pwc-archive/papers-with-abstracts huggingface dataset:
Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features
Authors: Jeremy Kawahara, Ghassan Hamarneh
Date: 2017-03-14 00:00:00
Tasks: General Classification, Image Classification, Semantic Segmentation, Superpixels

The presence of certain clinical dermoscopic features within a skin lesion
may indicate melanoma, and automatically detecting these features may lead to
more quantitative and reproducible diagnoses. We reformulate the task of
classifying clinical dermoscopic features within superpixels as a segmentation
problem, and propose a fully convolutional neural network to detect clinical
dermoscopic features from dermoscopy skin lesion images. Our neural network
architecture uses interpolated feature maps from several intermediate network
layers, and addresses imbalanced labels by minimizing a negative multi-label
Dice-F$_1$ score, where the score is computed across the mini-batch for each
label. Our approach ranked first place in the 2017 ISIC-ISBI Part 2:
Dermoscopic Feature Classification Task challenge over both the provided
validation and test datasets, achieving a 0.895% area under the receiver
operator characteristic curve score. We show how simple baseline models can
outrank state-of-the-art approaches when using the official metrics of the
challenge, and propose to use a fuzzy Jaccard Index that ignores the empty set
(i.e., masks devoid of positive pixels) when ranking models. Our results
suggest that (i) the classification of clinical dermoscopic features can be
effectively approached as a segmentation problem, and (ii) the current metrics
used to rank models may not well capture the efficacy of the model. We plan to
make our trained model and code publicly available.
One Size Fits Many: Column Bundle for Multi-X Learning
Authors: Trang Pham, Truyen Tran, Svetha Venkatesh
Date: 2017-02-22 00:00:00
Tasks: MULTI-VIEW LEARNING

Much recent machine learning research has been directed towards leveraging
shared statistics among labels, instances and data views, commonly referred to
as multi-label, multi-instance and multi-view learning. The underlying premises
are that there exist correlations among input parts and among output targets,
and the predictive performance would increase when the correlations are
incorporated. In this paper, we propose Column Bundle (CLB), a novel deep
neural network for capturing the shared statistics in data. CLB is generic that
the same architecture can be applied for various types of shared statistics by
changing only input and output handling. CLB is capable of scaling to thousands
of input parts and output labels by avoiding explicit modeling of pairwise
relations. We evaluate CLB on different types of data: (a) multi-label, (b)
multi-view, (c) multi-view/multi-label and (d) multi-instance. CLB demonstrates
a comparable and competitive performance in all datasets against
state-of-the-art methods designed specifically for each type.
Sensor Fusion for Robot Control through Deep Reinforcement Learning
Authors: Steven Bohez, Tim Verbelen, Elias De Coninck, Bert Vankeirsbilck, Pieter Simoens, Bart Dhoedt
Date: 2017-03-13 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sensor Fusion

Deep reinforcement learning is becoming increasingly popular for robot
control algorithms, with the aim for a robot to self-learn useful feature
representations from unstructured sensory input leading to the optimal
actuation policy. In addition to sensors mounted on the robot, sensors might
also be deployed in the environment, although these might need to be accessed
via an unreliable wireless connection. In this paper, we demonstrate deep
neural network architectures that are able to fuse information coming from
multiple sensors and are robust to sensor failures at runtime. We evaluate our
method on a search and pick task for a robot both in simulation and the real
world.
High-Throughput and Language-Agnostic Entity Disambiguation and Linking on User Generated Data
Authors: Preeti Bhargava, Nemanja Spasojevic, Guoning Hu
Date: 2017-03-13 00:00:00
Tasks: Entity Disambiguation, Information Retrieval, Retrieval, Text Categorization

The Entity Disambiguation and Linking (EDL) task matches entity mentions in
text to a unique Knowledge Base (KB) identifier such as a Wikipedia or Freebase
id. It plays a critical role in the construction of a high quality information
network, and can be further leveraged for a variety of information retrieval
and NLP tasks such as text categorization and document tagging. EDL is a
complex and challenging problem due to ambiguity of the mentions and real world
text being multi-lingual. Moreover, EDL systems need to have high throughput
and should be lightweight in order to scale to large datasets and run on
off-the-shelf machines. More importantly, these systems need to be able to
extract and disambiguate dense annotations from the data in order to enable an
Information Retrieval or Extraction task running on the data to be more
efficient and accurate. In order to address all these challenges, we present
the Lithium EDL system and algorithm - a high-throughput, lightweight,
language-agnostic EDL system that extracts and correctly disambiguates 75% more
entities than state-of-the-art EDL systems and is significantly faster than
them.
Comparison of echo state network output layer classification methods on noisy data
Authors: Ashley Prater
Date: 2017-03-13 00:00:00
Tasks: Event Detection, General Classification, speech-recognition, Speech Recognition

Echo state networks are a recently developed type of recurrent neural network
where the internal layer is fixed with random weights, and only the output
layer is trained on specific data. Echo state networks are increasingly being
used to process spatiotemporal data in real-world settings, including speech
recognition, event detection, and robot control. A strength of echo state
networks is the simple method used to train the output layer - typically a
collection of linear readout weights found using a least squares approach.
Although straightforward to train and having a low computational cost to use,
this method may not yield acceptable accuracy performance on noisy data.
  This study compares the performance of three echo state network output layer
methods to perform classification on noisy data: using trained linear weights,
using sparse trained linear weights, and using trained low-rank approximations
of reservoir states. The methods are investigated experimentally on both
synthetic and natural datasets. The experiments suggest that using regularized
least squares to train linear output weights is superior on data with low
noise, but using the low-rank approximations may significantly improve accuracy
on datasets contaminated with higher noise levels.
Reinforcement Learning for Transition-Based Mention Detection
Authors: Georgiana Dinu, Wael Hamza, Radu Florian
Date: 2017-03-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper describes an application of reinforcement learning to the mention
detection task. We define a novel action-based formulation for the mention
detection task, in which a model can flexibly revise past labeling decisions by
grouping together tokens and assigning partial mention labels. We devise a
method to create mention-level episodes and we train a model by rewarding
correctly labeled complete mentions, irrespective of the inner structure
created. The model yields results which are on par with a competitive
supervised counterpart while being more flexible in terms of achieving targeted
behavior through reward modeling and generating internal mention structure,
especially on longer mentions.
Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
Authors: Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, Maurizio Tesconi
Date: 2017-03-13 00:00:00
Tasks: DNA analysis

Spambot detection in online social networks is a long-lasting challenge
involving the study and design of detection techniques capable of efficiently
identifying ever-evolving spammers. Recently, a new wave of social spambots has
emerged, with advanced human-like characteristics that allow them to go
undetected even by current state-of-the-art algorithms. In this paper, we show
that efficient spambots detection can be achieved via an in-depth analysis of
their collective behaviors exploiting the digital DNA technique for modeling
the behaviors of social network users. Inspired by its biological counterpart,
in the digital DNA representation the behavioral lifetime of a digital account
is encoded in a sequence of characters. Then, we define a similarity measure
for such digital DNA sequences. We build upon digital DNA and the similarity
between groups of users to characterize both genuine accounts and spambots.
Leveraging such characterization, we design the Social Fingerprinting
technique, which is able to discriminate among spambots and genuine accounts in
both a supervised and an unsupervised fashion. We finally evaluate the
effectiveness of Social Fingerprinting and we compare it with three
state-of-the-art detection algorithms. Among the peculiarities of our approach
is the possibility to apply off-the-shelf DNA analysis techniques to study
online users behaviors and to efficiently rely on a limited number of
lightweight account characteristics.
Geometrical morphology
Authors: John Goldsmith, Eric Rosen
Date: 2017-03-13 00:00:00

We explore inflectional morphology as an example of the relationship of the
discrete and the continuous in linguistics. The grammar requests a form of a
lexeme by specifying a set of feature values, which corresponds to a corner M
of a hypercube in feature value space. The morphology responds to that request
by providing a morpheme, or a set of morphemes, whose vector sum is
geometrically closest to the corner M. In short, the chosen morpheme $\mu$ is
the morpheme (or set of morphemes) that maximizes the inner product of $\mu$
and M.
DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks
Authors: Lingpeng Kong, Chris Alberti, Daniel Andor, Ivan Bogatyy, David Weiss
Date: 2017-03-13 00:00:00
Tasks: Decoder, Dependency Parsing, Extractive Summarization, Multi-Task Learning

In this work, we present a compact, modular framework for constructing novel
recurrent neural architectures. Our basic module is a new generic unit, the
Transition Based Recurrent Unit (TBRU). In addition to hidden layer
activations, TBRUs have discrete state dynamics that allow network connections
to be built dynamically as a function of intermediate activations. By
connecting multiple TBRUs, we can extend and combine commonly used
architectures such as sequence-to-sequence, attention mechanisms, and
re-cursive tree-structured models. A TBRU can also serve as both an encoder for
downstream tasks and as a decoder for its own task simultaneously, resulting in
more accurate multi-task learning. We call our approach Dynamic Recurrent
Acyclic Graphical Neural Networks, or DRAGNN. We show that DRAGNN is
significantly more accurate and efficient than seq2seq with attention for
syntactic dependency parsing and yields more accurate multi-task learning for
extractive summarization tasks.
An Empirical-Bayes Score for Discrete Bayesian Networks
Authors: Marco Scutari
Date: 2016-05-12 00:00:00

Bayesian network structure learning is often performed in a Bayesian setting,
by evaluating candidate structures using their posterior probabilities for a
given data set. Score-based algorithms then use those posterior probabilities
as an objective function and return the maximum a posteriori network as the
learned model. For discrete Bayesian networks, the canonical choice for a
posterior score is the Bayesian Dirichlet equivalent uniform (BDeu) marginal
likelihood with a uniform (U) graph prior (Heckerman et al., 1995). Its
favourable theoretical properties descend from assuming a uniform prior both on
the space of the network structures and on the space of the parameters of the
network. In this paper, we revisit the limitations of these assumptions; and we
introduce an alternative set of assumptions and the resulting score: the
Bayesian Dirichlet sparse (BDs) empirical Bayes marginal likelihood with a
marginal uniform (MU) graph prior. We evaluate its performance in an extensive
simulation study, showing that MU+BDs is more accurate than U+BDeu both in
learning the structure of the network and in predicting new observations, while
not being computationally more complex to estimate.
Fixpoint Approximation of Strategic Abilities under Imperfect Information
Authors: Wojciech Jamroga, Michał Knapik, Damian Kurpiewski
Date: 2016-12-08 00:00:00

Model checking of strategic ability under imperfect information is known to
be hard. The complexity results range from NP-completeness to undecidability,
depending on the precise setup of the problem. No less importantly, fixpoint
equivalences do not generally hold for imperfect information strategies, which
seriously hampers incremental synthesis of winning strategies. In this paper,
we propose translations of ATLir formulae that provide lower and upper bounds
for their truth values, and are cheaper to verify than the original
specifications. That is, if the expression is verified as true then the
corresponding formula of ATLir should also hold in the given model. We begin by
showing where the straightforward approach does not work. Then, we propose how
it can be modified to obtain guaranteed lower bounds. To this end, we alter the
next-step operator in such a way that traversing one's indistinguishability
relation is seen as atomic activity. Most interestingly, the lower
approximation is provided by a fixpoint expression that uses a nonstandard
variant of the next-step ability operator. We show the correctness of the
translations, establish their computational complexity, and validate the
approach by experiments with a scalable scenario of Bridge play.
Locating a Small Cluster Privately
Authors: Kobbi Nissim, Uri Stemmer, Salil Vadhan
Date: 2016-04-19 00:00:00
Tasks: Clustering

We present a new algorithm for locating a small cluster of points with
differential privacy [Dwork, McSherry, Nissim, and Smith, 2006]. Our algorithm
has implications to private data exploration, clustering, and removal of
outliers. Furthermore, we use it to significantly relax the requirements of the
sample and aggregate technique [Nissim, Raskhodnikova, and Smith, 2007], which
allows compiling of "off the shelf" (non-private) analyses into analyses that
preserve differential privacy.
Encoder-decoder with Focus-mechanism for Sequence Labelling Based Spoken Language Understanding
Authors: Su Zhu, Kai Yu
Date: 2016-08-06 00:00:00
Tasks: Decoder, speech-recognition, Speech Recognition, Spoken Language Understanding

This paper investigates the framework of encoder-decoder with attention for
sequence labelling based spoken language understanding. We introduce
Bidirectional Long Short Term Memory - Long Short Term Memory networks
(BLSTM-LSTM) as the encoder-decoder model to fully utilize the power of deep
learning. In the sequence labelling task, the input and output sequences are
aligned word by word, while the attention mechanism cannot provide the exact
alignment. To address this limitation, we propose a novel focus mechanism for
encoder-decoder framework. Experiments on the standard ATIS dataset showed that
BLSTM-LSTM with focus mechanism defined the new state-of-the-art by
outperforming standard BLSTM and attention based encoder-decoder. Further
experiments also show that the proposed model is more robust to speech
recognition errors.
Guetzli: Perceptually Guided JPEG Encoder
Authors: Jyrki Alakuijala, Robert Obryk, Ostap Stoliarchuk, Zoltan Szabadka, Lode Vandevenne, Jan Wassenberg
Date: 2017-03-13 00:00:00
Tasks: Perceptual Distance, Quantization

Guetzli is a new JPEG encoder that aims to produce visually indistinguishable
images at a lower bit-rate than other common JPEG encoders. It optimizes both
the JPEG global quantization tables and the DCT coefficient values in each JPEG
block using a closed-loop optimizer. Guetzli uses Butteraugli, our perceptual
distance metric, as the source of feedback in its optimization process. We
reach a 29-45% reduction in data size for a given perceptual distance,
according to Butteraugli, in comparison to other compressors we tried.
Guetzli's computation is currently extremely slow, which limits its
applicability to compressing static content and serving as a proof- of-concept
that we can achieve significant reductions in size by combining advanced
psychovisual models with lossy compression techniques.
Improving LBP and its variants using anisotropic diffusion
Authors: Mariane B. Neiva, Patrick Guidotti, Odemir M. Bruno
Date: 2017-03-13 00:00:00
Tasks: General Classification, Texture Classification

The main purpose of this paper is to propose a new preprocessing step in
order to improve local feature descriptors and texture classification.
Preprocessing is implemented by using transformations which help highlight
salient features that play a significant role in texture recognition. We
evaluate and compare four different competing methods: three different
anisotropic diffusion methods including the classical anisotropic Perona-Malik
diffusion and two subsequent regularizations of it and the application of a
Gaussian kernel, which is the classical multiscale approach in texture
analysis. The combination of the transformed images and the original ones are
analyzed. The results show that the use of the preprocessing step does lead to
improved texture recognition.
El Lenguaje Natural como Lenguaje Formal
Authors: Franco M. Luque
Date: 2017-03-13 00:00:00

Formal languages theory is useful for the study of natural language. In
particular, it is of interest to study the adequacy of the grammatical
formalisms to express syntactic phenomena present in natural language. First,
it helps to draw hypothesis about the nature and complexity of the
speaker-hearer linguistic competence, a fundamental question in linguistics and
other cognitive sciences. Moreover, from an engineering point of view, it
allows the knowledge of practical limitations of applications based on those
formalisms. In this article I introduce the adequacy problem of grammatical
formalisms for natural language, also introducing some formal language theory
concepts required for this discussion. Then, I review the formalisms that have
been proposed in history, and the arguments that have been given to support or
reject their adequacy.
  -----
  La teor\'ia de lenguajes formales es \'util para el estudio de los lenguajes
naturales. En particular, resulta de inter\'es estudiar la adecuaci\'on de los
formalismos gramaticales para expresar los fen\'omenos sint\'acticos presentes
en el lenguaje natural. Primero, ayuda a trazar hip\'otesis acerca de la
naturaleza y complejidad de las competencias ling\"u\'isticas de los
hablantes-oyentes del lenguaje, un interrogante fundamental de la
ling\"u\'istica y otras ciencias cognitivas. Adem\'as, desde el punto de vista
de la ingenier\'ia, permite conocer limitaciones pr\'acticas de las
aplicaciones basadas en dichos formalismos. En este art\'iculo hago una
introducci\'on al problema de la adecuaci\'on de los formalismos gramaticales
para el lenguaje natural, introduciendo tambi\'en algunos conceptos de la
teor\'ia de lenguajes formales necesarios para esta discusi\'on. Luego, hago un
repaso de los formalismos que han sido propuestos a lo largo de la historia, y
de los argumentos que se han dado para sostener o refutar su adecuaci\'on.
Users prefer Guetzli JPEG over same-sized libjpeg
Authors: Jyrki Alakuijala, Robert Obryk, Zoltan Szabadka, Jan Wassenberg
Date: 2017-03-13 00:00:00

We report on pairwise comparisons by human raters of JPEG images from libjpeg
and our new Guetzli encoder. Although both files are size-matched, 75% of
ratings are in favor of Guetzli. This implies the Butteraugli psychovisual
image similarity metric which guides Guetzli is reasonably close to human
perception at high quality levels. We provide access to the raw ratings and
source images for further analysis and study.
Zero-Shot Learning -- The Good, the Bad and the Ugly
Authors: Yongqin Xian, Bernt Schiele, Zeynep Akata
Date: 2017-03-13 00:00:00
Proceeding: CVPR 2017 7
Tasks: Zero-Shot Learning

Due to the importance of zero-shot learning, the number of proposed approaches has increased steadily recently. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g. pre-training on zero-shot test classes. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss limitations of the current status of the area which can be taken as a basis for advancing it.
Cost-Based Intuitionist Probabilities on Spaces of Graphs, Hypergraphs and Theorems
Authors: Ben Goertzel
Date: 2017-03-13 00:00:00

A novel partial order is defined on the space of digraphs or hypergraphs,
based on assessing the cost of producing a graph via a sequence of elementary
transformations. Leveraging work by Knuth and Skilling on the foundations of
inference, and the structure of Heyting algebras on graph space, this partial
order is used to construct an intuitionistic probability measure that applies
to either digraphs or hypergraphs. As logical inference steps can be
represented as transformations on hypergraphs representing logical statements,
this also yields an intuitionistic probability measure on spaces of theorems.
The central result is also extended to yield intuitionistic probabilities based
on more general weighted rule systems defined over bicartesian closed
categories.
Symbol Grounding via Chaining of Morphisms
Authors: Ruiting Lian, Ben Goertzel, Linas Vepstas, David Hanson, Changle Zhou
Date: 2017-03-13 00:00:00
Tasks: Common Sense Reasoning

A new model of symbol grounding is presented, in which the structures of
natural language, logical semantics, perception and action are represented
categorically, and symbol grounding is modeled via the composition of morphisms
between the relevant categories. This model gives conceptual insight into the
fundamentally systematic nature of symbol grounding, and also connects
naturally to practical real-world AI systems in current research and commercial
use. Specifically, it is argued that the structure of linguistic syntax can be
modeled as a certain asymmetric monoidal category, as e.g. implicit in the link
grammar formalism; the structure of spatiotemporal relationships and action
plans can be modeled similarly using "image grammars" and "action grammars";
and common-sense logical semantic structure can be modeled using
dependently-typed lambda calculus with uncertain truth values. Given these
formalisms, the grounding of linguistic descriptions in spatiotemporal
perceptions and coordinated actions consists of following morphisms from
language to logic through to spacetime and body (for comprehension), and vice
versa (for generation). The mapping is indicated between the spatial
relationships in the Region Connection Calculus and Allen Interval Algebra and
corresponding entries in the link grammar syntax parsing dictionary. Further,
the abstractions introduced here are shown to naturally model the structures
and systems currently being deployed in the context of using the OpenCog
cognitive architecture to control Hanson Robotics humanoid robots.