suggest
that defensive factors as opposed to offensive factors are the most important
characteristics shared among NBA playoff teams. We then use neural networks to
classify championship teams based on regular season data. From this, we show
that the most important factor in a team not winning a championship is that
team's opponent number of made three-point shots per game. This once again
implies that defensive characteristics are of great importance in not only
determining a team's playoff eligibility, but certainly, one can conclude that
a lack of perimeter defense negatively impacts a team's championship chances in
a given season. Further, it is shown that made two-point shots and defensive
rebounding are by far the most important factor in a team's chances at winning
a championship in a given season.
Online deforestation detection
Authors: Emiliano Diaz
Date: 2017-04-03 00:00:00
Tasks: Management, Time Series Analysis

Deforestation detection using satellite images can make an important
contribution to forest management. Current approaches can be broadly divided
into those that compare two images taken at similar periods of the year and
those that monitor changes by using multiple images taken during the growing
season. The CMFDA algorithm described in Zhu et al. (2012) is an algorithm that
builds on the latter category by implementing a year-long, continuous,
time-series based approach to monitoring images. This algorithm was developed
for 30m resolution, 16-day frequency reflectance data from the Landsat
satellite. In this work we adapt the algorithm to 1km, 16-day frequency
reflectance data from the modis sensor aboard the Terra satellite. The CMFDA
algorithm is composed of two submodels which are fitted on a pixel-by-pixel
basis. The first estimates the amount of surface reflectance as a function of
the day of the year. The second estimates the occurrence of a deforestation
event by comparing the last few predicted and real reflectance values. For this
comparison, the reflectance observations for six different bands are first
combined into a forest index. Real and predicted values of the forest index are
then compared and high absolute differences for consecutive observation dates
are flagged as deforestation events. Our adapted algorithm also uses the two
model framework. However, since the modis 13A2 dataset used, includes
reflectance data for different spectral bands than those included in the
Landsat dataset, we cannot construct the forest index. Instead we propose two
contrasting approaches: a multivariate and an index approach similar to that of
CMFDA.
Stick-Breaking Variational Autoencoders
Authors: Eric Nalisnick, Padhraic Smyth
Date: 2016-05-20 00:00:00

We extend Stochastic Gradient Variational Bayes to perform posterior
inference for the weights of Stick-Breaking processes. This development allows
us to define a Stick-Breaking Variational Autoencoder (SB-VAE), a Bayesian
nonparametric version of the variational autoencoder that has a latent
representation with stochastic dimensionality. We experimentally demonstrate
that the SB-VAE, and a semi-supervised variant, learn highly discriminative
latent representations that often outperform the Gaussian VAE's.
A Probabilistic Linear Genetic Programming with Stochastic Context-Free Grammar for solving Symbolic Regression problems
Authors: Léo Françoso Dal Piccol Sotto, Vinícius Veloso de Melo
Date: 2017-04-03 00:00:00
Tasks: regression, Symbolic Regression

Traditional Linear Genetic Programming (LGP) algorithms are based only on the
selection mechanism to guide the search. Genetic operators combine or mutate
random portions of the individuals, without knowing if the result will lead to
a fitter individual. Probabilistic Model Building Genetic Programming (PMB-GP)
methods were proposed to overcome this issue through a probability model that
captures the structure of the fit individuals and use it to sample new
individuals. This work proposes the use of LGP with a Stochastic Context-Free
Grammar (SCFG), that has a probability distribution that is updated according
to selected individuals. We proposed a method for adapting the grammar into the
linear representation of LGP. Tests performed with the proposed probabilistic
method, and with two hybrid approaches, on several symbolic regression
benchmark problems show that the results are statistically better than the
obtained by the traditional LGP.
NIPS 2016 Tutorial: Generative Adversarial Networks
Authors: Ian Goodfellow
Date: 2016-12-31 00:00:00

This report summarizes the tutorial presented by the author at NIPS 2016 on
generative adversarial networks (GANs). The tutorial describes: (1) Why
generative modeling is a topic worth studying, (2) how generative models work,
and how GANs compare to other generative models, (3) the details of how GANs
work, (4) research frontiers in GANs, and (5) state-of-the-art image models
that combine GANs with other methods. Finally, the tutorial contains three
exercises for readers to complete, and the solutions to these exercises.
Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies
Authors: Amir Sadeghian, Alexandre Alahi, Silvio Savarese
Date: 2017-01-08 00:00:00
Proceeding: ICCV 2017 10

The majority of existing solutions to the Multi-Target Tracking (MTT) problem
do not combine cues in a coherent end-to-end fashion over a long period of
time. However, we present an online method that encodes long-term temporal
dependencies across multiple cues. One key challenge of tracking methods is to
accurately track occluded targets or those which share similar appearance
properties with surrounding objects. To address this challenge, we present a
structure of Recurrent Neural Networks (RNN) that jointly reasons on multiple
cues over a temporal window. We are able to correct many data association
errors and recover observations from an occluded state. We demonstrate the
robustness of our data-driven approach by tracking multiple targets using their
appearance, motion, and even interactions. Our method outperforms previous
works on multiple publicly available datasets including the challenging MOT
benchmark.
Adversarial Feature Learning
Authors: Jeff Donahue, Philipp Krähenbühl, Trevor Darrell
Date: 2016-05-31 00:00:00

The ability of the Generative Adversarial Networks (GANs) framework to learn
generative models mapping from simple latent distributions to arbitrarily
complex data distributions has been demonstrated empirically, with compelling
results showing that the latent space of such generators captures semantic
variation in the data distribution. Intuitively, models trained to predict
these semantic latent representations given data may serve as useful feature
representations for auxiliary problems where semantics are relevant. However,
in their existing form, GANs have no means of learning the inverse mapping --
projecting data back into the latent space. We propose Bidirectional Generative
Adversarial Networks (BiGANs) as a means of learning this inverse mapping, and
demonstrate that the resulting learned feature representation is useful for
auxiliary supervised discrimination tasks, competitive with contemporary
approaches to unsupervised and self-supervised feature learning.
Realizing an optimization approach inspired from Piagets theory on cognitive development
Authors: Utku Kose, Ahmet Arslan
Date: 2017-04-03 00:00:00

The objective of this paper is to introduce an artificial intelligence based
optimization approach, which is inspired from Piagets theory on cognitive
development. The approach has been designed according to essential processes
that an individual may experience while learning something new or improving his
/ her knowledge. These processes are associated with the Piagets ideas on an
individuals cognitive development. The approach expressed in this paper is a
simple algorithm employing swarm intelligence oriented tasks in order to
overcome single-objective optimization problems. For evaluating effectiveness
of this early version of the algorithm, test operations have been done via some
benchmark functions. The obtained results show that the approach / algorithm
can be an alternative to the literature in terms of single-objective
optimization. The authors have suggested the name: Cognitive Development
Optimization Algorithm (CoDOA) for the related intelligent optimization
approach.
On the idea of a new artificial intelligence based optimization algorithm inspired from the nature of vortex
Authors: Utku Kose, Ahmet Arslan
Date: 2017-04-03 00:00:00

In this paper, the idea of a new artificial intelligence based optimization
algorithm, which is inspired from the nature of vortex, has been provided
briefly. As also a bio-inspired computation algorithm, the idea is generally
focused on a typical vortex flow / behavior in nature and inspires from some
dynamics that are occurred in the sense of vortex nature. Briefly, the
algorithm is also a swarm-oriented evolutional problem solution approach;
because it includes many methods related to elimination of weak swarm members
and trying to improve the solution process by supporting the solution space via
new swarm members. In order have better idea about success of the algorithm; it
has been tested via some benchmark functions. At this point, the obtained
results show that the algorithm can be an alternative to the literature in
terms of single-objective optimization solution ways. Vortex Optimization
Algorithm (VOA) is the name suggestion by the authors; for this new idea of
intelligent optimization approach.
Design and development of a software system for swarm intelligence based research studies
Authors: Utku Kose
Date: 2017-04-03 00:00:00

This paper introduce a software system including widely-used Swarm
Intelligence algorithms or approaches to be used for the related scientific
research studies associated with the subject area. The programmatic
infrastructure of the system allows working on a fast, easy-to-use, interactive
platform to perform Swarm Intelligence based studies in a more effective,
efficient and accurate way. In this sense, the system employs all of the
necessary controls for the algorithms and it ensures an interactive platform on
which computer users can perform studies on a wide spectrum of solution
approaches associated with simple and also more advanced problems.
Geometric Insights into Support Vector Machine Behavior using the KKT Conditions
Authors: Iain Carmichael, J. S. Marron
Date: 2017-04-03 00:00:00

The support vector machine (SVM) is a powerful and widely used classification
algorithm. This paper uses the Karush-Kuhn-Tucker conditions to provide
rigorous mathematical proof for new insights into the behavior of SVM. These
insights provide perhaps unexpected relationships between SVM and two other
linear classifiers: the mean difference and the maximal data piling direction.
For example, we show that in many cases SVM can be viewed as a cropped version
of these classifiers. By carefully exploring these connections we show how SVM
tuning behavior is affected by characteristics including: balanced vs.
unbalanced classes, low vs. high dimension, separable vs. non-separable data.
These results provide further insights into tuning SVM via cross-validation by
explaining observed pathological behavior and motivating improved
cross-validation methodology. Finally, we also provide new results on the
geometry of complete data piling directions in high dimensional space.
AMC: Attention guided Multi-modal Correlation Learning for Image Search
Authors: Kan Chen, Trung Bui, Fang Chen, Zhaowen Wang, Ram Nevatia
Date: 2017-04-03 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Retrieval

Given a user's query, traditional image search systems rank images according
to its relevance to a single modality (e.g., image content or surrounding
text). Nowadays, an increasing number of images on the Internet are available
with associated meta data in rich modalities (e.g., titles, keywords, tags,
etc.), which can be exploited for better similarity measure with queries. In
this paper, we leverage visual and textual modalities for image search by
learning their correlation with input query. According to the intent of query,
attention mechanism can be introduced to adaptively balance the importance of
different modalities. We propose a novel Attention guided Multi-modal
Correlation (AMC) learning method which consists of a jointly learned hierarchy
of intra and inter-attention networks. Conditioned on query's intent,
intra-attention networks (i.e., visual intra-attention network and language
intra-attention network) attend on informative parts within each modality; a
multi-modal inter-attention network promotes the importance of the most
query-relevant modalities. In experiments, we evaluate AMC models on the search
logs from two real world image search engines and show a significant boost on
the ranking of user-clicked images in search results. Additionally, we extend
AMC models to caption ranking task on COCO dataset and achieve competitive
results compared with recent state-of-the-arts.
Unsupervised Action Proposal Ranking through Proposal Recombination
Authors: Waqas Sultani, Dong Zhang, Mubarak Shah
Date: 2017-04-03 00:00:00
Tasks: Action Detection, Action Recognition, Temporal Action Localization

Recently, action proposal methods have played an important role in action
recognition tasks, as they reduce the search space dramatically. Most
unsupervised action proposal methods tend to generate hundreds of action
proposals which include many noisy, inconsistent, and unranked action
proposals, while supervised action proposal methods take advantage of
predefined object detectors (e.g., human detector) to refine and score the
action proposals, but they require thousands of manual annotations to train.
  Given the action proposals in a video, the goal of the proposed work is to
generate a few better action proposals that are ranked properly. In our
approach, we first divide action proposal into sub-proposal and then use
Dynamic Programming based graph optimization scheme to select the optimal
combinations of sub-proposals from different proposals and assign each new
proposal a score. We propose a new unsupervised image-based actioness detector
that leverages web images and employs it as one of the node scores in our graph
formulation. Moreover, we capture motion information by estimating the number
of motion contours within each action proposal patch. The proposed method is an
unsupervised method that neither needs bounding box annotations nor video level
labels, which is desirable with the current explosion of large-scale action
datasets. Our approach is generic and does not depend on a specific action
proposal method. We evaluate our approach on several publicly available trimmed
and un-trimmed datasets and obtain better performance compared to several
proposal ranking methods. In addition, we demonstrate that properly ranked
proposals produce significantly better action detection as compared to
state-of-the-art proposal based methods.
GC-SROIQ(C) : Expressive Constraint Modelling and Grounded Circumscription for SROIQ
Authors: Arjun Bhardwaj, Sangeetha
Date: 2014-11-03 00:00:00

Developments in semantic web technologies have promoted ontological encoding
of knowledge from diverse domains. However, modelling many practical domains
requires more expressive representations schemes than what the standard
description logics(DLs) support. We extend the DL SROIQ with constraint
networks and grounded circumscription. Applications of constraint modelling
include embedding ontologies with temporal or spatial information, while
grounded circumscription allows defeasible inference and closed world
reasoning. This paper overcomes restrictions on existing constraint modelling
approaches by introducing expressive constructs. Grounded circumscription
allows concept and role minimization and is decidable for DL. We provide a
general and intuitive algorithm for the framework of grounded circumscription
that can be applied to a whole range of logics. We present the resulting logic:
GC-SROIQ(C), and describe a tableau decision procedure for it.
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis
Authors: Rong Ge, Chi Jin, Yi Zheng
Date: 2017-04-03 00:00:00
Proceeding: ICML 2017 8
Tasks: Matrix Completion

In this paper we develop a new framework that captures the common landscape
underlying the common non-convex low-rank matrix problems including matrix
sensing, matrix completion and robust PCA. In particular, we show for all above
problems (including asymmetric cases): 1) all local minima are also globally
optimal; 2) no high-order saddle points exists. These results explain why
simple algorithms such as stochastic gradient descent have global converge, and
efficiently optimize these non-convex objective functions in practice. Our
framework connects and simplifies the existing analyses on optimization
landscapes for matrix sensing and symmetric matrix completion. The framework
naturally leads to new results for asymmetric matrix completion and robust PCA.
Comparison of Different Methods for Tissue Segmentation in Histopathological Whole-Slide Images
Authors: Péter Bándi, Rob van de Loo, Milad Intezar, Daan Geijs, Francesco Ciompi, Bram van Ginneken, Jeroen van der Laak, Geert Litjens
Date: 2017-03-17 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, whole slide images

Tissue segmentation is an important pre-requisite for efficient and accurate
diagnostics in digital pathology. However, it is well known that whole-slide
scanners can fail in detecting all tissue regions, for example due to the
tissue type, or due to weak staining because their tissue detection algorithms
are not robust enough. In this paper, we introduce two different convolutional
neural network architectures for whole slide image segmentation to accurately
identify the tissue sections. We also compare the algorithms to a published
traditional method. We collected 54 whole slide images with differing stains
and tissue types from three laboratories to validate our algorithms. We show
that while the two methods do not differ significantly they outperform their
traditional counterpart (Jaccard index of 0.937 and 0.929 vs. 0.870, p < 0.01).
Graph Partitioning with Acyclicity Constraints
Authors: Orlando Moreira, Merten Popp, Christian Schulz
Date: 2017-04-03 00:00:00
Tasks: graph partitioning, Scheduling

Graphs are widely used to model execution dependencies in applications. In
particular, the NP-complete problem of partitioning a graph under constraints
receives enormous attention by researchers because of its applicability in
multiprocessor scheduling. We identified the additional constraint of acyclic
dependencies between blocks when mapping computer vision and imaging
applications to a heterogeneous embedded multiprocessor. Existing algorithms
and heuristics do not address this requirement and deliver results that are not
applicable for our use-case. In this work, we show that this more constrained
version of the graph partitioning problem is NP-complete and present heuristics
that achieve a close approximation of the optimal solution found by an
exhaustive search for small problem instances and much better scalability for
larger instances. In addition, we can show a positive impact on the schedule of
a real imaging application that improves communication volume and execution
time.
Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO
Authors: Luís F. Simões, Dario Izzo, Evert Haasdijk, A. E. Eiben
Date: 2017-04-03 00:00:00
Tasks: Bilevel Optimization

The design of spacecraft trajectories for missions visiting multiple
celestial bodies is here framed as a multi-objective bilevel optimization
problem. A comparative study is performed to assess the performance of
different Beam Search algorithms at tackling the combinatorial problem of
finding the ideal sequence of bodies. Special focus is placed on the
development of a new hybridization between Beam Search and the Population-based
Ant Colony Optimization algorithm. An experimental evaluation shows all
algorithms achieving exceptional performance on a hard benchmark problem. It is
found that a properly tuned deterministic Beam Search always outperforms the
remaining variants. Beam P-ACO, however, demonstrates lower parameter
sensitivity, while offering superior worst-case performance. Being an anytime
algorithm, it is then found to be the preferable choice for certain practical
applications.
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs
Authors: Li Jing, Yichen Shen, Tena Dubček, John Peurifoy, Scott Skirlo, Yann Lecun, Max Tegmark, Marin Soljačić
Date: 2016-12-15 00:00:00
Proceeding: ICML 2017 8
Tasks: Permuted-MNIST

Using unitary (instead of general) matrices in artificial neural networks
(ANNs) is a promising way to solve the gradient explosion/vanishing problem, as
well as to enable ANNs to learn long-term correlations in the data. This
approach appears particularly promising for Recurrent Neural Networks (RNNs).
In this work, we present a new architecture for implementing an Efficient
Unitary Neural Network (EUNNs); its main advantages can be summarized as
follows. Firstly, the representation capacity of the unitary space in an EUNN
is fully tunable, ranging from a subspace of SU(N) to the entire unitary space.
Secondly, the computational complexity for training an EUNN is merely
$\mathcal{O}(1)$ per parameter. Finally, we test the performance of EUNNs on
the standard copying task, the pixel-permuted MNIST digit recognition benchmark
as well as the Speech Prediction Test (TIMIT). We find that our architecture
significantly outperforms both other state-of-the-art unitary RNNs and the LSTM
architecture, in terms of the final performance and/or the wall-clock training
speed. EUNNs are thus promising alternatives to RNNs and LSTMs for a wide
variety of applications.