pwc-archive/papers-with-abstracts huggingface dataset:
Weakly supervised clustering: Learning fine-grained signals from coarse labels
Authors: Stefan Wager, Alexander Blocker, Niall Cardin
Date: 2013-10-04 00:00:00
Tasks: Classification, Clustering, General Classification

Consider a classification problem where we do not have access to labels for
individual training examples, but only have average labels over subpopulations.
We give practical examples of this setup and show how such a classification
task can usefully be analyzed as a weakly supervised clustering problem. We
propose three approaches to solving the weakly supervised clustering problem,
including a latent variables model that performs well in our experiments. We
illustrate our methods on an analysis of aggregated elections data and an
industry data set that was the original motivation for this research.
Neuron detection in stack images: a persistent homology interpretation
Authors: Jónathan Heras, Gadea Mata, Germán Cuesto, Julio Rubio, Miguel Morales
Date: 2015-09-15 00:00:00

Automation and reliability are the two main requirements when computers are
applied in Life Sciences. In this paper we report on an application to neuron
recognition, an important step in our long-term project of providing software
systems to the study of neural morphology and functionality from biomedical
images. Our algorithms have been implemented in an ImageJ plugin called
NeuronPersistentJ, which has been validated experimentally. The soundness and
reliability of our approach are based on the interpretation of our processing
methods with respect to persistent homology, a well-known tool in computational
mathematics.
A Random Matrix Theoretical Approach to Early Event Detection in Smart Grid
Authors: Xing He, Robert Caiming Qiu, Qian Ai, Yinshuang Cao, Jie Gu, Zhijian Jin
Date: 2015-01-31 00:00:00
Tasks: Anomaly Detection, Decision Making, Event Detection

Power systems are developing very fast nowadays, both in size and in
complexity; this situation is a challenge for Early Event Detection (EED). This
paper proposes a data- driven unsupervised learning method to handle this
challenge. Specifically, the random matrix theories (RMTs) are introduced as
the statistical foundations for random matrix models (RMMs); based on the RMMs,
linear eigenvalue statistics (LESs) are defined via the test functions as the
system indicators. By comparing the values of the LES between the experimental
and the theoretical ones, the anomaly detection is conducted. Furthermore, we
develop 3D power-map to visualize the LES; it provides a robust auxiliary
decision-making mechanism to the operators. In this sense, the proposed method
conducts EED with a pure statistical procedure, requiring no knowledge of
system topologies, unit operation/control models, etc. The LES, as a key
ingredient during this procedure, is a high dimensional indictor derived
directly from raw data. As an unsupervised learning indicator, the LES is much
more sensitive than the low dimensional indictors obtained from supervised
learning. With the statistical procedure, the proposed method is universal and
fast; moreover, it is robust against traditional EED challenges (such as error
accumulations, spurious correlations, and even bad data in core area). Case
studies, with both simulated data and real ones, validate the proposed method.
To manage large-scale distributed systems, data fusion is mentioned as another
data processing ingredient.
Analyzing structural characteristics of object category representations from their semantic-part distributions
Authors: Ravi Kiran Sarvadevabhatla, Venkatesh Babu R
Date: 2015-09-15 00:00:00
Tasks: Object, Object Recognition

Studies from neuroscience show that part-mapping computations are employed by
human visual system in the process of object recognition. In this work, we
present an approach for analyzing semantic-part characteristics of object
category representations. For our experiments, we use category-epitome, a
recently proposed sketch-based spatial representation for objects. To enable
part-importance analysis, we first obtain semantic-part annotations of
hand-drawn sketches originally used to construct the corresponding epitomes. We
then examine the extent to which the semantic-parts are present in the epitomes
of a category and visualize the relative importance of parts as a word cloud.
Finally, we show how such word cloud visualizations provide an intuitive
understanding of category-level structural trends that exist in the
category-epitome object representations.
Exponential Family Matrix Completion under Structural Constraints
Authors: Suriya Gunasekar, Pradeep Ravikumar, Joydeep Ghosh
Date: 2015-09-15 00:00:00
Tasks: Matrix Completion

We consider the matrix completion problem of recovering a structured matrix
from noisy and partial measurements. Recent works have proposed tractable
estimators with strong statistical guarantees for the case where the underlying
matrix is low--rank, and the measurements consist of a subset, either of the
exact individual entries, or of the entries perturbed by additive Gaussian
noise, which is thus implicitly suited for thin--tailed continuous data.
Arguably, common applications of matrix completion require estimators for (a)
heterogeneous data--types, such as skewed--continuous, count, binary, etc., (b)
for heterogeneous noise models (beyond Gaussian), which capture varied
uncertainty in the measurements, and (c) heterogeneous structural constraints
beyond low--rank, such as block--sparsity, or a superposition structure of
low--rank plus elementwise sparseness, among others. In this paper, we provide
a vastly unified framework for generalized matrix completion by considering a
matrix completion setting wherein the matrix entries are sampled from any
member of the rich family of exponential family distributions; and impose
general structural constraints on the underlying matrix, as captured by a
general regularizer $\mathcal{R}(.)$. We propose a simple convex regularized
$M$--estimator for the generalized framework, and provide a unified and novel
statistical analysis for this general class of estimators. We finally
corroborate our theoretical results on simulated datasets.
Dependency length minimization: Puzzles and Promises
Authors: Haitao Liu, Chunshan Xu, Junying Liang
Date: 2015-09-15 00:00:00

In the recent issue of PNAS, Futrell et al. claims that their study of 37
languages gives the first large scale cross-language evidence for Dependency
Length Minimization, which is an overstatement that ignores similar previous
researches. In addition,this study seems to pay no attention to factors like
the uniformity of genres,which weakens the validity of the argument that DLM is
universal. Another problem is that this study sets the baseline random language
as projective, which fails to truly uncover the difference between natural
language and random language, since projectivity is an important feature of
many natural languages. Finally, the paper contends an "apparent relationship
between head finality and dependency length" despite the lack of an explicit
statistical comparison, which renders this conclusion rather hasty and
improper.
Precise Phase Transition of Total Variation Minimization
Authors: Bingwen Zhang, Weiyu Xu, Jian-Feng Cai, Lifeng Lai
Date: 2015-09-15 00:00:00
Tasks: compressed sensing, Denoising

Characterizing the phase transitions of convex optimizations in recovering
structured signals or data is of central importance in compressed sensing,
machine learning and statistics. The phase transitions of many convex
optimization signal recovery methods such as $\ell_1$ minimization and nuclear
norm minimization are well understood through recent years' research. However,
rigorously characterizing the phase transition of total variation (TV)
minimization in recovering sparse-gradient signal is still open. In this paper,
we fully characterize the phase transition curve of the TV minimization. Our
proof builds on Donoho, Johnstone and Montanari's conjectured phase transition
curve for the TV approximate message passing algorithm (AMP), together with the
linkage between the minmax Mean Square Error of a denoising problem and the
high-dimensional convex geometry for TV minimization.
Improved Relation Extraction with Feature-Rich Compositional Embedding Models
Authors: Matthew R. Gormley, Mo Yu, Mark Dredze
Date: 2015-05-10 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Relation, Relation Classification, Relation Extraction, Sentence, Word Embeddings

Compositional embedding models build a representation (or embedding) for a
linguistic structure based on its component word embeddings. We propose a
Feature-rich Compositional Embedding Model (FCM) for relation extraction that
is expressive, generalizes to new domains, and is easy-to-implement. The key
idea is to combine both (unlexicalized) hand-crafted features with learned word
embeddings. The model is able to directly tackle the difficulties met by
traditional compositional embeddings models, such as handling arbitrary types
of sentence annotations and utilizing global information for composition. We
test the proposed model on two relation extraction tasks, and demonstrate that
our model outperforms both previous compositional models and traditional
feature rich models on the ACE 2005 relation extraction task, and the SemEval
2010 relation classification task. The combination of our model and a
log-linear classifier with hand-crafted features gives state-of-the-art
results.
Towards Making High Dimensional Distance Metric Learning Practical
Authors: Qi Qian, Rong Jin, Lijun Zhang, Shenghuo Zhu
Date: 2015-09-15 00:00:00
Tasks: Dimensionality Reduction, Metric Learning, Vocal Bursts Intensity Prediction

In this work, we study distance metric learning (DML) for high dimensional
data. A typical approach for DML with high dimensional data is to perform the
dimensionality reduction first before learning the distance metric. The main
shortcoming of this approach is that it may result in a suboptimal solution due
to the subspace removed by the dimensionality reduction method. In this work,
we present a dual random projection frame for DML with high dimensional data
that explicitly addresses the limitation of dimensionality reduction for DML.
The key idea is to first project all the data points into a low dimensional
space by random projection, and compute the dual variables using the projected
vectors. It then reconstructs the distance metric in the original space using
the estimated dual variables. The proposed method, on one hand, enjoys the
light computation of random projection, and on the other hand, alleviates the
limitation of most dimensionality reduction methods. We verify both empirically
and theoretically the effectiveness of the proposed algorithm for high
dimensional DML.
Voted Kernel Regularization
Authors: Corinna Cortes, Prasoon Goyal, Vitaly Kuznetsov, Mehryar Mohri
Date: 2015-09-14 00:00:00
Tasks: General Classification

This paper presents an algorithm, Voted Kernel Regularization , that provides
the flexibility of using potentially very complex kernel functions such as
predictors based on much higher-degree polynomial kernels, while benefitting
from strong learning guarantees. The success of our algorithm arises from
derived bounds that suggest a new regularization penalty in terms of the
Rademacher complexities of the corresponding families of kernel maps. In a
series of experiments we demonstrate the improved performance of our algorithm
as compared to baselines. Furthermore, the algorithm enjoys several favorable
properties. The optimization problem is convex, it allows for learning with
non-PDS kernels, and the solutions are highly sparse, resulting in improved
classification speed and memory requirements.
Learning without Recall by Random Walks on Directed Graphs
Authors: Mohammad Amin Rahimian, Shahin Shahrampour, Ali Jadbabaie
Date: 2015-09-14 00:00:00
Tasks: Bayesian Inference

We consider a network of agents that aim to learn some unknown state of the
world using private observations and exchange of beliefs. At each time, agents
observe private signals generated based on the true unknown state. Each agent
might not be able to distinguish the true state based only on her private
observations. This occurs when some other states are observationally equivalent
to the true state from the agent's perspective. To overcome this shortcoming,
agents must communicate with each other to benefit from local observations. We
propose a model where each agent selects one of her neighbors randomly at each
time. Then, she refines her opinion using her private signal and the prior of
that particular neighbor. The proposed rule can be thought of as a Bayesian
agent who cannot recall the priors based on which other agents make inferences.
This learning without recall approach preserves some aspects of the Bayesian
inference while being computationally tractable. By establishing a
correspondence with a random walk on the network graph, we prove that under the
described protocol, agents learn the truth exponentially fast in the almost
sure sense. The asymptotic rate is expressed as the sum of the relative
entropies between the signal structures of every agent weighted by the
stationary distribution of the random walk.
Analyzing Tensor Power Method Dynamics in Overcomplete Regime
Authors: Anima Anandkumar, Rong Ge, Majid Janzamin
Date: 2014-11-06 00:00:00

We present a novel analysis of the dynamics of tensor power iterations in the
overcomplete regime where the tensor CP rank is larger than the input
dimension. Finding the CP decomposition of an overcomplete tensor is NP-hard in
general. We consider the case where the tensor components are randomly drawn,
and show that the simple power iteration recovers the components with bounded
error under mild initialization conditions. We apply our analysis to
unsupervised learning of latent variable models, such as multi-view mixture
models and spherical Gaussian mixtures. Given the third order moment tensor, we
learn the parameters using tensor power iterations. We prove it can correctly
learn the model parameters when the number of hidden components $k$ is much
larger than the data dimension $d$, up to $k = o(d^{1.5})$. We initialize the
power iterations with data samples and prove its success under mild conditions
on the signal-to-noise ratio of the samples. Our analysis significantly expands
the class of latent variable models where spectral methods are applicable. Our
analysis also deals with noise in the input tensor leading to sample complexity
result in the application to learning latent variable models.
A Practioner's Guide to Evaluating Entity Resolution Results
Authors: Matt Barnes
Date: 2015-09-14 00:00:00
Tasks: Clustering, Entity Resolution, General Classification, Multi-class Classification

Entity resolution (ER) is the task of identifying records belonging to the
same entity (e.g. individual, group) across one or multiple databases.
Ironically, it has multiple names: deduplication and record linkage, among
others. In this paper we survey metrics used to evaluate ER results in order to
iteratively improve performance and guarantee sufficient quality prior to
deployment. Some of these metrics are borrowed from multi-class classification
and clustering domains, though some key differences exist differentiating
entity resolution from general clustering. Menestrina et al. empirically showed
rankings from these metrics often conflict with each other, thus our primary
motivation for studying them. This paper provides practitioners the basic
knowledge to begin evaluating their entity resolution results.
gSLICr: SLIC superpixels at over 250Hz
Authors: Carl Yuheng Ren, Victor Adrian Prisacariu, Ian D. Reid
Date: 2015-09-14 00:00:00
Tasks: Clustering, GPU, Segmentation, Superpixels

We introduce a parallel GPU implementation of the Simple Linear Iterative
Clustering (SLIC) superpixel segmentation. Using a single graphic card, our
implementation achieves speedups of up to $83\times$ from the standard
sequential implementation. Our implementation is fully compatible with the
standard sequential implementation and the software is now available online and
is open source.
Twitter Sentiment Analysis
Authors: Afroze Ibrahim Baqapuri
Date: 2015-09-14 00:00:00
Tasks: Sentiment Analysis, Sentiment Classification, Twitter Sentiment Analysis

This project addresses the problem of sentiment analysis in twitter; that is
classifying tweets according to the sentiment expressed in them: positive,
negative or neutral. Twitter is an online micro-blogging and social-networking
platform which allows users to write short status updates of maximum length 140
characters. It is a rapidly expanding service with over 200 million registered
users - out of which 100 million are active users and half of them log on
twitter on a daily basis - generating nearly 250 million tweets per day. Due to
this large amount of usage we hope to achieve a reflection of public sentiment
by analysing the sentiments expressed in the tweets. Analysing the public
sentiment is important for many applications such as firms trying to find out
the response of their products in the market, predicting political elections
and predicting socioeconomic phenomena like stock exchange. The aim of this
project is to develop a functional classifier for accurate and automatic
sentiment classification of an unknown tweet stream.
Deep Learning Applied to Image and Text Matching
Authors: Afroze Ibrahim Baqapuri
Date: 2015-09-14 00:00:00
Tasks: Deep Learning, Image Retrieval, Information Retrieval, Retrieval, Sentence, Text Matching

The ability to describe images with natural language sentences is the
hallmark for image and language understanding. Such a system has wide ranging
applications such as annotating images and using natural sentences to search
for images.In this project we focus on the task of bidirectional image
retrieval: such asystem is capable of retrieving an image based on a sentence
(image search) andretrieve sentence based on an image query (image annotation).
We present asystem based on a global ranking objective function which uses a
combinationof convolutional neural networks (CNN) and multi layer perceptrons
(MLP).It takes a pair of image and sentence and processes them in different
channels,finally embedding it into a common multimodal vector space. These
embeddingsencode abstract semantic information about the two inputs and can be
comparedusing traditional information retrieval approaches. For each such pair,
the modelreturns a score which is interpretted as a similarity metric. If this
score is high,the image and sentence are likely to convey similar meaning, and
if the score is low then they are likely not to.
  The visual input is modeled via deep convolutional neural network. On
theother hand we explore three models for the textual module. The first one
isbag of words with an MLP. The second one uses n-grams (bigram, trigrams,and a
combination of trigram & skip-grams) with an MLP. The third is morespecialized
deep network specific for modeling variable length sequences (SSE).We report
comparable performance to recent work in the field, even though ouroverall
model is simpler. We also show that the training time choice of how wecan
generate our negative samples has a significant impact on performance, and can
be used to specialize the bi-directional system in one particular task.
Learning to See by Moving
Authors: Pulkit Agrawal, Joao Carreira, Jitendra Malik
Date: 2015-05-07 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object Recognition, Scene Recognition, Visual Odometry

The dominant paradigm for feature learning in computer vision relies on
training neural networks for the task of object recognition using millions of
hand labelled images. Is it possible to learn useful features for a diverse set
of visual tasks using any other form of supervision? In biology, living
organisms developed the ability of visual perception for the purpose of moving
and acting in the world. Drawing inspiration from this observation, in this
work we investigate if the awareness of egomotion can be used as a supervisory
signal for feature learning. As opposed to the knowledge of class labels,
information about egomotion is freely available to mobile agents. We show that
given the same number of training images, features learnt using egomotion as
supervision compare favourably to features learnt using class-label as
supervision on visual tasks of scene recognition, object recognition, visual
odometry and keypoint matching.
Giraffe: Using Deep Reinforcement Learning to Play Chess
Authors: Matthew Lai
Date: 2015-09-04 00:00:00
Tasks: BIG-bench Machine Learning, Deep Reinforcement Learning, Game of Chess, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This report presents Giraffe, a chess engine that uses self-play to discover
all its domain-specific knowledge, with minimal hand-crafted knowledge given by
the programmer. Unlike previous attempts using machine learning only to perform
parameter-tuning on hand-crafted evaluation functions, Giraffe's learning
system also performs automatic feature extraction and pattern recognition. The
trained evaluation function performs comparably to the evaluation functions of
state-of-the-art chess engines - all of which containing thousands of lines of
carefully hand-crafted pattern recognizers, tuned over many years by both
computer chess experts and human chess masters. Giraffe is the most successful
attempt thus far at using end-to-end machine learning to play chess.
Color-Phase Analysis for Sinusoidal Structured Light in Rapid Range Imaging
Authors: Changsoo Je, Sang Wook Lee, Rae-Hong Park
Date: 2015-09-14 00:00:00

Active range sensing using structured-light is the most accurate and reliable
method for obtaining 3D information. However, most of the work has been limited
to range sensing of static objects, and range sensing of dynamic (moving or
deforming) objects has been investigated recently only by a few researchers.
Sinusoidal structured-light is one of the well-known optical methods for 3D
measurement. In this paper, we present a novel method for rapid high-resolution
range imaging using color sinusoidal pattern. We consider the real-world
problem of nonlinearity and color-band crosstalk in the color light projector
and color camera, and present methods for accurate recovery of color-phase. For
high-resolution ranging, we use high-frequency patterns and describe new
unwrapping algorithms for reliable range recovery. The experimental results
demonstrate the effectiveness of our methods.
Benchmarking for Bayesian Reinforcement Learning
Authors: Michael Castronovo, Damien Ernst, Adrien Couetoux, Raphael Fonteneau
Date: 2015-09-14 00:00:00
Tasks: Benchmarking, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In the Bayesian Reinforcement Learning (BRL) setting, agents try to maximise
the collected rewards while interacting with their environment while using some
prior knowledge that is accessed beforehand. Many BRL algorithms have already
been proposed, but even though a few toy examples exist in the literature,
there are still no extensive or rigorous benchmarks to compare them. The paper
addresses this problem, and provides a new BRL comparison methodology along
with the corresponding open source library. In this methodology, a comparison
criterion that measures the performance of algorithms on large sets of Markov
Decision Processes (MDPs) drawn from some probability distributions is defined.
In order to enable the comparison of non-anytime algorithms, our methodology
also includes a detailed analysis of the computation time requirement of each
algorithm. Our library is released with all source code and documentation: it
includes three test problems, each of which has two different prior
distributions, and seven state-of-the-art RL algorithms. Finally, our library
is illustrated by comparing all the available algorithms and the results are
discussed.