pwc-archive/papers-with-abstracts huggingface dataset:
An Automated Text Categorization Framework based on Hyperparameter Optimization
Authors: Eric S. Tellez, Daniela Moctezuma, Sabino Miranda-Jímenez, Mario Graff
Date: 2017-04-06 00:00:00
Tasks: Authorship Attribution, General Classification, Hyperparameter Optimization, Lemmatization, Sentiment Analysis, Spam detection, Text Categorization, text-classification, Text Classification

A great variety of text tasks such as topic or spam identification, user
profiling, and sentiment analysis can be posed as a supervised learning problem
and tackle using a text classifier. A text classifier consists of several
subprocesses, some of them are general enough to be applied to any supervised
learning problem, whereas others are specifically designed to tackle a
particular task, using complex and computational expensive processes such as
lemmatization, syntactic analysis, etc. Contrary to traditional approaches, we
propose a minimalistic and wide system able to tackle text classification tasks
independent of domain and language, namely microTC. It is composed by some easy
to implement text transformations, text representations, and a supervised
learning algorithm. These pieces produce a competitive classifier even in the
domain of informally written text. We provide a detailed description of microTC
along with an extensive experimental comparison with relevant state-of-the-art
methods. mircoTC was compared on 30 different datasets. Regarding accuracy,
microTC obtained the best performance in 20 datasets while achieves competitive
results in the remaining 10. The compared datasets include several problems
like topic and polarity classification, spam detection, user profiling and
authorship attribution. Furthermore, it is important to state that our approach
allows the usage of the technology even without knowledge of machine learning
and natural language processing.
Abstractions for AI-Based User Interfaces and Systems
Authors: Alex Renda, Harrison Goldstein, Sarah Bird, Chris Quirk, Adrian Sampson
Date: 2017-09-14 00:00:00

Novel user interfaces based on artificial intelligence, such as
natural-language agents, present new categories of engineering challenges.
These systems need to cope with uncertainty and ambiguity, interface with
machine learning algorithms, and compose information from multiple users to
make decisions. We propose to treat these challenges as language-design
problems. We describe three programming language abstractions for three core
problems in intelligent system design. First, hypothetical worlds support
nondeterministic search over spaces of alternative actions. Second, a feature
type system abstracts the interaction between applications and learning
algorithms. Finally, constructs for collaborative execution extend hypothetical
worlds across multiple machines while controlling access to private data. We
envision these features as first steps toward a complete language for
implementing AI-based interfaces and applications.
Toward Cognitive and Immersive Systems: Experiments in a Cognitive Microworld
Authors: Matthew Peveler, Naveen Sundar Govindarajulu, Selmer Bringsjord, Biplav Srivastava, Kartik Talamadupula, Hui Su
Date: 2017-09-14 00:00:00

As computational power has continued to increase, and sensors have become
more accurate, the corresponding advent of systems that are at once cognitive
and immersive has arrived. These \textit{cognitive and immersive systems}
(CAISs) fall squarely into the intersection of AI with HCI/HRI: such systems
interact with and assist the human agents that enter them, in no small part
because such systems are infused with AI able to understand and reason about
these humans and their knowledge, beliefs, goals, communications, plans, etc.
We herein explain our approach to engineering CAISs. We emphasize the capacity
of a CAIS to develop and reason over a `theory of the mind' of its human
partners. This capacity entails that the AI in question has a sophisticated
model of the beliefs, knowledge, goals, desires, emotions, etc.\ of these
humans. To accomplish this engineering, a formal framework of very high
expressivity is needed. In our case, this framework is a \textit{cognitive
event calculus}, a particular kind of quantified multi-operator modal logic,
and a matching high-expressivity automated reasoner and planner. To explain,
advance, and to a degree validate our approach, we show that a calculus of this
type satisfies a set of formal requirements, and can enable a CAIS to
understand a psychologically tricky scenario couched in what we call the
\textit{cognitive polysolid framework} (CPF). We also formally show that a room
that satisfies these requirements can have a useful property we term
\emph{expectation of usefulness}. CPF, a sub-class of \textit{cognitive
microworlds}, includes machinery able to represent and plan over not merely
blocks and actions (such as seen in the primitive `blocks worlds' of old), but
also over agents and their mental attitudes about both other agents and
inanimate objects.
On Coordinate Minimization of Convex Piecewise-Affine Functions
Authors: Tomas Werner
Date: 2017-09-14 00:00:00

A popular class of algorithms to optimize the dual LP relaxation of the
discrete energy minimization problem (a.k.a.\ MAP inference in graphical models
or valued constraint satisfaction) are convergent message-passing algorithms,
such as max-sum diffusion, TRW-S, MPLP and SRMP. These algorithms are
successful in practice, despite the fact that they are a version of coordinate
minimization applied to a convex piecewise-affine function, which is not
guaranteed to converge to a global minimizer. These algorithms converge only to
a local minimizer, characterized by local consistency known from constraint
programming. We generalize max-sum diffusion to a version of coordinate
minimization applicable to an arbitrary convex piecewise-affine function, which
converges to a local consistency condition. This condition can be seen as the
sign relaxation of the global optimality condition.
Deep Learning for Automatic Stereotypical Motor Movement Detection using Wearable Sensors in Autism Spectrum Disorders
Authors: Nastaran Mohammadian Rad, Seyed Mostafa Kia, Calogero Zarbo, Twan van Laarhoven, Giuseppe Jurman, Paola Venuti, Elena Marchiori, Cesare Furlanello
Date: 2017-09-14 00:00:00
Tasks: Ensemble Learning, Transfer Learning

Autism Spectrum Disorders are associated with atypical movements, of which
stereotypical motor movements (SMMs) interfere with learning and social
interaction. The automatic SMM detection using inertial measurement units (IMU)
remains complex due to the strong intra and inter-subject variability,
especially when handcrafted features are extracted from the signal. We propose
a new application of the deep learning to facilitate automatic SMM detection
using multi-axis IMUs. We use a convolutional neural network (CNN) to learn a
discriminative feature space from raw data. We show how the CNN can be used for
parameter transfer learning to enhance the detection rate on longitudinal data.
We also combine the long short-term memory (LSTM) with CNN to model the
temporal patterns in a sequence of multi-axis signals. Further, we employ
ensemble learning to combine multiple LSTM learners into a more robust SMM
detector. Our results show that: 1) feature learning outperforms handcrafted
features; 2) parameter transfer learning is beneficial in longitudinal
settings; 3) using LSTM to learn the temporal dynamic of signals enhances the
detection rate especially for skewed training data; 4) an ensemble of LSTMs
provides more accurate and stable detectors. These findings provide a
significant step toward accurate SMM detection in real-time scenarios.
Taxonomy Induction using Hypernym Subsequences
Authors: Amit Gupta, Rémi Lebret, Hamza Harkous, Karl Aberer
Date: 2017-04-25 00:00:00

We propose a novel, semi-supervised approach towards domain taxonomy
induction from an input vocabulary of seed terms. Unlike all previous
approaches, which typically extract direct hypernym edges for terms, our
approach utilizes a novel probabilistic framework to extract hypernym
subsequences. Taxonomy induction from extracted subsequences is cast as an
instance of the minimumcost flow problem on a carefully designed directed
graph. Through experiments, we demonstrate that our approach outperforms
stateof- the-art taxonomy induction approaches across four languages.
Importantly, we also show that our approach is robust to the presence of noise
in the input vocabulary. To the best of our knowledge, no previous approaches
have been empirically proven to manifest noise-robustness in the input
vocabulary.
Cross-Platform Emoji Interpretation: Analysis, a Solution, and Applications
Authors: Fred Morstatter, Kai Shu, Suhang Wang, Huan Liu
Date: 2017-09-14 00:00:00
Tasks: Sentiment Analysis

Most social media platforms are largely based on text, and users often write
posts to describe where they are, what they are seeing, and how they are
feeling. Because written text lacks the emotional cues of spoken and
face-to-face dialogue, ambiguities are common in written language. This problem
is exacerbated in the short, informal nature of many social media posts. To
bypass this issue, a suite of special characters called "emojis," which are
small pictograms, are embedded within the text. Many emojis are small
depictions of facial expressions designed to help disambiguate the emotional
meaning of the text. However, a new ambiguity arises in the way that emojis are
rendered. Every platform (Windows, Mac, and Android, to name a few) renders
emojis according to their own style. In fact, it has been shown that some
emojis can be rendered so differently that they look "happy" on some platforms,
and "sad" on others. In this work, we use real-world data to verify the
existence of this problem. We verify that the usage of the same emoji can be
significantly different across platforms, with some emojis exhibiting different
sentiment polarities on different platforms. We propose a solution to identify
the intended emoji based on the platform-specific nature of the emoji used by
the author of a social media post. We apply our solution to sentiment analysis,
a task that can benefit from the emoji calibration technique we use in this
work. We conduct experiments to evaluate the effectiveness of the mapping in
this task.
Dynamic Pricing in Competitive Markets
Authors: Paresh Nakhe
Date: 2017-09-14 00:00:00

Dynamic pricing of goods in a competitive environment to maximize revenue is
a natural objective and has been a subject of research over the years. In this
paper, we focus on a class of markets exhibiting the substitutes property with
sellers having divisible and replenishable goods. Depending on the prices
chosen, each seller observes a certain demand which is satisfied subject to the
supply constraint. The goal of the seller is to price her good dynamically so
as to maximize her revenue. For the static market case, when the consumer
utility satisfies the Constant Elasticity of Substitution (CES) property, we
give a $O(\sqrt{T})$ regret bound on the maximum loss in revenue of a seller
using a modified version of the celebrated Online Gradient Descent Algorithm by
Zinkevich. For a more specialized set of consumer utilities satisfying the
iso-elasticity condition, we show that when each seller uses a
regret-minimizing algorithm satisfying a certain technical property, the regret
with respect to $(1-\alpha)$ times optimal revenue is bounded as $O(T^{1/4} /
\sqrt{\alpha})$. We extend this result to markets with dynamic supplies and
prove a corresponding dynamic regret bound, whose guarantee deteriorates
smoothly with the inherent instability of the market. As a side-result, we also
extend the previously known convergence results of these algorithms in a
general game to the dynamic setting.
Shared Learning : Enhancing Reinforcement in $Q$-Ensembles
Authors: Rakesh R. Menon, Balaraman Ravindran
Date: 2017-09-14 00:00:00
Tasks: Atari Games, continuous-control, Continuous Control, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Deep Reinforcement Learning has been able to achieve amazing successes in a
variety of domains from video games to continuous control by trying to maximize
the cumulative reward. However, most of these successes rely on algorithms that
require a large amount of data to train in order to obtain results on par with
human-level performance. This is not feasible if we are to deploy these systems
on real world tasks and hence there has been an increased thrust in exploring
data efficient algorithms. To this end, we propose the Shared Learning
framework aimed at making $Q$-ensemble algorithms data-efficient. For achieving
this, we look into some principles of transfer learning which aim to study the
benefits of information exchange across tasks in reinforcement learning and
adapt transfer to learning our value function estimates in a novel manner. In
this paper, we consider the special case of transfer between the value function
estimates in the $Q$-ensemble architecture of BootstrappedDQN. We further
empirically demonstrate how our proposed framework can help in speeding up the
learning process in $Q$-ensembles with minimum computational overhead on a
suite of Atari 2600 Games.
One-Shot Visual Imitation Learning via Meta-Learning
Authors: Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, Sergey Levine
Date: 2017-09-14 00:00:00
Tasks: Imitation Learning, Meta-Learning

In order for a robot to be a generalist that can perform a wide range of
jobs, it must be able to acquire a wide variety of skills quickly and
efficiently in complex unstructured environments. High-capacity models such as
deep neural networks can enable a robot to represent complex skills, but
learning each skill from scratch then becomes infeasible. In this work, we
present a meta-imitation learning method that enables a robot to learn how to
learn more efficiently, allowing it to acquire new skills from just a single
demonstration. Unlike prior methods for one-shot imitation, our method can
scale to raw pixel inputs and requires data from significantly fewer prior
tasks for effective learning of new skills. Our experiments on both simulated
and real robot platforms demonstrate the ability to learn new tasks,
end-to-end, from a single visual demonstration.
Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages
Authors: Ehsaneddin Asgari, Hinrich Schütze
Date: 2017-04-28 00:00:00
Proceeding: EMNLP 2017 9

We present SuperPivot, an analysis method for low-resource languages that
occur in a superparallel corpus, i.e., in a corpus that contains an order of
magnitude more languages than parallel corpora currently in use. We show that
SuperPivot performs well for the crosslingual analysis of the linguistic
phenomenon of tense. We produce analysis results for more than 1000 languages,
conducting - to the best of our knowledge - the largest crosslingual
computational study performed to date. We extend existing methodology for
leveraging parallel corpora for typological analysis by overcoming a limiting
assumption of earlier work: We only require that a linguistic feature is
overtly marked in a few of thousands of languages as opposed to requiring that
it be marked in all languages under investigation.
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Authors: Briland Hitaj, Giuseppe Ateniese, Fernando Perez-Cruz
Date: 2017-02-24 00:00:00
Tasks: Deep Learning, Federated Learning, Generative Adversarial Network, Privacy Preserving

Deep Learning has recently become hugely popular in machine learning,
providing significant improvements in classification accuracy in the presence
of highly-structured and large databases.
  Researchers have also considered privacy implications of deep learning.
Models are typically trained in a centralized manner with all the data being
processed by the same training algorithm. If the data is a collection of users'
private data, including habits, personal pictures, geographical positions,
interests, and more, the centralized server will have access to sensitive
information that could potentially be mishandled. To tackle this problem,
collaborative deep learning models have recently been proposed where parties
locally train their deep learning structures and only share a subset of the
parameters in the attempt to keep their respective training sets private.
Parameters can also be obfuscated via differential privacy (DP) to make
information extraction even more challenging, as proposed by Shokri and
Shmatikov at CCS'15.
  Unfortunately, we show that any privacy-preserving collaborative deep
learning is susceptible to a powerful attack that we devise in this paper. In
particular, we show that a distributed, federated, or decentralized deep
learning approach is fundamentally broken and does not protect the training
sets of honest participants. The attack we developed exploits the real-time
nature of the learning process that allows the adversary to train a Generative
Adversarial Network (GAN) that generates prototypical samples of the targeted
training set that was meant to be private (the samples generated by the GAN are
intended to come from the same distribution as the training data).
Interestingly, we show that record-level DP applied to the shared parameters of
the model, as suggested in previous work, is ineffective (i.e., record-level DP
is not designed to address our attack).
Food Recognition using Fusion of Classifiers based on CNNs
Authors: Eduardo Aguilar, Marc Bolaños, Petia Radeva
Date: 2017-09-14 00:00:00
Tasks: Food Recognition, General Classification

With the arrival of convolutional neural networks, the complex problem of
food recognition has experienced an important improvement in recent years. The
best results have been obtained using methods based on very deep convolutional
neural networks, which show that the deeper the model,the better the
classification accuracy will be obtain. However, very deep neural networks may
suffer from the overfitting problem. In this paper, we propose a combination of
multiple classifiers based on different convolutional models that complement
each other and thus, achieve an improvement in performance. The evaluation of
our approach is done on two public datasets: Food-101 as a dataset with a wide
variety of fine-grained dishes, and Food-11 as a dataset of high-level food
categories, where our approach outperforms the independent CNN models.
Random Forests of Interaction Trees for Estimating Individualized Treatment Effects in Randomized Trials
Authors: Xiaogang Su, Annette T. Peña, Lei Liu, Richard A. Levine
Date: 2017-09-14 00:00:00

Assessing heterogeneous treatment effects has become a growing interest in
advancing precision medicine. Individualized treatment effects (ITE) play a
critical role in such an endeavor. Concerning experimental data collected from
randomized trials, we put forward a method, termed random forests of
interaction trees (RFIT), for estimating ITE on the basis of interaction trees
(Su et al., 2009). To this end, we first propose a smooth sigmoid surrogate
(SSS) method, as an alternative to greedy search, to speed up tree
construction. RFIT outperforms the traditional `separate regression' approach
in estimating ITE. Furthermore, standard errors for the estimated ITE via RFIT
can be obtained with the infinitesimal jackknife method. We assess and
illustrate the use of RFIT via both simulation and the analysis of data from an
acupuncture headache trial.
Large-scale Image Geo-Localization Using Dominant Sets
Authors: Eyasu Zemene, Yonatan Tariku, Haroon Idrees, Andrea Prati, Marcello Pelillo, Mubarak Shah
Date: 2017-02-04 00:00:00
Tasks: Clustering, geo-localization

This paper presents a new approach for the challenging problem of
geo-locating an image using image matching in a structured database of
city-wide reference images with known GPS coordinates. We cast the
geo-localization as a clustering problem on local image features. Akin to
existing approaches on the problem, our framework builds on low-level features
which allow partial matching between images. For each local feature in the
query image, we find its approximate nearest neighbors in the reference set.
Next, we cluster the features from reference images using Dominant Set
clustering, which affords several advantages over existing approaches. First,
it permits variable number of nodes in the cluster which we use to dynamically
select the number of nearest neighbors (typically coming from multiple
reference images) for each query feature based on its discrimination value.
Second, as we also quantify in our experiments, this approach is several orders
of magnitude faster than existing approaches. Thus, we obtain multiple clusters
(different local maximizers) and obtain a robust final solution to the problem
using multiple weak solutions through constrained Dominant Set clustering on
global image features, where we enforce the constraint that the query image
must be included in the cluster. This second level of clustering also bypasses
heuristic approaches to voting and selecting the reference image that matches
to the query. We evaluated the proposed framework on an existing dataset of
102k street view images as well as a new dataset of 300k images, and show that
it outperforms the state-of-the-art by 20% and 7%, respectively, on the two
datasets.
Informed Non-convex Robust Principal Component Analysis with Features
Authors: Niannan Xue, Jiankang Deng, Yannis Panagakis, Stefanos Zafeiriou
Date: 2017-09-14 00:00:00
Tasks: Denoising, General Classification, image-classification, Image Classification

We revisit the problem of robust principal component analysis with features
acting as prior side information. To this aim, a novel, elegant, non-convex
optimization approach is proposed to decompose a given observation matrix into
a low-rank core and the corresponding sparse residual. Rigorous theoretical
analysis of the proposed algorithm results in exact recovery guarantees with
low computational complexity. Aptly designed synthetic experiments demonstrate
that our method is the first to wholly harness the power of non-convexity over
convexity in terms of both recoverability and speed. That is, the proposed
non-convex approach is more accurate and faster compared to the best available
algorithms for the problem under study. Two real-world applications, namely
image classification and face denoising further exemplify the practical
superiority of the proposed method.
General problem solving with category theory
Authors: Francisco J. Arjonilla, Tetsuya OGATA
Date: 2017-09-14 00:00:00

This paper proposes a formal cognitive framework for problem solving based on
category theory. We introduce cognitive categories, which are categories with
exactly one morphism between any two objects. Objects in these categories are
interpreted as states and morphisms as transformations between states.
Moreover, cognitive problems are reduced to the specification of two objects in
a cognitive category: an outset (i.e. the current state of the system) and a
goal (i.e. the desired state). Cognitive systems transform the target system by
means of generators and evaluators. Generators realize cognitive operations
over a system by grouping morphisms, whilst evaluators group objects as a way
to generalize outsets and goals to partially defined states. Meta-cognition
emerges when the whole cognitive system is self-referenced as sub-states in the
cognitive category, whilst learning must always be considered as a
meta-cognitive process to maintain consistency. Several examples grounded in
basic AI methods are provided as well.