pwc-archive/papers-with-abstracts huggingface dataset:
A Detailed Rubric for Motion Segmentation
Authors: Pia Bideau, Erik Learned-Miller
Date: 2016-10-31 00:00:00
Tasks: Motion Segmentation, Segmentation

Motion segmentation is currently an active area of research in computer
Vision. The task of comparing different methods of motion segmentation is
complicated by the fact that researchers may use subtly different definitions
of the problem. Questions such as "Which objects are moving?", "What is
background?", and "How can we use motion of the camera to segment objects,
whether they are static or moving?" are clearly related to each other, but lead
to different algorithms, and imply different versions of the ground truth. This
report has two goals. The first is to offer a precise definition of motion
segmentation so that the intent of an algorithm is as well-defined as possible.
The second is to report on new versions of three previously existing data sets
that are compatible with this definition. We hope that this more detailed
definition, and the three data sets that go with it, will allow more meaningful
comparisons of certain motion segmentation methods.
hi-RF: Incremental Learning Random Forest for large-scale multi-class Data Classification
Authors: Ting-Ting Xie, Yuxing Peng, Changjian Wang
Date: 2016-08-31 00:00:00
Tasks: Computational Efficiency, General Classification, Incremental Learning

In recent years, dynamically growing data and incrementally growing number of
classes pose new challenges to large-scale data classification research. Most
traditional methods struggle to balance the precision and computational burden
when data and its number of classes increased. However, some methods are with
weak precision, and the others are time-consuming. In this paper, we propose an
incremental learning method, namely, heterogeneous incremental Nearest Class
Mean Random Forest (hi-RF), to handle this issue. It is a heterogeneous method
that either replaces trees or updates trees leaves in the random forest
adaptively, to reduce the computational time in comparable performance, when
data of new classes arrive. Specifically, to keep the accuracy, one proportion
of trees are replaced by new NCM decision trees; to reduce the computational
load, the rest trees are updated their leaves probabilities only. Most of all,
out-of-bag estimation and out-of-bag boosting are proposed to balance the
accuracy and the computational efficiency. Fair experiments were conducted and
demonstrated its comparable precision with much less computational time.
Sequential Convex Programming Methods for A Class of Structured Nonlinear Programming
Authors: Zhaosong Lu
Date: 2012-10-10 00:00:00

In this paper we study a broad class of structured nonlinear programming (SNLP) problems. In particular, we first establish the first-order optimality conditions for them. Then we propose sequential convex programming (SCP) methods for solving them in which each iteration is obtained by solving a convex programming problem. Under some suitable assumptions, we establish that any accumulation point of the sequence generated by the methods is a KKT point of the SNLP problems. In addition, we propose a variant of the SCP method for SNLP in which nonmonotone scheme and ``local'' Lipschitz constants of the associated functions are used. A similar convergence result as mentioned above is established.
Schatten-$p$ Quasi-Norm Regularized Matrix Optimization via Iterative Reweighted Singular Value Minimization
Authors: Zhaosong Lu, Yong Zhang
Date: 2014-01-05 00:00:00

In this paper we study general Schatten-$p$ quasi-norm (SPQN) regularized
matrix minimization problems. In particular, we first introduce a class of
first-order stationary points for them, and show that the first-order
stationary points introduced in [11] for an SPQN regularized $vector$
minimization problem are equivalent to those of an SPQN regularized $matrix$
minimization reformulation. We also show that any local minimizer of the SPQN
regularized matrix minimization problems must be a first-order stationary
point. Moreover, we derive lower bounds for nonzero singular values of the
first-order stationary points and hence also of the local minimizers of the
SPQN regularized matrix minimization problems. The iterative reweighted
singular value minimization (IRSVM) methods are then proposed to solve these
problems, whose subproblems are shown to have a closed-form solution. In
contrast to the analogous methods for the SPQN regularized $vector$
minimization problems, the convergence analysis of these methods is
significantly more challenging. We develop a novel approach to establishing the
convergence of these methods, which makes use of the expression of a specific
solution of their subproblems and avoids the intricate issue of finding the
explicit expression for the Clarke subdifferential of the objective of their
subproblems. In particular, we show that any accumulation point of the sequence
generated by the IRSVM methods is a first-order stationary point of the
problems. Our computational results demonstrate that the IRSVM methods
generally outperform some recently developed state-of-the-art methods in terms
of solution quality and/or speed.
Learning Multiagent Communication with Backpropagation
Authors: Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus
Date: 2016-05-25 00:00:00
Proceeding: NeurIPS 2016 12

Many tasks in AI require the collaboration of multiple agents. Typically, the
communication protocol between agents is manually specified and not altered
during training. In this paper we explore a simple neural model, called
CommNet, that uses continuous communication for fully cooperative tasks. The
model consists of multiple agents and the communication between them is learned
alongside their policy. We apply this model to a diverse set of tasks,
demonstrating the ability of the agents to learn to communicate amongst
themselves, yielding improved performance over non-communicative agents and
baselines. In some cases, it is possible to interpret the language devised by
the agents, revealing simple but effective strategies for solving the task at
hand.
DP-EM: Differentially Private Expectation Maximization
Authors: Mijung Park, Jimmy Foulds, Kamalika Chaudhuri, Max Welling
Date: 2016-05-23 00:00:00
Tasks: Privacy Preserving

The iterative nature of the expectation maximization (EM) algorithm presents
a challenge for privacy-preserving estimation, as each iteration increases the
amount of noise needed. We propose a practical private EM algorithm that
overcomes this challenge using two innovations: (1) a novel moment perturbation
formulation for differentially private EM (DP-EM), and (2) the use of two
recently developed composition methods to bound the privacy "cost" of multiple
EM iterations: the moments accountant (MA) and zero-mean concentrated
differential privacy (zCDP). Both MA and zCDP bound the moment generating
function of the privacy loss random variable and achieve a refined tail bound,
which effectively decrease the amount of additive noise. We present empirical
results showing the benefits of our approach, as well as similar performance
between these two composition methods in the DP-EM setting for Gaussian mixture
models. Our approach can be readily extended to many iterative learning
algorithms, opening up various exciting future directions.
Joint Dimensionality Reduction for Two Feature Vectors
Authors: Yanjun Li, Yoram Bresler
Date: 2016-02-13 00:00:00
Tasks: Dimensionality Reduction, General Classification, Vocal Bursts Valence Prediction

Many machine learning problems, especially multi-modal learning problems,
have two sets of distinct features (e.g., image and text features in news story
classification, or neuroimaging data and neurocognitive data in cognitive
science research). This paper addresses the joint dimensionality reduction of
two feature vectors in supervised learning problems. In particular, we assume a
discriminative model where low-dimensional linear embeddings of the two feature
vectors are sufficient statistics for predicting a dependent variable. We show
that a simple algorithm involving singular value decomposition can accurately
estimate the embeddings provided that certain sample complexities are
satisfied, without specifying the nonlinear link function (regressor or
classifier). The main results establish sample complexities under multiple
settings. Sample complexities for different link functions only differ by
constant factors.
Generating Sentiment Lexicons for German Twitter
Authors: Uladzimir Sidarenka, Manfred Stede
Date: 2016-10-31 00:00:00
Proceeding: WS 2016 12

Despite a substantial progress made in developing new sentiment lexicon
generation (SLG) methods for English, the task of transferring these approaches
to other languages and domains in a sound way still remains open. In this
paper, we contribute to the solution of this problem by systematically
comparing semi-automatic translations of common English polarity lists with the
results of the original automatic SLG algorithms, which were applied directly
to German data. We evaluate these lexicons on a corpus of 7,992 manually
annotated tweets. In addition to that, we also collate the results of
dictionary- and corpus-based SLG methods in order to find out which of these
paradigms is better suited for the inherently noisy domain of social media. Our
experiments show that semi-automatic translations notably outperform automatic
systems (reaching a macro-averaged F1-score of 0.589), and that
dictionary-based techniques produce much better polarity lists as compared to
corpus-based approaches (whose best F1-scores run up to 0.479 and 0.419
respectively) even for the non-standard Twitter genre.
Benchmark for License Plate Character Segmentation
Authors: Gabriel Resende Gonçalves, Sirlene Pio Gomes da Silva, David Menotti, William Robson Schwartz
Date: 2016-07-11 00:00:00
Tasks: License Plate Detection, License Plate Recognition, Optical Character Recognition, Optical Character Recognition (OCR), Segmentation

Automatic License Plate Recognition (ALPR) has been the focus of many
researches in the past years. In general, ALPR is divided into the following
problems: detection of on-track vehicles, license plates detection, segmention
of license plate characters and optical character recognition (OCR). Even
though commercial solutions are available for controlled acquisition
conditions, e.g., the entrance of a parking lot, ALPR is still an open problem
when dealing with data acquired from uncontrolled environments, such as roads
and highways when relying only on imaging sensors. Due to the multiple
orientations and scales of the license plates captured by the camera, a very
challenging task of the ALPR is the License Plate Character Segmentation (LPCS)
step, which effectiveness is required to be (near) optimal to achieve a high
recognition rate by the OCR. To tackle the LPCS problem, this work proposes a
novel benchmark composed of a dataset designed to focus specifically on the
character segmentation step of the ALPR within an evaluation protocol.
Furthermore, we propose the Jaccard-Centroid coefficient, a new evaluation
measure more suitable than the Jaccard coefficient regarding the location of
the bounding box within the ground-truth annotation. The dataset is composed of
2,000 Brazilian license plates consisting of 14,000 alphanumeric symbols and
their corresponding bounding box annotations. We also present a new
straightforward approach to perform LPCS efficiently. Finally, we provide an
experimental evaluation for the dataset based on four LPCS approaches and
demonstrate the importance of character segmentation for achieving an accurate
OCR.
Seeding K-Means using Method of Moments
Authors: Sayantan Dasgupta
Date: 2015-11-18 00:00:00
Tasks: Clustering

K-means is one of the most widely used algorithms for clustering in Data
Mining applications, which attempts to minimize the sum of the square of the
Euclidean distance of the points in the clusters from the respective means of
the clusters. However, K-means suffers from local minima problem and is not
guaranteed to converge to the optimal cost. K-means++ tries to address the
problem by seeding the means using a distance-based sampling scheme. However,
seeding the means in K-means++ needs $O\left(K\right)$ sequential passes
through the entire dataset, and this can be very costly for large datasets.
Here we propose a method of seeding the initial means based on factorizations
of higher order moments for bounded data. Our method takes $O\left(1\right)$
passes through the entire dataset to extract the initial set of means, and its
final cost can be proven to be within $O(\sqrt{K})$ of the optimal cost. We
demonstrate the performance of our algorithm in comparison with the existing
algorithms on various benchmark datasets.
Sentiment Analysis of Review Datasets Using Naive Bayes and K-NN Classifier
Authors: Lopamudra Dey, Sanjay Chakraborty, Anuraag Biswas, Beepa Bose, Sweta Tiwari
Date: 2016-10-31 00:00:00
Tasks: Recommendation Systems, Sentence, Sentiment Analysis

The advent of Web 2.0 has led to an increase in the amount of sentimental
content available in the Web. Such content is often found in social media web
sites in the form of movie or product reviews, user comments, testimonials,
messages in discussion forums etc. Timely discovery of the sentimental or
opinionated web content has a number of advantages, the most important of all
being monetization. Understanding of the sentiments of human masses towards
different entities and products enables better services for contextual
advertisements, recommendation systems and analysis of market trends. The focus
of our project is sentiment focussed web crawling framework to facilitate the
quick discovery of sentimental contents of movie reviews and hotel reviews and
analysis of the same. We use statistical methods to capture elements of
subjective style and the sentence polarity. The paper elaborately discusses two
supervised machine learning algorithms: K-Nearest Neighbour(K-NN) and Naive
Bayes and compares their overall accuracy, precisions as well as recall values.
It was seen that in case of movie reviews Naive Bayes gave far better results
than K-NN but for hotel reviews these algorithms gave lesser, almost same
accuracies.
Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition
Authors: Hagen Soltau, Hank Liao, Hasim Sak
Date: 2016-10-31 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

We present results that show it is possible to build a competitive, greatly
simplified, large vocabulary continuous speech recognition system with whole
words as acoustic units. We model the output vocabulary of about 100,000 words
directly using deep bi-directional LSTM RNNs with CTC loss. The model is
trained on 125,000 hours of semi-supervised acoustic training data, which
enables us to alleviate the data sparsity problem for word models. We show that
the CTC word models work very well as an end-to-end all-neural speech
recognition model without the use of traditional context-dependent sub-word
phone units that require a pronunciation lexicon, and without any language
model removing the need to decode. We demonstrate that the CTC word models
perform better than a strong, more complex, state-of-the-art baseline with
sub-word units.
Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals
Authors: Alexandre Coninx, Pierre Bessière, Jacques Droulez
Date: 2016-09-14 00:00:00
Tasks: Bayesian Inference

Reconstruction of the tridimensional geometry of a visual scene using the
binocular disparity information is an important issue in computer vision and
mobile robotics, which can be formulated as a Bayesian inference problem.
However, computation of the full disparity distribution with an advanced
Bayesian model is usually an intractable problem, and proves computationally
challenging even with a simple model. In this paper, we show how probabilistic
hardware using distributed memory and alternate representation of data as
stochastic bitstreams can solve that problem with high performance and energy
efficiency. We put forward a way to express discrete probability distributions
using stochastic data representations and perform Bayesian fusion using those
representations, and show how that approach can be applied to diparity
computation. We evaluate the system using a simulated stochastic implementation
and discuss possible hardware implementations of such architectures and their
potential for sensorimotor processing and robotics.
Ontology Verbalization using Semantic-Refinement
Authors: Vinu E. V, P. Sreenivasa Kumar
Date: 2016-10-31 00:00:00

We propose a rule-based technique to generate redundancy-free NL descriptions
of OWL entities.The existing approaches which address the problem of
verbalizing OWL ontologies generate NL text segments which are close to their
counterpart OWL statements.Some of these approaches also perform grouping and
aggregating of these NL text segments to generate a more fluent and
comprehensive form of the content.Restricting our attention to description of
individuals and concepts, we find that the approach currently followed in the
available tools is that of determining the set of all logical conditions that
are satisfied by the given individual/concept name and translate these
conditions verbatim into corresponding NL descriptions.Human-understandability
of such descriptions is affected by the presence of repetitions and
redundancies, as they have high fidelity to their OWL representation.In the
literature, no efforts had been taken to remove redundancies and repetitions at
the logical-level before generating the NL descriptions of entities and we find
this to be the main reason for lack of readability of the generated
text.Herein, we propose a technique called semantic-refinement(SR) to generate
meaningful and easily-understandable descriptions of individuals and concepts
of a given OWLontology.We identify the combinations of OWL/DL constructs that
lead to repetitive/redundant descriptions and propose a series of refinement
rules to rewrite the conditions that are satisfied by an individual/concept in
a meaning-preserving manner.The reduced set of conditions are then employed for
generating NL descriptions.Our experiments show that, SR leads to significantly
improved descriptions of ontology entities.We also test the effectiveness and
usefulness of the the generated descriptions for the purpose of validating the
ontologies and find that the proposed technique is indeed helpful in the
context.
Complex-Valued Kernel Methods for Regression
Authors: Rafael Boloix-Tortosa, Juan José Murillo-Fuentes, Irene Santos Velázquez, Fernando Pérez-Cruz
Date: 2016-10-31 00:00:00
Tasks: regression

Usually, complex-valued RKHS are presented as an straightforward application
of the real-valued case. In this paper we prove that this procedure yields a
limited solution for regression. We show that another kernel, here denoted as
pseudo kernel, is needed to learn any function in complex-valued fields.
Accordingly, we derive a novel RKHS to include it, the widely RKHS (WRKHS).
When the pseudo-kernel cancels, WRKHS reduces to complex-valued RKHS of
previous approaches. We address the kernel and pseudo-kernel design, paying
attention to the kernel and the pseudo-kernel being complex-valued. In the
experiments included we report remarkable improvements in simple scenarios
where real a imaginary parts have different similitude relations for given
inputs or cases where real and imaginary parts are correlated. In the context
of these novel results we revisit the problem of non-linear channel
equalization, to show that the WRKHS helps to design more efficient solutions.
Named Entity Recognition for Novel Types by Transfer Learning
Authors: Lizhen Qu, Gabriela Ferraro, Liyuan Zhou, Weiwei Hou, Timothy Baldwin
Date: 2016-10-31 00:00:00
Proceeding: EMNLP 2016 11
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Transfer Learning

In named entity recognition, we often don't have a large in-domain training
corpus or a knowledge base with adequate coverage to train a model directly. In
this paper, we propose a method where, given training data in a related domain
with similar (but not identical) named entity (NE) types and a small amount of
in-domain training data, we use transfer learning to learn a domain-specific NE
model. That is, the novelty in the task setup is that we assume not just domain
mismatch, but also label mismatch.
Joint Large-Scale Motion Estimation and Image Reconstruction
Authors: Hendrik Dirks
Date: 2016-10-31 00:00:00
Tasks: Image Reconstruction, Motion Estimation, Optical Flow Estimation

This article describes the implementation of the joint motion estimation and
image reconstruction framework presented by Burger, Dirks and Sch\"onlieb and
extends this framework to large-scale motion between consecutive image frames.
The variational framework uses displacements between consecutive frames based
on the optical flow approach to improve the image reconstruction quality on the
one hand and the motion estimation quality on the other. The energy functional
consists of a data-fidelity term with a general operator that connects the
input sequence to the solution, it has a total variation term for the image
sequence and is connected to the underlying flow using an optical flow term.
Additional spatial regularity for the flow is modeled by a total variation
regularizer for both components of the flow. The numerical minimization is
performed in an alternating manner using primal-dual techniques. The resulting
schemes are presented as pseudo-code together with a short numerical
evaluation.
Learning Runtime Parameters in Computer Systems with Delayed Experience Injection
Authors: Michael Schaarschmidt, Felix Gessert, Valentin Dalibard, Eiko Yoneki
Date: 2016-10-31 00:00:00
Tasks: CPU, Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning effective configurations in computer systems without hand-crafting
models for every parameter is a long-standing problem. This paper investigates
the use of deep reinforcement learning for runtime parameters of cloud
databases under latency constraints. Cloud services serve up to thousands of
concurrent requests per second and can adjust critical parameters by leveraging
performance metrics. In this work, we use continuous deep reinforcement
learning to learn optimal cache expirations for HTTP caching in content
delivery networks. To this end, we introduce a technique for asynchronous
experience management called delayed experience injection, which facilitates
delayed reward and next-state computation in concurrent environments where
measurements are not immediately available. Evaluation results show that our
approach based on normalized advantage functions and asynchronous CPU-only
training outperforms a statistical estimator.
Mining Social Media for Open Innovation in Transportation Systems
Authors: Daniela Ulloa, Pedro Saleiro, Rosaldo J. F. Rossetti, Elis Regina Silva
Date: 2016-10-31 00:00:00
Tasks: Sentiment Analysis

This work proposes a novel framework for the development of new products and
services in transportation through an open innovation approach based on
automatic content analysis of social media data. The framework is able to
extract users comments from Online Social Networks (OSN), to process and
analyze text through information extraction and sentiment analysis techniques
to obtain relevant information about product reception on the market. A use
case was developed using the mobile application Uber, which is today one of the
fastest growing technology companies in the world. We measured how a
controversial, highly diffused event influences the volume of tweets about Uber
and the perception of its users. While there is no change in the image of Uber,
a large increase in the number of tweets mentioning the company is observed,
which meant a free and important diffusion of its product.