pwc-archive/papers-with-abstracts huggingface dataset:
Finding Alternate Features in Lasso
Authors: Satoshi Hara, Takanori Maehara
Date: 2016-11-18 00:00:00

We propose a method for finding alternate features missing in the Lasso
optimal solution. In ordinary Lasso problem, one global optimum is obtained and
the resulting features are interpreted as task-relevant features. However, this
can overlook possibly relevant features not selected by the Lasso. With the
proposed method, we can provide not only the Lasso optimal solution but also
possible alternate features to the Lasso solution. We show that such alternate
features can be computed efficiently by avoiding redundant computations. We
also demonstrate how the proposed method works in the 20 newsgroup data, which
shows that reasonable features are found as alternate features.
Sentiment Analysis on Bangla and Romanized Bangla Text (BRBT) using Deep Recurrent models
Authors: A. Hassan, M. R. Amin, N. Mohammed, A. K. A. Azad
Date: 2016-10-02 00:00:00
Tasks: Sentiment Analysis

Sentiment Analysis (SA) is an action research area in the digital age. With
rapid and constant growth of online social media sites and services, and the
increasing amount of textual data such as - statuses, comments, reviews etc.
available in them, application of automatic SA is on the rise. However, most of
the research works on SA in natural language processing (NLP) are based on
English language. Despite being the sixth most widely spoken language in the
world, Bangla still does not have a large and standard dataset. Because of
this, recent research works in Bangla have failed to produce results that can
be both comparable to works done by others and reusable as stepping stones for
future researchers to progress in this field. Therefore, we first tried to
provide a textual dataset - that includes not just Bangla, but Romanized Bangla
texts as well, is substantial, post-processed and multiple validated, ready to
be used in SA experiments. We tested this dataset in Deep Recurrent model,
specifically, Long Short Term Memory (LSTM), using two types of loss functions
- binary crossentropy and categorical crossentropy, and also did some
experimental pre-training by using data from one validation to pre-train the
other and vice versa. Lastly, we documented the results along with some
analysis on them, which were promising.
Teaching and compressing for low VC-dimension
Authors: Shay Moran, Amir Shpilka, Avi Wigderson, Amir Yehudayoff
Date: 2015-02-22 00:00:00

In this work we study the quantitative relation between VC-dimension and two
other basic parameters related to learning and teaching. Namely, the quality of
sample compression schemes and of teaching sets for classes of low
VC-dimension. Let $C$ be a binary concept class of size $m$ and VC-dimension
$d$. Prior to this work, the best known upper bounds for both parameters were
$\log(m)$, while the best lower bounds are linear in $d$. We present
significantly better upper bounds on both as follows. Set $k = O(d 2^d \log
\log |C|)$.
  We show that there always exists a concept $c$ in $C$ with a teaching set
(i.e. a list of $c$-labeled examples uniquely identifying $c$ in $C$) of size
$k$. This problem was studied by Kuhlmann (1999). Our construction implies that
the recursive teaching (RT) dimension of $C$ is at most $k$ as well. The
RT-dimension was suggested by Zilles et al. and Doliwa et al. (2010). The same
notion (under the name partial-ID width) was independently studied by Wigderson
and Yehudayoff (2013). An upper bound on this parameter that depends only on
$d$ is known just for the very simple case $d=1$, and is open even for $d=2$.
We also make small progress towards this seemingly modest goal.
  We further construct sample compression schemes of size $k$ for $C$, with
additional information of $k \log(k)$ bits. Roughly speaking, given any list of
$C$-labelled examples of arbitrary length, we can retain only $k$ labeled
examples in a way that allows to recover the labels of all others examples in
the list, using additional $k\log (k)$ information bits. This problem was first
suggested by Littlestone and Warmuth (1986).
Infinite Variational Autoencoder for Semi-Supervised Learning
Authors: Ehsan Abbasnejad, Anthony Dick, Anton Van Den Hengel
Date: 2016-11-23 00:00:00
Proceeding: CVPR 2017 7

This paper presents an infinite variational autoencoder (VAE) whose capacity
adapts to suit the input data. This is achieved using a mixture model where the
mixing coefficients are modeled by a Dirichlet process, allowing us to
integrate over the coefficients when performing inference. Critically, this
then allows us to automatically vary the number of autoencoders in the mixture
based on the data. Experiments show the flexibility of our method, particularly
for semi-supervised learning, where only a small number of training samples are
available.
Algebraic multigrid support vector machines
Authors: Ehsan Sadrfaridpour, Sandeep Jeereddy, Ken Kennedy, Andre Luckow, Talayeh Razzaghi, Ilya Safro
Date: 2016-11-16 00:00:00
Tasks: General Classification

The support vector machine is a flexible optimization-based technique widely
used for classification problems. In practice, its training part becomes
computationally expensive on large-scale data sets because of such reasons as
the complexity and number of iterations in parameter fitting methods,
underlying optimization solvers, and nonlinearity of kernels. We introduce a
fast multilevel framework for solving support vector machine models that is
inspired by the algebraic multigrid. Significant improvement in the running has
been achieved without any loss in the quality. The proposed technique is highly
beneficial on imbalanced sets. We demonstrate computational results on publicly
available and industrial data sets.
Towards a new quantum cognition model
Authors: Riccardo Franco
Date: 2016-11-23 00:00:00
Tasks: model

This article presents a new quantum-like model for cognition explicitly based
on knowledge. It is shown that this model, called QKT (quantum knowledge-based
theory), is able to coherently describe some experimental results that are
problematic for the prior quantum-like decision models. In particular, I
consider the experimental results relevant to the post-decision cognitive
dissonance, the problems relevant to the question order effect and response
replicability, and those relevant to the grand-reciprocity equations. A new set
of postulates is proposed, which evidence the different meaning given to the
projectors and to the quantum states. In the final part, I show that the use of
quantum gates can help to better describe and understand the evolution of
quantum-like models.
On Design Mining: Coevolution and Surrogate Models
Authors: Richard J. Preen, Larry Bull
Date: 2015-06-29 00:00:00
Tasks: Attribute

Design mining is the use of computational intelligence techniques to
iteratively search and model the attribute space of physical objects evaluated
directly through rapid prototyping to meet given objectives. It enables the
exploitation of novel materials and processes without formal models or complex
simulation. In this paper, we focus upon the coevolutionary nature of the
design process when it is decomposed into concurrent sub-design threads due to
the overall complexity of the task. Using an abstract, tuneable model of
coevolution we consider strategies to sample sub-thread designs for whole
system testing and how best to construct and use surrogate models within the
coevolutionary scenario. Drawing on our findings, the paper then describes the
effective design of an array of six heterogeneous vertical-axis wind turbines.
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
Authors: Lisha Li, Kevin Jamieson, Giulia Desalvo, Afshin Rostamizadeh, Ameet Talwalkar
Date: 2016-03-21 00:00:00
Tasks: Bayesian Optimization, Hyperparameter Optimization

Performance of machine learning algorithms depends critically on identifying
a good set of hyperparameters. While recent approaches use Bayesian
optimization to adaptively select configurations, we focus on speeding up
random search through adaptive resource allocation and early-stopping. We
formulate hyperparameter optimization as a pure-exploration non-stochastic
infinite-armed bandit problem where a predefined resource like iterations, data
samples, or features is allocated to randomly sampled configurations. We
introduce a novel algorithm, Hyperband, for this framework and analyze its
theoretical properties, providing several desirable guarantees. Furthermore, we
compare Hyperband with popular Bayesian optimization methods on a suite of
hyperparameter optimization problems. We observe that Hyperband can provide
over an order-of-magnitude speedup over our competitor set on a variety of
deep-learning and kernel-based learning problems.
New Trends in Neutrosophic Theory and Applications
Authors: Florentin Smarandache, Surapati Pramanik
Date: 2016-11-23 00:00:00
Tasks: Decision Making, Medical Diagnosis

Neutrosophic theory and applications have been expanding in all directions at
an astonishing rate especially after the introduction the journal entitled
Neutrosophic Sets and Systems. New theories, techniques, algorithms have been
rapidly developed. One of the most striking trends in the neutrosophic theory
is the hybridization of neutrosophic set with other potential sets such as
rough set, bipolar set, soft set, hesitant fuzzy set, etc. The different hybrid
structure such as rough neutrosophic set, single valued neutrosophic rough set,
bipolar neutrosophic set, single valued neutrosophic hesitant fuzzy set, etc.
are proposed in the literature in a short period of time. Neutrosophic set has
been a very important tool in all various areas of data mining, decision
making, e-learning, engineering, medicine, social science, and some more. The
book New Trends in Neutrosophic Theories and Applications focuses on theories,
methods, algorithms for decision making and also applications involving
neutrosophic information. Some topics deal with data mining, decision making,
e-learning, graph theory, medical diagnosis, probability theory, topology, and
some more.
Multi-Modal Mean-Fields via Cardinality-Based Clamping
Authors: Pierre Baqué, François Fleuret, Pascal Fua
Date: 2016-11-23 00:00:00
Proceeding: CVPR 2017 7
Tasks: Descriptive

Mean Field inference is central to statistical physics. It has attracted much
interest in the Computer Vision community to efficiently solve problems
expressible in terms of large Conditional Random Fields. However, since it
models the posterior probability distribution as a product of marginal
probabilities, it may fail to properly account for important dependencies
between variables. We therefore replace the fully factorized distribution of
Mean Field by a weighted mixture of such distributions, that similarly
minimizes the KL-Divergence to the true posterior. By introducing two new
ideas, namely, conditioning on groups of variables instead of single ones and
using a parameter of the conditional random field potentials, that we identify
to the temperature in the sense of statistical physics to select such groups,
we can perform this minimization efficiently. Our extension of the clamping
method proposed in previous works allows us to both produce a more descriptive
approximation of the true posterior and, inspired by the diverse MAP paradigms,
fit a mixture of Mean Field approximations. We demonstrate that this positively
impacts real-world algorithms that initially relied on mean fields.
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Authors: Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, Jeff Clune
Date: 2016-05-30 00:00:00
Proceeding: NeurIPS 2016 12

Deep neural networks (DNNs) have demonstrated state-of-the-art results on
many pattern recognition tasks, especially vision classification problems.
Understanding the inner workings of such computational brains is both
fascinating basic science that is interesting in its own right - similar to why
we study the human brain - and will enable researchers to further improve DNNs.
One path to understanding how a neural network functions internally is to study
what each of its neurons has learned to detect. One such method is called
activation maximization (AM), which synthesizes an input (e.g. an image) that
highly activates a neuron. Here we dramatically improve the qualitative state
of the art of activation maximization by harnessing a powerful, learned prior:
a deep generator network (DGN). The algorithm (1) generates qualitatively
state-of-the-art synthetic images that look almost real, (2) reveals the
features learned by each neuron in an interpretable way, (3) generalizes well
to new datasets and somewhat well to different network architectures without
requiring the prior to be relearned, and (4) can be considered as a
high-quality generative method (in this case, by generating novel, creative,
interesting, recognizable images).
The World of Fast Moving Objects
Authors: Denys Rozumnyi, Jan Kotera, Filip Sroubek, Lukas Novotny, Jiri Matas
Date: 2016-11-23 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, Super-Resolution

The notion of a Fast Moving Object (FMO), i.e. an object that moves over a
distance exceeding its size within the exposure time, is introduced. FMOs may,
and typically do, rotate with high angular speed. FMOs are very common in
sports videos, but are not rare elsewhere. In a single frame, such objects are
often barely visible and appear as semi-transparent streaks.
  A method for the detection and tracking of FMOs is proposed. The method
consists of three distinct algorithms, which form an efficient localization
pipeline that operates successfully in a broad range of conditions. We show
that it is possible to recover the appearance of the object and its axis of
rotation, despite its blurred appearance. The proposed method is evaluated on a
new annotated dataset. The results show that existing trackers are inadequate
for the problem of FMO localization and a new approach is required. Two
applications of localization, temporal super-resolution and highlighting, are
presented.
Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo
Authors: Paul Fearnhead, Joris Bierkens, Murray Pollock, Gareth O. Roberts
Date: 2016-11-23 00:00:00

Recently there have been exciting developments in Monte Carlo methods, with
the development of new MCMC and sequential Monte Carlo (SMC) algorithms which
are based on continuous-time, rather than discrete-time, Markov processes. This
has led to some fundamentally new Monte Carlo algorithms which can be used to
sample from, say, a posterior distribution. Interestingly, continuous-time
algorithms seem particularly well suited to Bayesian analysis in big-data
settings as they need only access a small sub-set of data points at each
iteration, and yet are still guaranteed to target the true posterior
distribution. Whilst continuous-time MCMC and SMC methods have been developed
independently we show here that they are related by the fact that both involve
simulating a piecewise deterministic Markov process. Furthermore we show that
the methods developed to date are just specific cases of a potentially much
wider class of continuous-time Monte Carlo algorithms. We give an informal
introduction to piecewise deterministic Markov processes, covering the aspects
relevant to these new Monte Carlo algorithms, with a view to making the
development of new continuous-time Monte Carlo more accessible. We focus on how
and why sub-sampling ideas can be used with these algorithms, and aim to give
insight into how these new algorithms can be implemented, and what are some of
the issues that affect their efficiency.
Cruciform: Solving Crosswords with Natural Language Processing
Authors: Dragomir Radev, Rui Zhang, Steve Wilson, Derek Van Assche, Henrique Spyra Gubert, Alisa Krivokapic, MeiXing Dong, Chongruo wu, Spruce Bondera, Luke Brandl, Jeremy Dohmann
Date: 2016-11-08 00:00:00

Crossword puzzles are popular word games that require not only a large
vocabulary, but also a broad knowledge of topics. Answering each clue is a
natural language task on its own as many clues contain nuances, puns, or
counter-intuitive word definitions. Additionally, it can be extremely difficult
to ascertain definitive answers without the constraints of the crossword grid
itself. This task is challenging for both humans and computers. We describe
here a new crossword solving system, Cruciform. We employ a group of natural
language components, each of which returns a list of candidate words with
scores when given a clue. These lists are used in conjunction with the fill
intersections in the puzzle grid to formulate a constraint satisfaction
problem, in a manner similar to the one used in the Dr. Fill system. We
describe the results of several of our experiments with the system.
Robust Unsupervised Transient Detection With Invariant Representation based on the Scattering Network
Authors: Randall Balestriero, Behnaam Aazhang
Date: 2016-11-23 00:00:00

We present a sparse and invariant representation with low asymptotic
complexity for robust unsupervised transient and onset zone detection in noisy
environments. This unsupervised approach is based on wavelet transforms and
leverages the scattering network from Mallat et al. by deriving frequency
invariance. This frequency invariance is a key concept to enforce robust
representations of transients in presence of possible frequency shifts and
perturbations occurring in the original signal. Implementation details as well
as complexity analysis are provided in addition of the theoretical framework
and the invariance properties. In this work, our primary application consists
of predicting the onset of seizure in epileptic patients from subdural
recordings as well as detecting inter-ictal spikes.
Parsimonious modeling with Information Filtering Networks
Authors: Wolfram Barfuss, Guido Previde Massara, T. Di Matteo, Tomaso Aste
Date: 2016-02-23 00:00:00
Tasks: Time Series, Time Series Analysis

We introduce a methodology to construct parsimonious probabilistic models.
This method makes use of Information Filtering Networks to produce a robust
estimate of the global sparse inverse covariance from a simple sum of local
inverse covariances computed on small sub-parts of the network. Being based on
local and low-dimensional inversions, this method is computationally very
efficient and statistically robust even for the estimation of inverse
covariance of high-dimensional, noisy and short time-series. Applied to
financial data our method results computationally more efficient than
state-of-the-art methodologies such as Glasso producing, in a fraction of the
computation time, models that can have equivalent or better performances but
with a sparser inference structure. We also discuss performances with sparse
factor models where we notice that relative performances decrease with the
number of factors. The local nature of this approach allows us to perform
computations in parallel and provides a tool for dynamical adaptation by
partial updating when the properties of some variables change without the need
of recomputing the whole model. This makes this approach particularly suitable
to handle big datasets with large numbers of variables. Examples of practical
application for forecasting, stress testing and risk allocation in financial
systems are also provided.
fastFM: A Library for Factorization Machines
Authors: Immanuel Bayer
Date: 2015-05-04 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Recommendation Systems, regression

Factorization Machines (FM) are only used in a narrow range of applications
and are not part of the standard toolbox of machine learning models. This is a
pity, because even though FMs are recognized as being very successful for
recommender system type applications they are a general model to deal with
sparse and high dimensional features. Our Factorization Machine implementation
provides easy access to many solvers and supports regression, classification
and ranking tasks. Such an implementation simplifies the use of FM's for a wide
field of applications. This implementation has the potential to improve our
understanding of the FM model and drive new development.
ATR4S: Toolkit with State-of-the-art Automatic Terms Recognition Methods in Scala
Authors: N. Astrakhantsev
Date: 2016-11-23 00:00:00
Tasks: Information Retrieval, Machine Translation, Retrieval, Sentiment Analysis, Translation

Automatically recognized terminology is widely used for various
domain-specific texts processing tasks, such as machine translation,
information retrieval or sentiment analysis. However, there is still no
agreement on which methods are best suited for particular settings and,
moreover, there is no reliable comparison of already developed methods. We
believe that one of the main reasons is the lack of state-of-the-art methods
implementations, which are usually non-trivial to recreate. In order to address
these issues, we present ATR4S, an open-source software written in Scala that
comprises more than 15 methods for automatic terminology recognition (ATR) and
implements the whole pipeline from text document preprocessing, to term
candidates collection, term candidates scoring, and finally, term candidates
ranking. It is highly scalable, modular and configurable tool with support of
automatic caching. We also compare 10 state-of-the-art methods on 7 open
datasets by average precision and processing time. Experimental comparison
reveals that no single method demonstrates best average precision for all
datasets and that other available tools for ATR do not contain the best
methods.
Object Detection using Image Processing
Authors: Fares Jalled, Ilia Voronkov
Date: 2016-11-23 00:00:00
Tasks: Face Detection, Object, object-detection, Object Detection

An Unmanned Ariel vehicle (UAV) has greater importance in the army for border
security. The main objective of this article is to develop an OpenCV-Python
code using Haar Cascade algorithm for object and face detection. Currently,
UAVs are used for detecting and attacking the infiltrated ground targets. The
main drawback for this type of UAVs is that sometimes the object are not
properly detected, which thereby causes the object to hit the UAV. This project
aims to avoid such unwanted collisions and damages of UAV. UAV is also used for
surveillance that uses Voila-jones algorithm to detect and track humans. This
algorithm uses cascade object detector function and vision. train function to
train the algorithm. The main advantage of this code is the reduced processing
time. The Python code was tested with the help of available database of video
and image, the output was verified.