pwc-archive/papers-with-abstracts huggingface dataset:
Deeply Aggregated Alternating Minimization for Image Restoration
Authors: Youngjung Kim, Hyungjoo Jung, Dongbo Min, Kwanghoon Sohn
Date: 2016-12-20 00:00:00
Proceeding: CVPR 2017 7
Tasks: Denoising, Image Denoising, Image Restoration, Super-Resolution

Regularization-based image restoration has remained an active research topic
in computer vision and image processing. It often leverages a guidance signal
captured in different fields as an additional cue. In this work, we present a
general framework for image restoration, called deeply aggregated alternating
minimization (DeepAM). We propose to train deep neural network to advance two
of the steps in the conventional AM algorithm: proximal mapping and ?-
continuation. Both steps are learned from a large dataset in an end-to-end
manner. The proposed framework enables the convolutional neural networks (CNNs)
to operate as a prior or regularizer in the AM algorithm. We show that our
learned regularizer via deep aggregation outperforms the recent data-driven
approaches as well as the nonlocalbased methods. The flexibility and
effectiveness of our framework are demonstrated in several image restoration
tasks, including single image denoising, RGB-NIR restoration, and depth
super-resolution.
Efficiently Computing Piecewise Flat Embeddings for Data Clustering and Image Segmentation
Authors: Renee T. Meinhold, Tyler L. Hayes, Nathan D. Cahill
Date: 2016-12-20 00:00:00
Tasks: Clustering, Image Segmentation, Segmentation, Semantic Segmentation

Image segmentation is a popular area of research in computer vision that has
many applications in automated image processing. A recent technique called
piecewise flat embeddings (PFE) has been proposed for use in image
segmentation; PFE transforms image pixel data into a lower dimensional
representation where similar pixels are pulled close together and dissimilar
pixels are pushed apart. This technique has shown promising results, but its
original formulation is not computationally feasible for large images. We
propose two improvements to the algorithm for computing PFE: first, we
reformulate portions of the algorithm to enable various linear algebra
operations to be performed in parallel; second, we propose utilizing an
iterative linear solver (preconditioned conjugate gradient) to quickly solve a
linear least-squares problem that occurs in the inner loop of a nested
iteration. With these two computational improvements, we show on a publicly
available image database that PFE can be sped up by an order of magnitude
without sacrificing segmentation performance. Our results make this technique
more practical for use on large data sets, not only for image segmentation, but
for general data clustering problems.
Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles
Authors: James Cross, Liang Huang
Date: 2016-12-20 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Constituency Parsing, Dependency Parsing, Reranking, Sentence

Parsing accuracy using efficient greedy transition systems has improved
dramatically in recent years thanks to neural networks. Despite striking
results in dependency parsing, however, neural models have not surpassed
state-of-the-art approaches in constituency parsing. To remedy this, we
introduce a new shift-reduce system whose stack contains merely sentence spans,
represented by a bare minimum of LSTM features. We also design the first
provably optimal dynamic oracle for constituency parsing, which runs in
amortized O(1) time, compared to O(n^3) oracles for standard dependency
parsing. Training with this oracle, we achieve the best F1 scores on both
English and French of any parser that does not use reranking or external data.
Randomized Clustered Nystrom for Large-Scale Kernel Machines
Authors: Farhad Pourkamali-Anaraki, Stephen Becker
Date: 2016-12-20 00:00:00
Tasks: Clustering

The Nystrom method has been popular for generating the low-rank approximation
of kernel matrices that arise in many machine learning problems. The
approximation quality of the Nystrom method depends crucially on the number of
selected landmark points and the selection procedure. In this paper, we present
a novel algorithm to compute the optimal Nystrom low-approximation when the
number of landmark points exceed the target rank. Moreover, we introduce a
randomized algorithm for generating landmark points that is scalable to
large-scale data sets. The proposed method performs K-means clustering on
low-dimensional random projections of a data set and, thus, leads to
significant savings for high-dimensional data sets. Our theoretical results
characterize the tradeoffs between the accuracy and efficiency of our proposed
method. Extensive experiments demonstrate the competitive performance as well
as the efficiency of our proposed method.
Exploring Structure for Long-Term Tracking of Multiple Objects in Sports Videos
Authors: Henrique Morimitsu, Isabelle Bloch, Roberto M. Cesar-Jr
Date: 2016-12-19 00:00:00

In this paper, we propose a novel approach for exploiting structural
relations to track multiple objects that may undergo long-term occlusion and
abrupt motion. We use a model-free approach that relies only on annotations
given in the first frame of the video to track all the objects online, i.e.
without knowledge from future frames. We initialize a probabilistic Attributed
Relational Graph (ARG) from the first frame, which is incrementally updated
along the video. Instead of using the structural information only to evaluate
the scene, the proposed approach considers it to generate new tracking
hypotheses. In this way, our method is capable of generating relevant object
candidates that are used to improve or recover the track of lost objects. The
proposed method is evaluated on several videos of table tennis, volleyball, and
on the ACASVA dataset. The results show that our approach is very robust,
flexible and able to outperform other state-of-the-art methods in sports videos
that present structural patterns.
Hardness of Online Sleeping Combinatorial Optimization Problems
Authors: Satyen Kale, Chansoo Lee, Dávid Pál
Date: 2015-09-11 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Combinatorial Optimization, PAC learning

We show that several online combinatorial optimization problems that admit
efficient no-regret algorithms become computationally hard in the sleeping
setting where a subset of actions becomes unavailable in each round.
Specifically, we show that the sleeping versions of these problems are at least
as hard as PAC learning DNF expressions, a long standing open problem. We show
hardness for the sleeping versions of Online Shortest Paths, Online Minimum
Spanning Tree, Online $k$-Subsets, Online $k$-Truncated Permutations, Online
Minimum Cut, and Online Bipartite Matching. The hardness result for the
sleeping version of the Online Shortest Paths problem resolves an open problem
presented at COLT 2015 (Koolen et al., 2015).
Binary Distance Transform to Improve Feature Extraction
Authors: Mariane Barros Neiva, Antoine Manzanera, Odemir Martinez Bruno
Date: 2016-12-19 00:00:00
Tasks: Binarization

To recognize textures many methods have been developed along the years.
However, texture datasets may be hard to be classified due to artefacts such as
a variety of scale, illumination and noise. This paper proposes the application
of binary distance transform on the original dataset to add information to
texture representation and consequently improve recognition. Texture images,
usually in grayscale, suffers a binarization prior to distance transform and
one of the resulted images are combined with original texture to improve the
amount of information. Four datasets are used to evaluate our approach. For
Outex dataset, for instance, the proposal outperforms all rates, improvements
of an up to 10\%, compared to traditional approach where descriptors are
applied on the original dataset, showing the importance of this approach.
Fractal Descriptors of Texture Images Based on the Triangular Prism Dimension
Authors: João B. Florindo, Odemir M. Bruno
Date: 2016-12-19 00:00:00
Tasks: Dimensionality Reduction, General Classification, Image Retrieval, Retrieval

This work presents a novel descriptor for texture images based on fractal
geometry and its application to image analysis. The descriptors are provided by
estimating the triangular prism fractal dimension under different scales with a
weight exponential parameter, followed by dimensionality reduction using
Karhunen-Lo\`{e}ve transform. The efficiency of the proposed descriptors is
tested on two well-known texture data sets, that is, Brodatz and Vistex, both
for classification and image retrieval. The novel method is also tested
concerning invariances in situations when the textures are rotated or affected
by Gaussian noise. The obtained results outperform other classical and
state-of-the-art descriptors in the literature and demonstrate the power of the
triangular descriptors in these tasks, suggesting their use in practical
applications of image analysis based on texture features.
Random Walk Models of Network Formation and Sequential Monte Carlo Methods for Graphs
Authors: Benjamin Bloem-Reddy, Peter Orbanz
Date: 2016-12-19 00:00:00

We introduce a class of generative network models that insert edges by
connecting the starting and terminal vertices of a random walk on the network
graph. Within the taxonomy of statistical network models, this class is
distinguished by permitting the location of a new edge to explicitly depend on
the structure of the graph, but being nonetheless statistically and
computationally tractable. In the limit of infinite walk length, the model
converges to an extension of the preferential attachment model---in this sense,
it can be motivated alternatively by asking what preferential attachment is an
approximation to. Theoretical properties, including the limiting degree
sequence, are studied analytically. If the entire history of the graph is
observed, parameters can be estimated by maximum likelihood. If only the final
graph is available, its history can be imputed using MCMC. We develop a class
of sequential Monte Carlo algorithms that are more generally applicable to
sequential network models, and may be of interest in their own right. The model
parameters can be recovered from a single graph generated by the model.
Applications to data clarify the role of the random walk length as a length
scale of interactions within the graph.
Talk it up or play it down? (Un)expected correlations between (de-)emphasis and recurrence of discussion points in consequential U.S. economic policy meetings
Authors: Chenhao Tan, Lillian Lee
Date: 2016-12-19 00:00:00

In meetings where important decisions get made, what items receive more
attention may influence the outcome. We examine how different types of
rhetorical (de-)emphasis -- including hedges, superlatives, and contrastive
conjunctions -- correlate with what gets revisited later, controlling for item
frequency and speaker. Our data consists of transcripts of recurring meetings
of the Federal Reserve's Open Market Committee (FOMC), where important aspects
of U.S. monetary policy are decided on. Surprisingly, we find that words
appearing in the context of hedging, which is usually considered a way to
express uncertainty, are more likely to be repeated in subsequent meetings,
while strong emphasis indicated by superlatives has a slightly negative effect
on word recurrence in subsequent meetings. We also observe interesting patterns
in how these effects vary depending on social factors such as status and gender
of the speaker. For instance, the positive effects of hedging are more
pronounced for female speakers than for male speakers.
Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation
Authors: Antonio Jimeno Yepes
Date: 2016-04-09 00:00:00
Tasks: Word Embeddings, Word Sense Disambiguation

Word sense disambiguation helps identifying the proper sense of ambiguous
words in text. With large terminologies such as the UMLS Metathesaurus
ambiguities appear and highly effective disambiguation methods are required.
Supervised learning algorithm methods are used as one of the approaches to
perform disambiguation. Features extracted from the context of an ambiguous
word are used to identify the proper sense of such a word. The type of features
have an impact on machine learning methods, thus affect disambiguation
performance. In this work, we have evaluated several types of features derived
from the context of the ambiguous word and we have explored as well more global
features derived from MEDLINE using word embeddings. Results show that word
embeddings improve the performance of more traditional features and allow as
well using recurrent neural network classifiers based on Long-Short Term Memory
(LSTM) nodes. The combination of unigrams and word embeddings with an SVM sets
a new state of the art performance with a macro accuracy of 95.97 in the MSH
WSD data set.
Handwritten Signature Verification Using Hand-Worn Devices
Authors: Ben Nassi, Alona Levy, Yuval Elovici, Erez Shmueli
Date: 2016-12-19 00:00:00

Online signature verification technologies, such as those available in banks
and post offices, rely on dedicated digital devices such as tablets or smart
pens to capture, analyze and verify signatures. In this paper, we suggest a
novel method for online signature verification that relies on the increasingly
available hand-worn devices, such as smartwatches or fitness trackers, instead
of dedicated ad-hoc devices. Our method uses a set of known genuine and forged
signatures, recorded using the motion sensors of a hand-worn device, to train a
machine learning classifier. Then, given the recording of an unknown signature
and a claimed identity, the classifier can determine whether the signature is
genuine or forged. In order to validate our method, it was applied on 1980
recordings of genuine and forged signatures that we collected from 66 subjects
in our institution. Using our method, we were able to successfully distinguish
between genuine and forged signatures with a high degree of accuracy (0.98 AUC
and 0.05 EER).
Simple Black-Box Adversarial Perturbations for Deep Networks
Authors: Nina Narodytska, Shiva Prasad Kasiviswanathan
Date: 2016-12-19 00:00:00

Deep neural networks are powerful and popular learning models that achieve
state-of-the-art pattern recognition performance on many computer vision,
speech, and language processing tasks. However, these networks have also been
shown susceptible to carefully crafted adversarial perturbations which force
misclassification of the inputs. Adversarial examples enable adversaries to
subvert the expected system behavior leading to undesired consequences and
could pose a security risk when these systems are deployed in the real world.
  In this work, we focus on deep convolutional neural networks and demonstrate
that adversaries can easily craft adversarial examples even without any
internal knowledge of the target network. Our attacks treat the network as an
oracle (black-box) and only assume that the output of the network can be
observed on the probed inputs. Our first attack is based on a simple idea of
adding perturbation to a randomly selected single pixel or a small set of them.
We then improve the effectiveness of this attack by carefully constructing a
small set of pixels to perturb by using the idea of greedy local-search. Our
proposed attacks also naturally extend to a stronger notion of
misclassification. Our extensive experimental results illustrate that even
these elementary attacks can reveal a deep neural network's vulnerabilities.
The simplicity and effectiveness of our proposed schemes mean that they could
serve as a litmus test for designing robust networks.
Fuzzy Longest Common Subsequence Matching With FCM Using R
Authors: Ibrahim Ozkan, I. Burhan Turksen
Date: 2015-08-14 00:00:00
Tasks: Time Series, Time Series Analysis

Capturing the interdependencies between real valued time series can be
achieved by finding common similar patterns. The abstraction of time series
makes the process of finding similarities closer to the way as humans do.
Therefore, the abstraction by means of a symbolic levels and finding the common
patterns attracts researchers. One particular algorithm, Longest Common
Subsequence, has been used successfully as a similarity measure between two
sequences including real valued time series. In this paper, we propose Fuzzy
Longest Common Subsequence matching for time series.
Disentangling Space and Time in Video with Hierarchical Variational Auto-encoders
Authors: Will Grathwohl, Aaron Wilson
Date: 2016-12-14 00:00:00
Tasks: Object, Transfer Learning, Variational Inference

There are many forms of feature information present in video data. Principle
among them are object identity information which is largely static across
multiple video frames, and object pose and style information which continuously
transforms from frame to frame. Most existing models confound these two types
of representation by mapping them to a shared feature space. In this paper we
propose a probabilistic approach for learning separable representations of
object identity and pose information using unsupervised video data. Our
approach leverages a deep generative model with a factored prior distribution
that encodes properties of temporal invariances in the hidden feature set.
Learning is achieved via variational inference. We present results of learning
identity and pose information on a dataset of moving characters as well as a
dataset of rotating 3D objects. Our experimental results demonstrate our
model's success in factoring its representation, and demonstrate that the model
achieves improved performance in transfer learning tasks.
Satisficing in multi-armed bandit problems
Authors: Paul Reverdy, Vaibhav Srivastava, Naomi Ehrich Leonard
Date: 2015-12-23 00:00:00
Tasks: Decision Making

Satisficing is a relaxation of maximizing and allows for less risky decision
making in the face of uncertainty. We propose two sets of satisficing
objectives for the multi-armed bandit problem, where the objective is to
achieve reward-based decision-making performance above a given threshold. We
show that these new problems are equivalent to various standard multi-armed
bandit problems with maximizing objectives and use the equivalence to find
bounds on performance. The different objectives can result in qualitatively
different behavior; for example, agents explore their options continually in
one case and only a finite number of times in another. For the case of Gaussian
rewards we show an additional equivalence between the two sets of satisficing
objectives that allows algorithms developed for one set to be applied to the
other. We then develop variants of the Upper Credible Limit (UCL) algorithm
that solve the problems with satisficing objectives and show that these
modified UCL algorithms achieve efficient satisficing performance.
Photo-Quality Evaluation based on Computational Aesthetics: Review of Feature Extraction Techniques
Authors: Dimitris Spathis
Date: 2016-12-19 00:00:00
Tasks: BIG-bench Machine Learning, Marketing

Researchers try to model the aesthetic quality of photographs into low and
high- level features, drawing inspiration from art theory, psychology and
marketing. We attempt to describe every feature extraction measure employed in
the above process. The contribution of this literature review is the taxonomy
of each feature by its implementation complexity, considering real-world
applications and integration in mobile apps and digital cameras. Also, we
discuss the machine learning results along with some unexplored research areas
as future work.
A framework for redescription set construction
Authors: Matej Mihelčić, Sašo Džeroski, Nada Lavrač, Tomislav Šmuc
Date: 2016-06-13 00:00:00
Tasks: Missing Values

Redescription mining is a field of knowledge discovery that aims at finding
different descriptions of similar subsets of instances in the data. These
descriptions are represented as rules inferred from one or more disjoint sets
of attributes, called views. As such, they support knowledge discovery process
and help domain experts in formulating new hypotheses or constructing new
knowledge bases and decision support systems. In contrast to previous
approaches that typically create one smaller set of redescriptions satisfying a
pre-defined set of constraints, we introduce a framework that creates large and
heterogeneous redescription set from which user/expert can extract compact sets
of differing properties, according to its own preferences. Construction of
large and heterogeneous redescription set relies on CLUS-RM algorithm and a
novel, conjunctive refinement procedure that facilitates generation of larger
and more accurate redescription sets. The work also introduces the variability
of redescription accuracy when missing values are present in the data, which
significantly extends applicability of the method. Crucial part of the
framework is the redescription set extraction based on heuristic
multi-objective optimization procedure that allows user to define importance
levels towards one or more redescription quality criteria. We provide both
theoretical and empirical comparison of the novel framework against current
state of the art redescription mining algorithms and show that it represents
more efficient and versatile approach for mining redescriptions from data.
Leveraging Video Descriptions to Learn Video Question Answering
Authors: Kuo-Hao Zeng, Tseng-Hung Chen, Ching-Yao Chuang, Yuan-Hong Liao, Juan Carlos Niebles, Min Sun
Date: 2016-11-12 00:00:00
Tasks: Question Answering, Video Question Answering, Visual Question Answering (VQA)

We propose a scalable approach to learn video-based question answering (QA):
answer a "free-form natural language question" about a video content. Our
approach automatically harvests a large number of videos and descriptions
freely available online. Then, a large number of candidate QA pairs are
automatically generated from descriptions rather than manually annotated. Next,
we use these candidate QA pairs to train a number of video-based QA methods
extended fromMN (Sukhbaatar et al. 2015), VQA (Antol et al. 2015), SA (Yao et
al. 2015), SS (Venugopalan et al. 2015). In order to handle non-perfect
candidate QA pairs, we propose a self-paced learning procedure to iteratively
identify them and mitigate their effects in training. Finally, we evaluate
performance on manually generated video-based QA pairs. The results show that
our self-paced learning procedure is effective, and the extended SS model
outperforms various baselines.
Distributed Optimization for Client-Server Architecture with Negative Gradient Weights
Authors: Shripad Gade, Nitin H. Vaidya
Date: 2016-08-12 00:00:00
Tasks: BIG-bench Machine Learning, Distributed Optimization

Availability of both massive datasets and computing resources have made
machine learning and predictive analytics extremely pervasive. In this work we
present a synchronous algorithm and architecture for distributed optimization
motivated by privacy requirements posed by applications in machine learning. We
present an algorithm for the recently proposed multi-parameter-server
architecture. We consider a group of parameter servers that learn a model based
on randomized gradients received from clients. Clients are computational
entities with private datasets (inducing a private objective function), that
evaluate and upload randomized gradients to the parameter servers. The
parameter servers perform model updates based on received gradients and share
the model parameters with other servers. We prove that the proposed algorithm
can optimize the overall objective function for a very general architecture
involving $C$ clients connected to $S$ parameter servers in an arbitrary time
varying topology and the parameter servers forming a connected network.