pwc-archive/papers-with-abstracts huggingface dataset:
Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms
Authors: Qing Wang, Chunqiu Zeng, Wubai Zhou, Tao Li, Larisa Shwartz, Genady Ya. Grabarnik
Date: 2017-08-10 00:00:00
Tasks: Articles, Collaborative Filtering, Interactive Recommendation, Recommendation Systems

Online interactive recommender systems strive to promptly suggest to
consumers appropriate items (e.g., movies, news articles) according to the
current context including both the consumer and item content information.
However, such context information is often unavailable in practice for the
recommendation, where only the users' interaction data on items can be
utilized. Moreover, the lack of interaction records, especially for new users
and items, worsens the performance of recommendation further. To address these
issues, collaborative filtering (CF), one of the recommendation techniques
relying on the interaction data only, as well as the online multi-armed bandit
mechanisms, capable of achieving the balance between exploitation and
exploration, are adopted in the online interactive recommendation settings, by
assuming independent items (i.e., arms). Nonetheless, the assumption rarely
holds in reality, since the real-world items tend to be correlated with each
other (e.g., two articles with similar topics). In this paper, we study online
interactive collaborative filtering problems by considering the dependencies
among items. We explicitly formulate the item dependencies as the clusters on
arms, where the arms within a single cluster share the similar latent topics.
In light of the topic modeling techniques, we come up with a generative model
to generate the items from their underlying topics. Furthermore, an efficient
online algorithm based on particle learning is developed for inferring both
latent parameters and states of our model. Additionally, our inferred model can
be naturally integrated with existing multi-armed selection strategies in the
online interactive collaborating setting. Empirical studies on two real-world
applications, online recommendations of movies and news, demonstrate both the
effectiveness and efficiency of the proposed approach.
Going Deeper with Semantics: Video Activity Interpretation using Semantic Contextualization
Authors: Sathyanarayanan N. Aakur, Fillipe DM de Souza, Sudeep Sarkar
Date: 2017-08-11 00:00:00

A deeper understanding of video activities extends beyond recognition of
underlying concepts such as actions and objects: constructing deep semantic
representations requires reasoning about the semantic relationships among these
concepts, often beyond what is directly observed in the data. To this end, we
propose an energy minimization framework that leverages large-scale commonsense
knowledge bases, such as ConceptNet, to provide contextual cues to establish
semantic relationships among entities directly hypothesized from video signal.
We mathematically express this using the language of Grenander's canonical
pattern generator theory. We show that the use of prior encoded commonsense
knowledge alleviate the need for large annotated training datasets and help
tackle imbalance in training through prior knowledge. Using three different
publicly available datasets - Charades, Microsoft Visual Description Corpus and
Breakfast Actions datasets, we show that the proposed model can generate video
interpretations whose quality is better than those reported by state-of-the-art
approaches, which have substantial training needs. Through extensive
experiments, we show that the use of commonsense knowledge from ConceptNet
allows the proposed approach to handle various challenges such as training data
imbalance, weak features, and complex semantic relationships and visual scenes.
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks
Authors: Surbhi Goel, Adam Klivans
Date: 2017-08-11 00:00:00
Proceeding: NeurIPS 2017 12

We consider the problem of learning function classes computed by neural
networks with various activations (e.g. ReLU or Sigmoid), a task believed to be
computationally intractable in the worst-case. A major open problem is to
understand the minimal assumptions under which these classes admit provably
efficient algorithms. In this work we show that a natural distributional
assumption corresponding to {\em eigenvalue decay} of the Gram matrix yields
polynomial-time algorithms in the non-realizable setting for expressive classes
of networks (e.g. feed-forward networks of ReLUs). We make no assumptions on
the structure of the network or the labels. Given sufficiently-strong
polynomial eigenvalue decay, we obtain {\em fully}-polynomial time algorithms
in {\em all} the relevant parameters with respect to square-loss. Milder decay
assumptions also lead to improved algorithms. This is the first purely
distributional assumption that leads to polynomial-time algorithms for networks
of ReLUs, even with one hidden layer. Further, unlike prior distributional
assumptions (e.g., the marginal distribution is Gaussian), eigenvalue decay has
been observed in practice on common data sets.
Deep Incremental Boosting
Authors: Alan Mosca, George D. Magoulas
Date: 2017-08-11 00:00:00
Tasks: Deep Learning

This paper introduces Deep Incremental Boosting, a new technique derived from
AdaBoost, specifically adapted to work with Deep Learning methods, that reduces
the required training time and improves generalisation. We draw inspiration
from Transfer of Learning approaches to reduce the start-up time to training
each incremental Ensemble member. We show a set of experiments that outlines
some preliminary results on some common Deep Learning datasets and discuss the
potential improvements Deep Incremental Boosting brings to traditional Ensemble
methods in Deep Learning.
WASSA-2017 Shared Task on Emotion Intensity
Authors: Saif M. Mohammad, Felipe Bravo-Marquez
Date: 2017-08-11 00:00:00
Proceeding: WS 2017 9

We present the first shared task on detecting the intensity of emotion felt
by the speaker of a tweet. We create the first datasets of tweets annotated for
anger, fear, joy, and sadness intensities using a technique called best--worst
scaling (BWS). We show that the annotations lead to reliable fine-grained
intensity scores (rankings of tweets by intensity). The data was partitioned
into training, development, and test sets for the competition. Twenty-two teams
participated in the shared task, with the best system obtaining a Pearson
correlation of 0.747 with the gold intensity scores. We summarize the machine
learning setups, resources, and tools used by the participating teams, with a
focus on the techniques and resources that are particularly useful for the
task. The emotion intensity dataset and the shared task are helping improve our
understanding of how we convey more or less intense emotions through language.
Improved Abusive Comment Moderation with User Embeddings
Authors: John Pavlopoulos, Prodromos Malakasiotis, Juli Bakagianni, Ion Androutsopoulos
Date: 2017-08-11 00:00:00
Proceeding: WS 2017 9
Tasks: Vocal Bursts Type Prediction

Experimenting with a dataset of approximately 1.6M user comments from a Greek
news sports portal, we explore how a state of the art RNN-based moderation
method can be improved by adding user embeddings, user type embeddings, user
biases, or user type biases. We observe improvements in all cases, with user
embeddings leading to the biggest performance gains.
Learning Rotation for Kernel Correlation Filter
Authors: Abdullah Hamdi, Bernard Ghanem
Date: 2017-08-11 00:00:00
Tasks: Visual Tracking

Kernel Correlation Filters have shown a very promising scheme for visual
tracking in terms of speed and accuracy on several benchmarks. However it
suffers from problems that affect its performance like occlusion, rotation and
scale change. This paper tries to tackle the problem of rotation by
reformulating the optimization problem for learning the correlation filter.
This modification (RKCF) includes learning rotation filter that utilizes
circulant structure of HOG feature to guesstimate rotation from one frame to
another and enhance the detection of KCF. Hence it gains boost in overall
accuracy in many of OBT50 detest videos with minimal additional computation.
Emotion Intensities in Tweets
Authors: Saif M. Mohammad, Felipe Bravo-Marquez
Date: 2017-08-11 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: regression

This paper examines the task of detecting intensity of emotion from text. We
create the first datasets of tweets annotated for anger, fear, joy, and sadness
intensities. We use a technique called best--worst scaling (BWS) that improves
annotation consistency and obtains reliable fine-grained scores. We show that
emotion-word hashtags often impact emotion intensity, usually conveying a more
intense emotion. Finally, we create a benchmark regression system and conduct
experiments to determine: which features are useful for detecting emotion
intensity, and, the extent to which two emotions are similar in terms of how
they manifest in language.
Deep Recurrent Neural Networks for mapping winter vegetation quality coverage via multi-temporal SAR Sentinel-1
Authors: Dinh Ho Tong Minh, Dino Ienco, Raffaele Gaetano, Nathalie Lalande, Emile Ndikumana, Faycal Osman, Pierre Maurel
Date: 2017-08-11 00:00:00
Tasks: Time Series, Time Series Analysis

Mapping winter vegetation quality coverage is a challenge problem of remote
sensing. This is due to the cloud coverage in winter period, leading to use
radar rather than optical images. The objective of this paper is to provide a
better understanding of the capabilities of radar Sentinel-1 and deep learning
concerning about mapping winter vegetation quality coverage. The analysis
presented in this paper is carried out on multi-temporal Sentinel-1 data over
the site of La Rochelle, France, during the campaign in December 2016. This
dataset were processed in order to produce an intensity radar data stack from
October 2016 to February 2017. Two deep Recurrent Neural Network (RNN) based
classifier methods were employed. We found that the results of RNNs clearly
outperformed the classical machine learning approaches (Support Vector Machine
and Random Forest). This study confirms that the time series radar Sentinel-1
and RNNs could be exploited for winter vegetation quality cover mapping.
Convolutional Neural Networks for Font Classification
Authors: Chris Tensmeyer, Daniel Saunders, Tony Martinez
Date: 2017-08-11 00:00:00
Tasks: Classification, Data Augmentation, General Classification, Optical Character Recognition, Optical Character Recognition (OCR)

Classifying pages or text lines into font categories aids transcription
because single font Optical Character Recognition (OCR) is generally more
accurate than omni-font OCR. We present a simple framework based on
Convolutional Neural Networks (CNNs), where a CNN is trained to classify small
patches of text into predefined font classes. To classify page or line images,
we average the CNN predictions over densely extracted patches. We show that
this method achieves state-of-the-art performance on a challenging dataset of
40 Arabic computer fonts with 98.8\% line level accuracy. This same method also
achieves the highest reported accuracy of 86.6% in predicting paleographic
scribal script classes at the page level on medieval Latin manuscripts.
Finally, we analyze what features are learned by the CNN on Latin manuscripts
and find evidence that the CNN is learning both the defining morphological
differences between scribal script classes as well as overfitting to
class-correlated nuisance factors. We propose a novel form of data augmentation
that improves robustness to text darkness, further increasing classification
performance.
Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data
Authors: Dominique T. Shipmon, Jason M. Gurevitch, Paolo M. Piselli, Stephen T. Edwards
Date: 2017-08-11 00:00:00
Tasks: Anomaly Classification, Anomaly Detection, Time Series, Time Series Analysis, Time Series Anomaly Detection

Google uses continuous streams of data from industry partners in order to
deliver accurate results to users. Unexpected drops in traffic can be an
indication of an underlying issue and may be an early warning that remedial
action may be necessary. Detecting such drops is non-trivial because streams
are variable and noisy, with roughly regular spikes (in many different shapes)
in traffic data. We investigated the question of whether or not we can predict
anomalies in these data streams. Our goal is to utilize Machine Learning and
statistical approaches to classify anomalous drops in periodic, but noisy,
traffic patterns. Since we do not have a large body of labeled examples to
directly apply supervised learning for anomaly classification, we approached
the problem in two parts. First we used TensorFlow to train our various models
including DNNs, RNNs, and LSTMs to perform regression and predict the expected
value in the time series. Secondly we created anomaly detection rules that
compared the actual values to predicted values. Since the problem requires
finding sustained anomalies, rather than just short delays or momentary
inactivity in the data, our two detection methods focused on continuous
sections of activity rather than just single points. We tried multiple
combinations of our models and rules and found that using the intersection of
our two anomaly detection methods proved to be an effective method of detecting
anomalies on almost all of our models. In the process we also found that not
all data fell within our experimental assumptions, as one data stream had no
periodicity, and therefore no time based model could predict it.
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning
Authors: Ning Liu, Zhe Li, Zhiyuan Xu, Jielong Xu, Sheng Lin, Qinru Qiu, Jian Tang, Yanzhi Wang
Date: 2017-03-13 00:00:00
Tasks: Cloud Computing, Decision Making, Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Automatic decision-making approaches, such as reinforcement learning (RL),
have been applied to (partially) solve the resource allocation problem
adaptively in the cloud computing system. However, a complete cloud resource
allocation framework exhibits high dimensions in state and action spaces, which
prohibit the usefulness of traditional RL techniques. In addition, high power
consumption has become one of the critical concerns in design and control of
cloud computing systems, which degrades system reliability and increases
cooling cost. An effective dynamic power management (DPM) policy should
minimize power consumption while maintaining performance degradation within an
acceptable level. Thus, a joint virtual machine (VM) resource allocation and
power management framework is critical to the overall cloud computing system.
Moreover, novel solution framework is necessary to address the even higher
dimensions in state and action spaces. In this paper, we propose a novel
hierarchical framework for solving the overall resource allocation and power
management problem in cloud computing systems. The proposed hierarchical
framework comprises a global tier for VM resource allocation to the servers and
a local tier for distributed power management of local servers. The emerging
deep reinforcement learning (DRL) technique, which can deal with complicated
control problems with large state space, is adopted to solve the global tier
problem. Furthermore, an autoencoder and a novel weight sharing structure are
adopted to handle the high-dimensional state space and accelerate the
convergence speed. On the other hand, the local tier of distributed server
power managements comprises an LSTM based workload predictor and a model-free
RL based power manager, operating in a distributed manner.
Systematic Testing of Convolutional Neural Networks for Autonomous Driving
Authors: Tommaso Dreossi, Shromona Ghosh, Alberto Sangiovanni-Vincentelli, Sanjit A. Seshia
Date: 2017-08-10 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Classification, General Classification

We present a framework to systematically analyze convolutional neural
networks (CNNs) used in classification of cars in autonomous vehicles. Our
analysis procedure comprises an image generator that produces synthetic
pictures by sampling in a lower dimension image modification subspace and a
suite of visualization tools. The image generator produces images which can be
used to test the CNN and hence expose its vulnerabilities. The presented
framework can be used to extract insights of the CNN classifier, compare across
classification models, or generate training and validation datasets.
Unsupervised Incremental Learning of Deep Descriptors From Video Streams
Authors: Federico Pernici, Alberto del Bimbo
Date: 2017-08-11 00:00:00
Tasks: Face Detection, Incremental Learning

We present a novel unsupervised method for face identity learning from video
sequences. The method exploits the ResNet deep network for face detection and
VGGface fc7 face descriptors together with a smart learning mechanism that
exploits the temporal coherence of visual data in video streams. We present a
novel feature matching solution based on Reverse Nearest Neighbour and a
feature forgetting strategy that supports incremental learning with memory size
control, while time progresses. It is shown that the proposed learning
procedure is asymptotically stable and can be effectively applied to relevant
applications like multiple face tracking.
A Large Deformation Diffeomorphic Approach to Registration of CLARITY Images via Mutual Information
Authors: Kwame S. Kutten, Nicolas Charon, Michael I. Miller, J. T. Ratnanather, Jordan Matelsky, Alexander D. Baden, Kunal Lillaney, Karl Deisseroth, Li Ye, Joshua T. Vogelstein
Date: 2016-12-01 00:00:00

CLARITY is a method for converting biological tissues into translucent and
porous hydrogel-tissue hybrids. This facilitates interrogation with light sheet
microscopy and penetration of molecular probes while avoiding physical slicing.
In this work, we develop a pipeline for registering CLARIfied mouse brains to
an annotated brain atlas. Due to the novelty of this microscopy technique it is
impractical to use absolute intensity values to align these images to existing
standard atlases. Thus we adopt a large deformation diffeomorphic approach for
registering images via mutual information matching. Furthermore we show how a
cascaded multi-resolution approach can improve registration quality while
reducing algorithm run time. As acquired image volumes were over a terabyte in
size, they were far too large for work on personal computers. Therefore the
NeuroData computational infrastructure was deployed for multi-resolution
storage and visualization of these images and aligned annotations on the web.
Semantic Word Clouds with Background Corpus Normalization and t-distributed Stochastic Neighbor Embedding
Authors: Erich Schubert, Andreas Spitz, Michael Weiler, Johanna Geiß, Michael Gertz
Date: 2017-08-11 00:00:00

Many word clouds provide no semantics to the word placement, but use a random
layout optimized solely for aesthetic purposes. We propose a novel approach to
model word significance and word affinity within a document, and in comparison
to a large background corpus. We demonstrate its usefulness for generating more
meaningful word clouds as a visual summary of a given document. We then select
keywords based on their significance and construct the word cloud based on the
derived affinity. Based on a modified t-distributed stochastic neighbor
embedding (t-SNE), we generate a semantic word placement. For words that
cooccur significantly, we include edges, and cluster the words according to
their cooccurrence. For this we designed a scalable and memory-efficient
sketch-based approach usable on commodity hardware to aggregate the required
corpus statistics needed for normalization, and for identifying keywords as
well as significant cooccurences. We empirically validate our approch using a
large Wikipedia corpus.
Automatic Identification of AltLexes using Monolingual Parallel Corpora
Authors: Elnaz Davoodi, Leila Kosseim
Date: 2017-08-11 00:00:00
Proceeding: RANLP 2017 9
Tasks: Text Simplification

The automatic identification of discourse relations is still a challenging
task in natural language processing. Discourse connectives, such as "since" or
"but", are the most informative cues to identify explicit relations; however
discourse parsers typically use a closed inventory of such connectives. As a
result, discourse relations signaled by markers outside these inventories (i.e.
AltLexes) are not detected as effectively. In this paper, we propose a novel
method to leverage parallel corpora in text simplification and lexical
resources to automatically identify alternative lexicalizations that signal
discourse relation. When applied to the Simple Wikipedia and Newsela corpora
along with WordNet and the PPDB, the method allowed the automatic discovery of
91 AltLexes.
Neural Translation of Musical Style
Authors: Iman Malik, Carl Henrik Ek
Date: 2017-08-11 00:00:00
Tasks: Translation

Music is an expressive form of communication often used to convey emotion in
scenarios where "words are not enough". Part of this information lies in the
musical composition where well-defined language exists. However, a significant
amount of information is added during a performance as the musician interprets
the composition. The performer injects expressiveness into the written score
through variations of different musical properties such as dynamics and tempo.
In this paper, we describe a model that can learn to perform sheet music. Our
research concludes that the generated performances are indistinguishable from a
human performance, thereby passing a test in the spirit of a "musical Turing
test".
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces
Authors: Benjamin Paaßen, Christina Göpfert, Barbara Hammer
Date: 2017-04-21 00:00:00
Tasks: Distributed Computing, Gaussian Processes, regression, Time Series, Time Series Analysis, Time Series Prediction

Graph models are relevant in many fields, such as distributed computing,
intelligent tutoring systems or social network analysis. In many cases, such
models need to take changes in the graph structure into account, i.e. a varying
number of nodes or edges. Predicting such changes within graphs can be expected
to yield important insight with respect to the underlying dynamics, e.g. with
respect to user behaviour. However, predictive techniques in the past have
almost exclusively focused on single edges or nodes. In this contribution, we
attempt to predict the future state of a graph as a whole. We propose to phrase
time series prediction as a regression problem and apply dissimilarity- or
kernel-based regression techniques, such as 1-nearest neighbor, kernel
regression and Gaussian process regression, which can be applied to graphs via
graph kernels. The output of the regression is a point embedded in a
pseudo-Euclidean space, which can be analyzed using subsequent dissimilarity-
or kernel-based processing methods. We discuss strategies to speed up Gaussian
Processes regression from cubic to linear time and evaluate our approach on two
well-established theoretical models of graph evolution as well as two real data
sets from the domain of intelligent tutoring systems. We find that simple
regression methods, such as kernel regression, are sufficient to capture the
dynamics in the theoretical models, but that Gaussian process regression
significantly improves the prediction error for real-world data.