pwc-archive/papers-with-abstracts huggingface dataset:
Fantasy Football Prediction
Authors: Roman Lutz
Date: 2015-05-26 00:00:00
Tasks: Prediction

The ubiquity of professional sports and specifically the NFL have lead to an
increase in popularity for Fantasy Football. Users have many tools at their
disposal: statistics, predictions, rankings of experts and even recommendations
of peers. There are issues with all of these, though. Especially since many
people pay money to play, the prediction tools should be enhanced as they
provide unbiased and easy-to-use assistance for users. This paper provides and
discusses approaches to predict Fantasy Football scores of Quarterbacks with
relatively limited data. In addition to that, it includes several suggestions
on how the data could be enhanced to achieve better results. The dataset
consists only of game data from the last six NFL seasons. I used two different
methods to predict the Fantasy Football scores of NFL players: Support Vector
Regression (SVR) and Neural Networks. The results of both are promising given
the limited data that was used.
Large-scale Machine Learning for Metagenomics Sequence Classification
Authors: Kévin Vervier, Pierre Mahé, Maud Tournoud, Jean-Baptiste Veyrieras, Jean-Philippe Vert
Date: 2015-05-26 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

Metagenomics characterizes the taxonomic diversity of microbial communities
by sequencing DNA directly from an environmental sample. One of the main
challenges in metagenomics data analysis is the binning step, where each
sequenced read is assigned to a taxonomic clade. Due to the large volume of
metagenomics datasets, binning methods need fast and accurate algorithms that
can operate with reasonable computing requirements. While standard
alignment-based methods provide state-of-the-art performance, compositional
approaches that assign a taxonomic class to a DNA read based on the k-mers it
contains have the potential to provide faster solutions. In this work, we
investigate the potential of modern, large-scale machine learning
implementations for taxonomic affectation of next-generation sequencing reads
based on their k-mers profile. We show that machine learning-based
compositional approaches benefit from increasing the number of fragments
sampled from reference genome to tune their parameters, up to a coverage of
about 10, and from increasing the k-mer size to about 12. Tuning these models
involves training a machine learning model on about 10 8 samples in 10 7
dimensions, which is out of reach of standard soft-wares but can be done
efficiently with modern implementations for large-scale machine learning. The
resulting models are competitive in terms of accuracy with well-established
alignment tools for problems involving a small to moderate number of candidate
species, and for reasonable amounts of sequencing errors. We show, however,
that compositional approaches are still limited in their ability to deal with
problems involving a greater number of species, and more sensitive to
sequencing errors. We finally confirm that compositional approach achieve
faster prediction times, with a gain of 3 to 15 times with respect to the
BWA-MEM short read mapper, depending on the number of candidate species and the
level of sequencing noise.
Using Dimension Reduction to Improve the Classification of High-dimensional Data
Authors: Andreas Grünauer, Markus Vincze
Date: 2015-05-26 00:00:00
Tasks: Dimensionality Reduction, feature selection, General Classification, Vocal Bursts Intensity Prediction

In this work we show that the classification performance of high-dimensional
structural MRI data with only a small set of training examples is improved by
the usage of dimension reduction methods. We assessed two different dimension
reduction variants: feature selection by ANOVA F-test and feature
transformation by PCA. On the reduced datasets, we applied common learning
algorithms using 5-fold cross-validation. Training, tuning of the
hyperparameters, as well as the performance evaluation of the classifiers was
conducted using two different performance measures: Accuracy, and Receiver
Operating Characteristic curve (AUC). Our hypothesis is supported by
experimental results.
Monte Carlo Planning method estimates planning horizons during interactive social exchange
Authors: Andreas Hula, P. Read Montague, Peter Dayan
Date: 2015-02-12 00:00:00

Reciprocating interactions represent a central feature of all human
exchanges. They have been the target of various recent experiments, with
healthy participants and psychiatric populations engaging as dyads in
multi-round exchanges such as a repeated trust task. Behaviour in such
exchanges involves complexities related to each agent's preference for equity
with their partner, beliefs about the partner's appetite for equity, beliefs
about the partner's model of their partner, and so on. Agents may also plan
different numbers of steps into the future. Providing a computationally precise
account of the behaviour is an essential step towards understanding what
underlies choices. A natural framework for this is that of an interactive
partially observable Markov decision process (IPOMDP). However, the various
complexities make IPOMDPs inordinately computationally challenging. Here, we
show how to approximate the solution for the multi-round trust task using a
variant of the Monte-Carlo tree search algorithm. We demonstrate that the
algorithm is efficient and effective, and therefore can be used to invert
observations of behavioural choices. We use generated behaviour to elucidate
the richness and sophistication of interactive inference.
Implementing feedback in creative systems: A workshop approach
Authors: Joseph Corneli, Anna Jordanous
Date: 2015-05-26 00:00:00
Tasks: Anatomy

One particular challenge in AI is the computational modelling and simulation
of creativity. Feedback and learning from experience are key aspects of the
creative process. Here we investigate how we could implement feedback in
creative systems using a social model. From the field of creative writing we
borrow the concept of a Writers Workshop as a model for learning through
feedback. The Writers Workshop encourages examination, discussion and debates
of a piece of creative work using a prescribed format of activities. We propose
a computational model of the Writers Workshop as a roadmap for incorporation of
feedback in artificial creativity systems. We argue that the Writers Workshop
setting describes the anatomy of the creative process. We support our claim
with a case study that describes how to implement the Writers Workshop model in
a computational creativity system. We present this work using patterns other
people can follow to implement similar designs in their own systems. We
conclude by discussing the broader relevance of this model to other aspects of
AI.
Discrete Independent Component Analysis (DICA) with Belief Propagation
Authors: Francesco A. N. Palmieri, Amedeo Buonanno
Date: 2015-05-26 00:00:00

We apply belief propagation to a Bayesian bipartite graph composed of
discrete independent hidden variables and discrete visible variables. The
network is the Discrete counterpart of Independent Component Analysis (DICA)
and it is manipulated in a factor graph form for inference and learning. A full
set of simulations is reported for character images from the MNIST dataset. The
results show that the factorial code implemented by the sources contributes to
build a good generative model for the data that can be used in various
inference modes.
Surrogate Functions for Maximizing Precision at the Top
Authors: Purushottam Kar, Harikrishna Narasimhan, Prateek Jain
Date: 2015-05-26 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

The problem of maximizing precision at the top of a ranked list, often dubbed
Precision@k (prec@k), finds relevance in myriad learning applications such as
ranking, multi-label classification, and learning with severe label imbalance.
However, despite its popularity, there exist significant gaps in our
understanding of this problem and its associated performance measure.
  The most notable of these is the lack of a convex upper bounding surrogate
for prec@k. We also lack scalable perceptron and stochastic gradient descent
algorithms for optimizing this performance measure. In this paper we make key
contributions in these directions. At the heart of our results is a family of
truly upper bounding surrogates for prec@k. These surrogates are motivated in a
principled manner and enjoy attractive properties such as consistency to prec@k
under various natural margin/noise conditions.
  These surrogates are then used to design a class of novel perceptron
algorithms for optimizing prec@k with provable mistake bounds. We also devise
scalable stochastic gradient descent style methods for this problem with
provable convergence bounds. Our proofs rely on novel uniform convergence
bounds which require an in-depth analysis of the structural properties of
prec@k and its surrogates. We conclude with experimental results comparing our
algorithms with state-of-the-art cutting plane and stochastic gradient
algorithms for maximizing prec@k.
Optimizing Non-decomposable Performance Measures: A Tale of Two Classes
Authors: Harikrishna Narasimhan, Purushottam Kar, Prateek Jain
Date: 2015-05-26 00:00:00
Tasks: General Classification, Vocal Bursts Valence Prediction

Modern classification problems frequently present mild to severe label
imbalance as well as specific requirements on classification characteristics,
and require optimizing performance measures that are non-decomposable over the
dataset, such as F-measure. Such measures have spurred much interest and pose
specific challenges to learning algorithms since their non-additive nature
precludes a direct application of well-studied large scale optimization methods
such as stochastic gradient descent.
  In this paper we reveal that for two large families of performance measures
that can be expressed as functions of true positive/negative rates, it is
indeed possible to implement point stochastic updates. The families we consider
are concave and pseudo-linear functions of TPR, TNR which cover several
popularly used performance measures such as F-measure, G-mean and H-mean.
  Our core contribution is an adaptive linearization scheme for these families,
using which we develop optimization techniques that enable truly point-based
stochastic updates. For concave performance measures we propose SPADE, a
stochastic primal dual solver; for pseudo-linear measures we propose STAMP, a
stochastic alternate maximization procedure. Both methods have crisp
convergence guarantees, demonstrate significant speedups over existing methods
- often by an order of magnitude or more, and give similar or more accurate
predictions on test data.
MLlib: Machine Learning in Apache Spark
Authors: Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar
Date: 2015-05-26 00:00:00
Tasks: BIG-bench Machine Learning

Apache Spark is a popular open-source platform for large-scale data
processing that is well-suited for iterative machine learning tasks. In this
paper we present MLlib, Spark's open-source distributed machine learning
library. MLlib provides efficient functionality for a wide range of learning
settings and includes several underlying statistical, optimization, and linear
algebra primitives. Shipped with Spark, MLlib supports several languages and
provides a high-level API that leverages Spark's rich ecosystem to simplify the
development of end-to-end machine learning pipelines. MLlib has experienced a
rapid growth due to its vibrant open-source community of over 140 contributors,
and includes extensive documentation to support further growth and to let users
quickly get up to speed.
Boosting-like Deep Learning For Pedestrian Detection
Authors: Lei Wang, Baochang Zhang
Date: 2015-05-26 00:00:00
Tasks: Deep Learning, Pedestrian Detection

This paper proposes boosting-like deep learning (BDL) framework for
pedestrian detection. Due to overtraining on the limited training samples,
overfitting is a major problem of deep learning. We incorporate a boosting-like
technique into deep learning to weigh the training samples, and thus prevent
overtraining in the iterative process. We theoretically give the details of
derivation of our algorithm, and report the experimental results on open data
sets showing that BDL achieves a better stable performance than the
state-of-the-arts. Our approach achieves 15.85% and 3.81% reduction in the
average miss rate compared with ACF and JointDeep on the largest Caltech
benchmark dataset, respectively.
Location Recognition Over Large Time Lags
Authors: Basura Fernando, Tatiana Tommasi, Tinne Tuytelaars
Date: 2014-09-26 00:00:00
Tasks: Domain Adaptation

Would it be possible to automatically associate ancient pictures to modern
ones and create fancy cultural heritage city maps? We introduce here the task
of recognizing the location depicted in an old photo given modern annotated
images collected from the Internet. We present an extensive analysis on
different features, looking for the most discriminative and most robust to the
image variability induced by large time lags. Moreover, we show that the
described task benefits from domain adaptation.
VeinPLUS: A Transillumination and Reflection-based Hand Vein Database
Authors: Alexander Gruschina
Date: 2015-05-25 00:00:00

This paper gives a short summary of work related to the creation of a
department-hosted hand vein database. After the introducing section, special
properties of the hand vein acquisition are explained, followed by a comparison
table, which shows key differences to existing well-known hand vein databases.
At the end, the ROI extraction process is described and sample images and ROIs
are presented.
Agnostic Learning of Disjunctions on Symmetric Distributions
Authors: Vitaly Feldman, Pravesh Kothari
Date: 2014-05-27 00:00:00

We consider the problem of approximating and learning disjunctions (or
equivalently, conjunctions) on symmetric distributions over $\{0,1\}^n$.
Symmetric distributions are distributions whose PDF is invariant under any
permutation of the variables. We give a simple proof that for every symmetric
distribution $\mathcal{D}$, there exists a set of $n^{O(\log{(1/\epsilon)})}$
functions $\mathcal{S}$, such that for every disjunction $c$, there is function
$p$, expressible as a linear combination of functions in $\mathcal{S}$, such
that $p$ $\epsilon$-approximates $c$ in $\ell_1$ distance on $\mathcal{D}$ or
$\mathbf{E}_{x \sim \mathcal{D}}[ |c(x)-p(x)|] \leq \epsilon$. This directly
gives an agnostic learning algorithm for disjunctions on symmetric
distributions that runs in time $n^{O( \log{(1/\epsilon)})}$. The best known
previous bound is $n^{O(1/\epsilon^4)}$ and follows from approximation of the
more general class of halfspaces (Wimmer, 2010). We also show that there exists
a symmetric distribution $\mathcal{D}$, such that the minimum degree of a
polynomial that $1/3$-approximates the disjunction of all $n$ variables is
$\ell_1$ distance on $\mathcal{D}$ is $\Omega( \sqrt{n})$. Therefore the
learning result above cannot be achieved via $\ell_1$-regression with a
polynomial basis used in most other agnostic learning algorithms.
  Our technique also gives a simple proof that for any product distribution
$\mathcal{D}$ and every disjunction $c$, there exists a polynomial $p$ of
degree $O(\log{(1/\epsilon)})$ such that $p$ $\epsilon$-approximates $c$ in
$\ell_1$ distance on $\mathcal{D}$. This was first proved by Blais et al.
(2008) via a more involved argument.
Stochastic Annealing for Variational Inference
Authors: San Gultekin, Aonan Zhang, John Paisley
Date: 2015-05-25 00:00:00
Tasks: Variational Inference

We empirically evaluate a stochastic annealing strategy for Bayesian
posterior optimization with variational inference. Variational inference is a
deterministic approach to approximate posterior inference in Bayesian models in
which a typically non-convex objective function is locally optimized over the
parameters of the approximating distribution. We investigate an annealing
method for optimizing this objective with the aim of finding a better local
optimal solution and compare with deterministic annealing methods and no
annealing. We show that stochastic annealing can provide clear improvement on
the GMM and HMM, while performance on LDA tends to favor deterministic
annealing methods.
Smooth and iteratively Restore: A simple and fast edge-preserving smoothing model
Authors: Philipp Kniefacz, Walter Kropatsch
Date: 2015-05-25 00:00:00

In image processing, it can be a useful pre-processing step to smooth away
small structures, such as noise or unimportant details, while retaining the
overall structure of the image by keeping edges, which separate objects, sharp.
Typically this edge-preserving smoothing process is achieved using edge-aware
filters. However such filters may preserve unwanted small structures as well if
they contain edges. In this work we present a novel framework for
edge-preserving smoothing which separates the process into two different steps:
First the image is smoothed using a blurring filter and in the second step the
important edges are restored using a guided edge-aware filter. The presented
method proves to deliver very good results, compared to state-of-the-art
edge-preserving smoothing filters, especially at removing unwanted small
structures. Furthermore it is very versatile and can easily be adapted to
different fields of applications while at the same time being very fast to
compute and therefore well-suited for real time applications.
Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares -- ICML
Authors: Garvesh Raskutti, Michael Mahoney
Date: 2015-05-25 00:00:00

We consider statistical and algorithmic aspects of solving large-scale
least-squares (LS) problems using randomized sketching algorithms. Prior
results show that, from an \emph{algorithmic perspective}, when using sketching
matrices constructed from random projections and leverage-score sampling, if
the number of samples $r$ much smaller than the original sample size $n$, then
the worst-case (WC) error is the same as solving the original problem, up to a
very small relative error. From a \emph{statistical perspective}, one typically
considers the mean-squared error performance of randomized sketching
algorithms, when data are generated according to a statistical linear model. In
this paper, we provide a rigorous comparison of both perspectives leading to
insights on how they differ. To do this, we first develop a framework for
assessing, in a unified manner, algorithmic and statistical aspects of
randomized sketching methods. We then consider the statistical prediction
efficiency (PE) and the statistical residual efficiency (RE) of the sketched LS
estimator; and we use our framework to provide upper bounds for several types
of random projection and random sampling algorithms. Among other results, we
show that the RE can be upper bounded when $r$ is much smaller than $n$, while
the PE typically requires the number of samples $r$ to be substantially larger.
Lower bounds developed in subsequent work show that our upper bounds on PE can
not be improved.
Recognition Confidence Analysis of Handwritten Chinese Character with CNN
Authors: Meijun He, Shuye Zhang, Huiyun Mao, Lianwen Jin
Date: 2015-05-25 00:00:00
Tasks: regression

In this paper, we present an effective method to analyze the recognition
confidence of handwritten Chinese character, based on the softmax regression
score of a high performance convolutional neural networks (CNN). Through
careful and thorough statistics of 827,685 testing samples that randomly
selected from total 8836 different classes of Chinese characters, we find that
the confidence measurement based on CNN is an useful metric to know how
reliable the recognition results are. Furthermore, we find by experiments that
the recognition confidence can be used to find out similar and confusable
character-pairs, to check wrongly or cursively written samples, and even to
discover and correct mis-labelled samples. Many interesting observations and
statistics are given and analyzed in this study.
Electre Tri-Machine Learning Approach to the Record Linkage Problem
Authors: Renato De Leone, Valentina Minnetti
Date: 2015-05-25 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Ordinal Classification

In this short paper, the Electre Tri-Machine Learning Method, generally used
to solve ordinal classification problems, is proposed for solving the Record
Linkage problem. Preliminary experimental results show that, using the Electre
Tri method, high accuracy can be achieved and more than 99% of the matches and
nonmatches were correctly identified by the procedure.
Fast Detection of Curved Edges at Low SNR
Authors: Nati Ofir, Meirav Galun, Boaz Nadler, Ronen Basri
Date: 2015-05-25 00:00:00
Proceeding: CVPR 2016 6
Tasks: Edge Detection

Detecting edges is a fundamental problem in computer vision with many
applications, some involving very noisy images. While most edge detection
methods are fast, they perform well only on relatively clean images. Indeed,
edges in such images can be reliably detected using only local filters.
Detecting faint edges under high levels of noise cannot be done locally at the
individual pixel level, and requires more sophisticated global processing.
Unfortunately, existing methods that achieve this goal are quite slow. In this
paper we develop a novel multiscale method to detect curved edges in noisy
images. While our algorithm searches for edges over a huge set of candidate
curves, it does so in a practical runtime, nearly linear in the total number of
image pixels. As we demonstrate experimentally, our algorithm is orders of
magnitude faster than previous methods designed to deal with high noise levels.
Nevertheless, it obtains comparable, if not better, edge detection quality on a
variety of challenging noisy images.
Boosting Named Entity Recognition with Neural Character Embeddings
Authors: Cicero Nogueira dos Santos, Victor Guimarães
Date: 2015-05-19 00:00:00
Proceeding: WS 2015 7
Tasks: Chunking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Part-Of-Speech Tagging, POS, POS Tagging, Single Particle Analysis

Most state-of-the-art named entity recognition (NER) systems rely on
handcrafted features and on the output of other NLP tasks such as
part-of-speech (POS) tagging and text chunking. In this work we propose a
language-independent NER system that uses automatically learned features only.
Our approach is based on the CharWNN deep neural network, which uses word-level
and character-level representations (embeddings) to perform sequential
classification. We perform an extensive number of experiments using two
annotated corpora in two different languages: HAREM I corpus, which contains
texts in Portuguese; and the SPA CoNLL-2002 corpus, which contains texts in
Spanish. Our experimental results shade light on the contribution of neural
character embeddings for NER. Moreover, we demonstrate that the same neural
network which has been successfully applied to POS tagging can also achieve
state-of-the-art results for language-independet NER, using the same
hyperparameters, and without any handcrafted features. For the HAREM I corpus,
CharWNN outperforms the state-of-the-art system by 7.9 points in the F1-score
for the total scenario (ten NE classes), and by 7.2 points in the F1 for the
selective scenario (five NE classes).