pwc-archive/papers-with-abstracts huggingface dataset:
Efficient PAC Learning from the Crowd
Authors: Pranjal Awasthi, Avrim Blum, Nika Haghtalab, Yishay Mansour
Date: 2017-03-21 00:00:00
Tasks: Computational Efficiency, PAC learning

In recent years crowdsourcing has become the method of choice for gathering
labeled training data for learning algorithms. Standard approaches to
crowdsourcing view the process of acquiring labeled data separately from the
process of learning a classifier from the gathered data. This can give rise to
computational and statistical challenges. For example, in most cases there are
no known computationally efficient learning algorithms that are robust to the
high level of noise that exists in crowdsourced data, and efforts to eliminate
noise through voting often require a large number of queries per example.
  In this paper, we show how by interleaving the process of labeling and
learning, we can attain computational efficiency with much less overhead in the
labeling cost. In particular, we consider the realizable setting where there
exists a true target function in $\mathcal{F}$ and consider a pool of labelers.
When a noticeable fraction of the labelers are perfect, and the rest behave
arbitrarily, we show that any $\mathcal{F}$ that can be efficiently learned in
the traditional realizable PAC model can be learned in a computationally
efficient manner by querying the crowd, despite high amounts of noise in the
responses. Moreover, we show that this can be done while each labeler only
labels a constant number of examples and the number of labels requested per
example, on average, is a constant. When no perfect labelers exist, a related
task is to find a set of the labelers which are good but not perfect. We show
that we can identify all good labelers, when at least the majority of labelers
are good.
Identity and Granularity of Events in Text
Authors: Piek Vossen, Agata Cybulska
Date: 2017-04-13 00:00:00
Tasks: Articles, Event Detection

In this paper we describe a method to detect event descrip- tions in
different news articles and to model the semantics of events and their
components using RDF representations. We compare these descriptions to solve a
cross-document event coreference task. Our com- ponent approach to event
semantics defines identity and granularity of events at different levels. It
performs close to state-of-the-art approaches on the cross-document event
coreference task, while outperforming other works when assuming similar quality
of event detection. We demonstrate how granularity and identity are
interconnected and we discuss how se- mantic anomaly could be used to define
differences between coreference, subevent and topical relations.
Visual Recognition of Paper Analytical Device Images for Detection of Falsified Pharmaceuticals
Authors: Sandipan Banerjee, James Sweet, Christopher Sweet, Marya Lieberman
Date: 2017-04-13 00:00:00

Falsification of medicines is a big problem in many developing countries,
where technological infrastructure is inadequate to detect these harmful
products. We have developed a set of inexpensive paper cards, called Paper
Analytical Devices (PADs), which can efficiently classify drugs based on their
chemical composition, as a potential solution to the problem. These cards have
different reagents embedded in them which produce a set of distinctive color
descriptors upon reacting with the chemical compounds that constitute
pharmaceutical dosage forms. If a falsified version of the medicine lacks the
active ingredient or includes substitute fillers, the difference in color is
perceivable by humans. However, reading the cards with accuracy takes training
and practice, which may hamper their scaling and implementation in low resource
settings. To deal with this, we have developed an automatic visual recognition
system to read the results from the PAD images. At first, the optimal set of
reagents was found by running singular value decomposition on the intensity
values of the color tones in the card images. A dataset of cards embedded with
these reagents is produced to generate the most distinctive results for a set
of 26 different active pharmaceutical ingredients (APIs) and excipients. Then,
we train two popular convolutional neural network (CNN) models, with the card
images. We also extract some "hand-crafted" features from the images and train
a nearest neighbor classifier and a non-linear support vector machine with
them. On testing, higher-level features performed much better in accurately
classifying the PAD images, with the CNN models reaching the highest average
accuracy of over 94\%.
Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems
Authors: Layla El Asri, Hannes Schulz, Shikhar Sharma, Jeremie Zumer, Justin Harris, Emery Fine, Rahul Mehrotra, Kaheer Suleman
Date: 2017-03-31 00:00:00
Proceeding: WS 2017 8
Tasks: Dialogue Management, Goal-Oriented Dialogue Systems, Management, Text Generation

This paper presents the Frames dataset (Frames is available at
http://datasets.maluuba.com/Frames), a corpus of 1369 human-human dialogues
with an average of 15 turns per dialogue. We developed this dataset to study
the role of memory in goal-oriented dialogue systems. Based on Frames, we
introduce a task called frame tracking, which extends state tracking to a
setting where several states are tracked simultaneously. We propose a baseline
model for this task. We show that Frames can also be used to study memory in
dialogue management and information presentation through natural language
generation.
Spatial Memory for Context Reasoning in Object Detection
Authors: Xinlei Chen, Abhinav Gupta
Date: 2017-04-13 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, Object Detection, Spatial Reasoning

Modeling instance-level context and object-object relationships is extremely
challenging. It requires reasoning about bounding boxes of different classes,
locations \etc. Above all, instance-level spatial reasoning inherently requires
modeling conditional distributions on previous detections. Unfortunately, our
current object detection systems do not have any {\bf memory} to remember what
to condition on! The state-of-the-art object detectors still detect all object
in parallel followed by non-maximal suppression (NMS). While memory has been
used for tasks such as captioning, they mostly use image-level memory cells
without capturing the spatial layout. On the other hand, modeling object-object
relationships requires {\bf spatial} reasoning -- not only do we need a memory
to store the spatial layout, but also a effective reasoning module to extract
spatial patterns. This paper presents a conceptually simple yet powerful
solution -- Spatial Memory Network (SMN), to model the instance-level context
efficiently and effectively. Our spatial memory essentially assembles object
instances back into a pseudo "image" representation that is easy to be fed into
another ConvNet for object-object context reasoning. This leads to a new
sequential reasoning architecture where image and memory are processed in
parallel to obtain detections which update the memory again. We show our SMN
direction is promising as it provides 2.2\% improvement over baseline Faster
RCNN on the COCO dataset so far.
Learning the Dimensionality of Word Embeddings
Authors: Eric Nalisnick, Sachin Ravi
Date: 2015-11-17 00:00:00
Tasks: Learning Word Embeddings, Word Embeddings

We describe a method for learning word embeddings with data-dependent
dimensionality. Our Stochastic Dimensionality Skip-Gram (SD-SG) and Stochastic
Dimensionality Continuous Bag-of-Words (SD-CBOW) are nonparametric analogs of
Mikolov et al.'s (2013) well-known 'word2vec' models. Vector dimensionality is
made dynamic by employing techniques used by Cote & Larochelle (2016) to define
an RBM with an infinite number of hidden units. We show qualitatively and
quantitatively that SD-SG and SD-CBOW are competitive with their
fixed-dimension counterparts while providing a distribution over embedding
dimensionalities, which offers a window into how semantics distribute across
dimensions.
Detecting Oriented Text in Natural Images by Linking Segments
Authors: Baoguang Shi, Xiang Bai, Serge Belongie
Date: 2017-03-19 00:00:00
Proceeding: CVPR 2017 7
Tasks: Curved Text Detection, Scene Text Detection, Text Detection

Most state-of-the-art text detection methods are specific to horizontal Latin
text and are not fast enough for real-time applications. We introduce Segment
Linking (SegLink), an oriented text detection method. The main idea is to
decompose text into two locally detectable elements, namely segments and links.
A segment is an oriented box covering a part of a word or text line; A link
connects two adjacent segments, indicating that they belong to the same word or
text line. Both elements are detected densely at multiple scales by an
end-to-end trained, fully-convolutional neural network. Final detections are
produced by combining segments connected by links. Compared with previous
methods, SegLink improves along the dimensions of accuracy, speed, and ease of
training. It achieves an f-measure of 75.0% on the standard ICDAR 2015
Incidental (Challenge 4) benchmark, outperforming the previous best by a large
margin. It runs at over 20 FPS on 512x512 images. Moreover, without
modification, SegLink is able to detect long lines of non-Latin text, such as
Chinese.
Learning Time Series Detection Models from Temporally Imprecise Labels
Authors: Roy J. Adams, Benjamin M. Marlin
Date: 2016-11-07 00:00:00
Tasks: Multiple Instance Learning, Time Series, Time Series Analysis

In this paper, we consider a new low-quality label learning problem: learning
time series detection models from temporally imprecise labels. In this problem,
the data consist of a set of input time series, and supervision is provided by
a sequence of noisy time stamps corresponding to the occurrence of positive
class events. Such temporally imprecise labels commonly occur in areas like
mobile health research where human annotators are tasked with labeling the
occurrence of very short duration events. We propose a general learning
framework for this problem that can accommodate different base classifiers and
noise models. We present results on real mobile health data showing that the
proposed framework significantly outperforms a number of alternatives including
assuming that the label time stamps are noise-free, transforming the problem
into the multiple instance learning framework, and learning on labels that were
manually re-aligned.
Room for improvement in automatic image description: an error analysis
Authors: Emiel van Miltenburg, Desmond Elliott
Date: 2017-04-13 00:00:00
Tasks: Image Description

In recent years we have seen rapid and significant progress in automatic
image description but what are the open problems in this area? Most work has
been evaluated using text-based similarity metrics, which only indicate that
there have been improvements, without explaining what has improved. In this
paper, we present a detailed error analysis of the descriptions generated by a
state-of-the-art attention-based model. Our analysis operates on two levels:
first we check the descriptions for accuracy, and then we categorize the types
of errors we observe in the inaccurate descriptions. We find only 20% of the
descriptions are free from errors, and surprisingly that 26% are unrelated to
the image. Finally, we manually correct the most frequently occurring error
types (e.g. gender identification) to estimate the performance reward for
addressing these errors, observing gains of 0.2--1 BLEU point per type.
On Sequential Elimination Algorithms for Best-Arm Identification in Multi-Armed Bandits
Authors: Shahin Shahrampour, Mohammad Noshad, Vahid Tarokh
Date: 2016-09-08 00:00:00
Tasks: Multi-Armed Bandits

We consider the best-arm identification problem in multi-armed bandits, which
focuses purely on exploration. A player is given a fixed budget to explore a
finite set of arms, and the rewards of each arm are drawn independently from a
fixed, unknown distribution. The player aims to identify the arm with the
largest expected reward. We propose a general framework to unify sequential
elimination algorithms, where the arms are dismissed iteratively until a unique
arm is left. Our analysis reveals a novel performance measure expressed in
terms of the sampling mechanism and number of eliminated arms at each round.
Based on this result, we develop an algorithm that divides the budget according
to a nonlinear function of remaining arms at each round. We provide theoretical
guarantees for the algorithm, characterizing the suitable nonlinearity for
different problem environments described by the number of competitive arms.
Matching the theoretical results, our experiments show that the nonlinear
algorithm outperforms the state-of-the-art. We finally study the
side-observation model, where pulling an arm reveals the rewards of its related
arms, and we establish improved theoretical guarantees in the pure-exploration
setting.
Can fully convolutional networks perform well for general image restoration problems?
Authors: Subhajit Chaudhury, Hiya Roy
Date: 2016-11-14 00:00:00
Tasks: Denoising, Image Denoising, Image Inpainting, Image Reconstruction, Image Restoration, Semantic Segmentation

We present a fully convolutional network(FCN) based approach for color image
restoration. FCNs have recently shown remarkable performance for high-level
vision problem like semantic segmentation. In this paper, we investigate if FCN
models can show promising performance for low-level problems like image
restoration as well. We propose a fully convolutional model, that learns a
direct end-to-end mapping between the corrupted images as input and the desired
clean images as output. Our proposed method takes inspiration from domain
transformation techniques but presents a data-driven task specific approach
where filters for novel basis projection, task dependent coefficient
alterations, and image reconstruction are represented as convolutional
networks. Experimental results show that our FCN model outperforms traditional
sparse coding based methods and demonstrates competitive performance compared
to the state-of-the-art methods for image denoising. We further show that our
proposed model can solve the difficult problem of blind image inpainting and
can produce reconstructed images of impressive visual quality.
A Procedural Texture Generation Framework Based on Semantic Descriptions
Authors: Junyu Dong, Li-Na Wang, Jun Liu, Xin Sun
Date: 2017-04-13 00:00:00
Tasks: Multi-Label Learning, Texture Synthesis

Procedural textures are normally generated from mathematical models with
parameters carefully selected by experienced users. However, for naive users,
the intuitive way to obtain a desired texture is to provide semantic
descriptions such as "regular," "lacelike," and "repetitive" and then a
procedural model with proper parameters will be automatically suggested to
generate the corresponding textures. By contrast, it is less practical for
users to learn mathematical models and tune parameters based on multiple
examinations of large numbers of generated textures. In this study, we propose
a novel framework that generates procedural textures according to user-defined
semantic descriptions, and we establish a mapping between procedural models and
semantic texture descriptions. First, based on a vocabulary of semantic
attributes collected from psychophysical experiments, a multi-label learning
method is employed to annotate a large number of textures with semantic
attributes to form a semantic procedural texture dataset. Then, we derive a low
dimensional semantic space in which the semantic descriptions can be separated
from one other. Finally, given a set of semantic descriptions, the diverse
properties of the samples in the semantic space can lead the framework to find
an appropriate generation model that uses appropriate parameters to produce a
desired texture. The experimental results show that the proposed framework is
effective and that the generated textures closely correlate with the input
semantic descriptions.
Fashion Conversation Data on Instagram
Authors: Yu-I Ha, Sejeong Kwon, Meeyoung Cha, Jungseock Joo
Date: 2017-04-13 00:00:00
Tasks: Marketing

The fashion industry is establishing its presence on a number of
visual-centric social media like Instagram. This creates an interesting clash
as fashion brands that have traditionally practiced highly creative and
editorialized image marketing now have to engage with people on the platform
that epitomizes impromptu, realtime conversation. What kinds of fashion images
do brands and individuals share and what are the types of visual features that
attract likes and comments? In this research, we take both quantitative and
qualitative approaches to answer these questions. We analyze visual features of
fashion posts first via manual tagging and then via training on convolutional
neural networks. The classified images were examined across four types of
fashion brands: mega couture, small couture, designers, and high street. We
find that while product-only images make up the majority of fashion
conversation in terms of volume, body snaps and face images that portray
fashion items more naturally tend to receive a larger number of likes and
comments by the audience. Our findings bring insights into building an
automated tool for classifying or generating influential fashion information.
We make our novel dataset of {24,752} labeled images on fashion conversations,
containing visual and textual cues, available for the research community.
Neural Face Editing with Intrinsic Image Disentangling
Authors: Zhixin Shu, Ersin Yumer, Sunil Hadap, Kalyan Sunkavalli, Eli Shechtman, Dimitris Samaras
Date: 2017-04-13 00:00:00
Proceeding: CVPR 2017 7
Tasks: Facial Editing, Generative Adversarial Network

Traditional face editing methods often require a number of sophisticated and
task specific algorithms to be applied one after the other --- a process that
is tedious, fragile, and computationally intensive. In this paper, we propose
an end-to-end generative adversarial network that infers a face-specific
disentangled representation of intrinsic face properties, including shape (i.e.
normals), albedo, and lighting, and an alpha matte. We show that this network
can be trained on "in-the-wild" images by incorporating an in-network
physically-based image formation module and appropriate loss functions. Our
disentangling latent representation allows for semantically relevant edits,
where one aspect of facial appearance can be manipulated while keeping
orthogonal properties fixed, and we demonstrate its use for a number of facial
editing applications.
A Search for Improved Performance in Regular Expressions
Authors: Brendan Cody-Kenny, Michael Fenton, Adrian Ronayne, Eoghan Considine, Thomas McGuire, Michael O'Neill
Date: 2017-04-13 00:00:00
Tasks: Diversity

The primary aim of automated performance improvement is to reduce the running
time of programs while maintaining (or improving on) functionality. In this
paper, Genetic Programming is used to find performance improvements in regular
expressions for an array of target programs, representing the first application
of automated software improvement for run-time performance in the Regular
Expression language. This particular problem is interesting as there may be
many possible alternative regular expressions which perform the same task while
exhibiting subtle differences in performance. A benchmark suite of candidate
regular expressions is proposed for improvement. We show that the application
of Genetic Programming techniques can result in performance improvements in all
cases.
  As we start evolution from a known good regular expression, diversity is
critical in escaping the local optima of the seed expression. In order to
understand diversity during evolution we compare an initial population
consisting of only seed programs with a population initialised using a
combination of a single seed individual with individuals generated using PI
Grow and Ramped-half-and-half initialisation mechanisms.
Gromov-Hausdorff limit of Wasserstein spaces on point clouds
Authors: Nicolas Garcia Trillos
Date: 2017-02-11 00:00:00

We consider a point cloud $X_n := \{ x_1, \dots, x_n \}$ uniformly distributed on the flat torus $\mathbb{T}^d : = \mathbb{R}^d / \mathbb{Z}^d $, and construct a geometric graph on the cloud by connecting points that are within distance $\varepsilon$ of each other. We let $\mathcal{P}(X_n)$ be the space of probability measures on $X_n$ and endow it with a discrete Wasserstein distance $W_n$ as introduced independently by Chow et al, Maas, and Mielke for general finite Markov chains. We show that as long as $\varepsilon= \varepsilon_n$ decays towards zero slower than an explicit rate depending on the level of uniformity of $X_n$, then the space $(\mathcal{P}(X_n), W_n)$ converges in the Gromov-Hausdorff sense towards the space of probability measures on $\mathbb{T}^d$ endowed with the Wasserstein distance. The analysis presented in this paper is a first step in the study of stability of evolution equations defined over random point clouds as the number of points grows to infinity.
Boosting as a kernel-based method
Authors: Aleksandr Y. Aravkin, Giulio Bottegal, Gianluigi Pillonetto
Date: 2016-08-08 00:00:00
Tasks: General Classification, regression

Boosting combines weak (biased) learners to obtain effective learning
algorithms for classification and prediction. In this paper, we show a
connection between boosting and kernel-based methods, highlighting both
theoretical and practical applications. In the context of $\ell_2$ boosting, we
start with a weak linear learner defined by a kernel $K$. We show that boosting
with this learner is equivalent to estimation with a special {\it boosting
kernel} that depends on $K$, as well as on the regression matrix, noise
variance, and hyperparameters. The number of boosting iterations is modeled as
a continuous hyperparameter, and fit along with other parameters using standard
techniques.
  We then generalize the boosting kernel to a broad new class of boosting
approaches for more general weak learners, including those based on the
$\ell_1$, hinge and Vapnik losses. The approach allows fast hyperparameter
tuning for this general class, and has a wide range of applications, including
robust regression and classification. We illustrate some of these applications
with numerical examples on synthetic and real data.
Recognizing Activities of Daily Living from Egocentric Images
Authors: Alejandro Cartas, Juan Marín, Petia Radeva, Mariella Dimiccoli
Date: 2017-04-13 00:00:00
Tasks: Activity Recognition, General Classification

Recognizing Activities of Daily Living (ADLs) has a large number of health
applications, such as characterize lifestyle for habit improvement, nursing and
rehabilitation services. Wearable cameras can daily gather large amounts of
image data that provide rich visual information about ADLs than using other
wearable sensors. In this paper, we explore the classification of ADLs from
images captured by low temporal resolution wearable camera (2fpm) by using a
Convolutional Neural Networks (CNN) approach. We show that the classification
accuracy of a CNN largely improves when its output is combined, through a
random decision forest, with contextual information from a fully connected
layer. The proposed method was tested on a subset of the NTCIR-12 egocentric
dataset, consisting of 18,674 images and achieved an overall accuracy of 86%
activity recognition on 21 classes.