pwc-archive/papers-with-abstracts huggingface dataset:
A Large-Scale CNN Ensemble for Medication Safety Analysis
Authors: Liliya Akhtyamova, Andrey Ignatov, John Cardiff
Date: 2017-06-17 00:00:00
Tasks: General Classification, Marketing

Revealing Adverse Drug Reactions (ADR) is an essential part of post-marketing
drug surveillance, and data from health-related forums and medical communities
can be of a great significance for estimating such effects. In this paper, we
propose an end-to-end CNN-based method for predicting drug safety on user
comments from healthcare discussion forums. We present an architecture that is
based on a vast ensemble of CNNs with varied structural parameters, where the
prediction is determined by the majority vote. To evaluate the performance of
the proposed solution, we present a large-scale dataset collected from a
medical website that consists of over 50 thousand reviews for more than 4000
drugs. The results demonstrate that our model significantly outperforms
conventional approaches and predicts medicine safety with an accuracy of 87.17%
for binary and 62.88% for multi-classification tasks.
Rgtsvm: Support Vector Machines on a GPU in R
Authors: Zhong Wang, Tinyi Chu, Lauren A Choate, Charles G Danko
Date: 2017-06-17 00:00:00
Tasks: General Classification, GPU, regression

Rgtsvm provides a fast and flexible support vector machine (SVM)
implementation for the R language. The distinguishing feature of Rgtsvm is that
support vector classification and support vector regression tasks are
implemented on a graphical processing unit (GPU), allowing the libraries to
scale to millions of examples with >100-fold improvement in performance over
existing implementations. Nevertheless, Rgtsvm retains feature parity and has
an interface that is compatible with the popular e1071 SVM package in R.
Altogether, Rgtsvm enables large SVM models to be created by both experienced
and novice practitioners.
Streaming Algorithm for Euler Characteristic Curves of Multidimensional Images
Authors: Teresa Heiss, Hubert Wagner
Date: 2017-05-04 00:00:00

We present an efficient algorithm to compute Euler characteristic curves of
gray scale images of arbitrary dimension. In various applications the Euler
characteristic curve is used as a descriptor of an image.
  Our algorithm is the first streaming algorithm for Euler characteristic
curves. The usage of streaming removes the necessity to store the entire image
in RAM. Experiments show that our implementation handles terabyte scale images
on commodity hardware. Due to lock-free parallelism, it scales well with the
number of processor cores. Our software---CHUNKYEuler---is available as open
source on Bitbucket.
  Additionally, we put the concept of the Euler characteristic curve in the
wider context of computational topology. In particular, we explain the
connection with persistence diagrams.
Rotation Invariance Neural Network
Authors: Shiyuan Li
Date: 2017-06-17 00:00:00
Tasks: One-Shot Learning, Position, Translation

Rotation invariance and translation invariance have great values in image
recognition tasks. In this paper, we bring a new architecture in convolutional
neural network (CNN) named cyclic convolutional layer to achieve rotation
invariance in 2-D symbol recognition. We can also get the position and
orientation of the 2-D symbol by the network to achieve detection purpose for
multiple non-overlap target. Last but not least, this architecture can achieve
one-shot learning in some cases using those invariance.
Adiabatic Quantum Computing for Binary Clustering
Authors: Christian Bauckhage, Eduardo Brito, Kostadin Cvejoski, Cesar Ojeda, Rafet Sifa, Stefan Wrobel
Date: 2017-06-17 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

Quantum computing for machine learning attracts increasing attention and
recent technological developments suggest that especially adiabatic quantum
computing may soon be of practical interest. In this paper, we therefore
consider this paradigm and discuss how to adopt it to the problem of binary
clustering. Numerical simulations demonstrate the feasibility of our approach
and illustrate how systems of qubits adiabatically evolve towards a solution.
Evaluating the quality of tourist agendas customized to different travel styles
Authors: Jesús Ibáñez-Ruiz, Laura Sebastiá, Eva Onaindia
Date: 2017-06-17 00:00:00
Tasks: Rhythm, Scheduling

Many tourist applications provide a personalized tourist agenda with the list
of recommended activities to the user. These applications must undoubtedly deal
with the constraints and preferences that define the user interests. Among
these preferences, we can find those that define the travel style of the user,
such as the rhythm of the trip, the number of visits to include in the tour or
the priority to visits of special interest for the user. In this paper, we deal
with the task of creating a customized tourist agenda as a planning and
scheduling application capable of conveniently scheduling the most appropriate
goals (visits) so as to maximize the user satisfaction with the tourist route.
This paper makes an analysis of the meaning of the travel style preferences and
compares the quality of the solutions obtained by two different solvers, a
PDDL-based planner and a Constraint Satisfaction Problem solver. We also define
several quality metrics and perform extensive experiments in order to evaluate
the results obtained with both solvers.
Place recognition: An Overview of Vision Perspective
Authors: Zhiqiang Zeng, Jian Zhang, Xiaodong Wang, Yuming Chen, Chaoyang Zhu
Date: 2017-06-17 00:00:00
Tasks: image-classification, Image Classification, Image Description, Image Retrieval, object-detection, Object Detection, Retrieval, Visual Place Recognition

Place recognition is one of the most fundamental topics in computer vision
and robotics communities, where the task is to accurately and efficiently
recognize the location of a given query image. Despite years of wisdom
accumulated in this field, place recognition still remains an open problem due
to the various ways in which the appearance of real-world places may differ.
This paper presents an overview of the place recognition literature. Since
condition invariant and viewpoint invariant features are essential factors to
long-term robust visual place recognition system, We start with traditional
image description methodology developed in the past, which exploit techniques
from image retrieval field. Recently, the rapid advances of related fields such
as object detection and image classification have inspired a new technique to
improve visual place recognition system, i.e., convolutional neural networks
(CNNs). Thus we then introduce recent progress of visual place recognition
system based on CNNs to automatically learn better image representations for
places. Eventually, we close with discussions and future work of place
recognition.
Training Deep Neural Networks via Optimization Over Graphs
Authors: Guo-Qiang Zhang, W. Bastiaan Kleijn
Date: 2017-02-11 00:00:00
Tasks: Distributed Optimization

In this work, we propose to train a deep neural network by distributed
optimization over a graph. Two nonlinear functions are considered: the
rectified linear unit (ReLU) and a linear unit with both lower and upper
cutoffs (DCutLU). The problem reformulation over a graph is realized by
explicitly representing ReLU or DCutLU using a set of slack variables. We then
apply the alternating direction method of multipliers (ADMM) to update the
weights of the network layerwise by solving subproblems of the reformulated
problem. Empirical results suggest that the ADMM-based method is less sensitive
to overfitting than the stochastic gradient descent (SGD) and Adam methods.
Open-Category Classification by Adversarial Sample Generation
Authors: Yang Yu, Wei-Yang Qu, Nan Li, Zimin Guo
Date: 2017-05-24 00:00:00
Tasks: Classification, General Classification

In real-world classification tasks, it is difficult to collect training
samples from all possible categories of the environment. Therefore, when an
instance of an unseen class appears in the prediction stage, a robust
classifier should be able to tell that it is from an unseen class, instead of
classifying it to be any known category. In this paper, adopting the idea of
adversarial learning, we propose the ASG framework for open-category
classification. ASG generates positive and negative samples of seen categories
in the unsupervised manner via an adversarial learning strategy. With the
generated samples, ASG then learns to tell seen from unseen in the supervised
manner. Experiments performed on several datasets show the effectiveness of
ASG.
Bayesian Conditional Generative Adverserial Networks
Authors: M. Ehsan Abbasnejad, Qinfeng Shi, Iman Abbasnejad, Anton Van Den Hengel, Anthony Dick
Date: 2017-06-17 00:00:00

Traditional GANs use a deterministic generator function (typically a neural
network) to transform a random noise input $z$ to a sample $\mathbf{x}$ that
the discriminator seeks to distinguish. We propose a new GAN called Bayesian
Conditional Generative Adversarial Networks (BC-GANs) that use a random
generator function to transform a deterministic input $y'$ to a sample
$\mathbf{x}$. Our BC-GANs extend traditional GANs to a Bayesian framework, and
naturally handle unsupervised learning, supervised learning, and
semi-supervised learning problems. Experiments show that the proposed BC-GANs
outperforms the state-of-the-arts.
SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions
Authors: Han Xiao, Minlie Huang, Xiaoyan Zhu
Date: 2016-04-17 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Question Answering

Knowledge representation is an important, long-history topic in AI, and there
have been a large amount of work for knowledge graph embedding which projects
symbolic entities and relations into low-dimensional, real-valued vector space.
However, most embedding methods merely concentrate on data fitting and ignore
the explicit semantic expression, leading to uninterpretable representations.
Thus, traditional embedding methods have limited potentials for many
applications such as question answering, and entity classification. To this
end, this paper proposes a semantic representation method for knowledge graph
\textbf{(KSR)}, which imposes a two-level hierarchical generative process that
globally extracts many aspects and then locally assigns a specific category in
each aspect for every triple. Since both aspects and categories are
semantics-relevant, the collection of categories in each aspect is treated as
the semantic representation of this triple. Extensive experiments justify our
model outperforms other state-of-the-art baselines substantially.
Comparison among dimensionality reduction techniques based on Random Projection for cancer classification
Authors: Haozhe Xie, Jie Li, Qiaosheng Zhang, Yadong Wang
Date: 2016-08-25 00:00:00
Tasks: Cancer Classification, Classification, Dimensionality Reduction, feature selection, General Classification

Random Projection (RP) technique has been widely applied in many scenarios
because it can reduce high-dimensional features into low-dimensional space
within short time and meet the need of real-time analysis of massive data.
There is an urgent need of dimensionality reduction with fast increase of big
genomics data. However, the performance of RP is usually lower. We attempt to
improve classification accuracy of RP through combining other reduction
dimension methods such as Principle Component Analysis (PCA), Linear
Discriminant Analysis (LDA), and Feature Selection (FS). We compared
classification accuracy and running time of different combination methods on
three microarray datasets and a simulation dataset. Experimental results show a
remarkable improvement of 14.77% in classification accuracy of FS followed by
RP compared to RP on BC-TCGA dataset. LDA followed by RP also helps RP to yield
a more discriminative subspace with an increase of 13.65% on classification
accuracy on the same dataset. FS followed by RP outperforms other combination
methods in classification accuracy on most of the datasets.
From One Point to A Manifold: Knowledge Graph Embedding For Precise Link Prediction
Authors: Han Xiao, Minlie Huang, Xiaoyan Zhu
Date: 2015-12-15 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Link Prediction, Position, Prediction

Knowledge graph embedding aims at offering a numerical knowledge
representation paradigm by transforming the entities and relations into
continuous vector space. However, existing methods could not characterize the
knowledge graph in a fine degree to make a precise prediction. There are two
reasons: being an ill-posed algebraic system and applying an overstrict
geometric form. As precise prediction is critical, we propose an manifold-based
embedding principle (\textbf{ManifoldE}) which could be treated as a well-posed
algebraic system that expands the position of golden triples from one point in
current models to a manifold in ours. Extensive experiments show that the
proposed models achieve substantial improvements against the state-of-the-art
baselines especially for the precise prediction task, and yet maintain high
efficiency.
Modular Multitask Reinforcement Learning with Policy Sketches
Authors: Jacob Andreas, Dan Klein, Sergey Levine
Date: 2016-11-06 00:00:00
Proceeding: ICML 2017 8
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We describe a framework for multitask deep reinforcement learning guided by
policy sketches. Sketches annotate tasks with sequences of named subtasks,
providing information about high-level structural relationships among tasks but
not how to implement them---specifically not providing the detailed guidance
used by much previous work on learning policy abstractions for RL (e.g.
intermediate rewards, subtask completion signals, or intrinsic motivations). To
learn from sketches, we present a model that associates every subtask with a
modular subpolicy, and jointly maximizes reward over full task-specific
policies by tying parameters across shared subpolicies. Optimization is
accomplished via a decoupled actor--critic training objective that facilitates
learning common behaviors from multiple dissimilar reward functions. We
evaluate the effectiveness of our approach in three environments featuring both
discrete and continuous control, and with sparse rewards that can be obtained
only after completing a number of high-level subgoals. Experiments show that
using our approach to learn policies guided by sketches gives better
performance than existing techniques for learning task-specific or shared
policies, while naturally inducing a library of interpretable primitive
behaviors that can be recombined to rapidly adapt to new tasks.
An online sequence-to-sequence model for noisy speech recognition
Authors: Chung-Cheng Chiu, Dieterich Lawson, Yuping Luo, George Tucker, Kevin Swersky, Ilya Sutskever, Navdeep Jaitly
Date: 2017-06-16 00:00:00
Tasks: Noisy Speech Recognition, speech-recognition, Speech Recognition

Generative models have long been the dominant approach for speech
recognition. The success of these models however relies on the use of
sophisticated recipes and complicated machinery that is not easily accessible
to non-practitioners. Recent innovations in Deep Learning have given rise to an
alternative - discriminative models called Sequence-to-Sequence models, that
can almost match the accuracy of state of the art generative models. While
these models are easy to train as they can be trained end-to-end in a single
step, they have a practical limitation that they can only be used for offline
recognition. This is because the models require that the entirety of the input
sequence be available at the beginning of inference, an assumption that is not
valid for instantaneous speech recognition. To address this problem, online
sequence-to-sequence models were recently introduced. These models are able to
start producing outputs as data arrives, and the model feels confident enough
to output partial transcripts. These models, like sequence-to-sequence are
causal - the output produced by the model until any time, $t$, affects the
features that are computed subsequently. This makes the model inherently more
powerful than generative models that are unable to change features that are
computed from the data. This paper highlights two main contributions - an
improvement to online sequence-to-sequence model training, and its application
to noisy settings with mixed speech from two speakers.
Sifting Common Information from Many Variables
Authors: Greg Ver Steeg, Shuyang Gao, Kyle Reing, Aram Galstyan
Date: 2016-06-07 00:00:00
Tasks: blind source separation, Dimensionality Reduction

Measuring the relationship between any pair of variables is a rich and active
area of research that is central to scientific practice. In contrast,
characterizing the common information among any group of variables is typically
a theoretical exercise with few practical methods for high-dimensional data. A
promising solution would be a multivariate generalization of the famous Wyner
common information, but this approach relies on solving an apparently
intractable optimization problem. We leverage the recently introduced
information sieve decomposition to formulate an incremental version of the
common information problem that admits a simple fixed point solution, fast
convergence, and complexity that is linear in the number of variables. This
scalable approach allows us to demonstrate the usefulness of common information
in high-dimensional learning problems. The sieve outperforms standard methods
on dimensionality reduction tasks, solves a blind source separation problem
that cannot be solved with ICA, and accurately recovers structure in brain
imaging data.
VideoMCC: a New Benchmark for Video Comprehension
Authors: Du Tran, Maksim Bolonkin, Manohar Paluri, Lorenzo Torresani
Date: 2016-06-23 00:00:00
Tasks: Multiple-choice, Video Description, Video Understanding

While there is overall agreement that future technology for organizing,
browsing and searching videos hinges on the development of methods for
high-level semantic understanding of video, so far no consensus has been
reached on the best way to train and assess models for this task. Casting video
understanding as a form of action or event categorization is problematic as it
is not fully clear what the semantic classes or abstractions in this domain
should be. Language has been exploited to sidestep the problem of defining
video categories, by formulating video understanding as the task of captioning
or description. However, language is highly complex, redundant and sometimes
ambiguous. Many different captions may express the same semantic concept. To
account for this ambiguity, quantitative evaluation of video description
requires sophisticated metrics, whose performance scores are typically hard to
interpret by humans.
  This paper provides four contributions to this problem. First, we formulate
Video Multiple Choice Caption (VideoMCC) as a new well-defined task with an
easy-to-interpret performance measure. Second, we describe a general
semi-automatic procedure to create benchmarks for this task. Third, we publicly
release a large-scale video benchmark created with an implementation of this
procedure and we include a human study that assesses human performance on our
dataset. Finally, we propose and test a varied collection of approaches on this
benchmark for the purpose of gaining a better understanding of the new
challenges posed by video comprehension.
Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementation
Authors: Min Liu, Tobi Delbruck
Date: 2017-06-16 00:00:00
Tasks: Motion Estimation, Optical Flow Estimation, Video Compression

Rapid and low power computation of optical flow (OF) is potentially useful in
robotics. The dynamic vision sensor (DVS) event camera produces quick and
sparse output, and has high dynamic range, but conventional OF algorithms are
frame-based and cannot be directly used with event-based cameras. Previous DVS
OF methods do not work well with dense textured input and are designed for
implementation in logic circuits. This paper proposes a new block-matching
based DVS OF algorithm which is inspired by motion estimation methods used for
MPEG video compression. The algorithm was implemented both in software and on
FPGA. For each event, it computes the motion direction as one of 9 directions.
The speed of the motion is set by the sample interval. Results show that the
Average Angular Error can be improved by 30\% compared with previous methods.
The OF can be calculated on FPGA with 50\,MHz clock in 0.2\,us per event (11
clock cycles), 20 times faster than a Java software implementation running on a
desktop PC. Sample data is shown that the method works on scenes dominated by
edges, sparse features, and dense texture.
Decoding Sentiment from Distributed Representations of Sentences
Authors: Edoardo Maria Ponti, Ivan Vulić, Anna Korhonen
Date: 2017-05-17 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Negation, Sentence

Distributed representations of sentences have been developed recently to
represent their meaning as real-valued vectors. However, it is not clear how
much information such representations retain about the polarity of sentences.
To study this question, we decode sentiment from unsupervised sentence
representations learned with different architectures (sensitive to the order of
words, the order of sentences, or none) in 9 typologically diverse languages.
Sentiment results from the (recursive) composition of lexical items and
grammatical strategies such as negation and concession. The results are
manifold: we show that there is no `one-size-fits-all' representation
architecture outperforming the others across the board. Rather, the top-ranking
architectures depend on the language and data at hand. Moreover, we find that
in several cases the additive composition model based on skip-gram word vectors
may surpass supervised state-of-art architectures such as bidirectional LSTMs.
Finally, we provide a possible explanation of the observed variation based on
the type of negative constructions in each language.
Local Feature Descriptor Learning with Adaptive Siamese Network
Authors: Chong Huang, Qiong Liu, Yan-Ying Chen, Kwang-Ting, Cheng
Date: 2017-06-16 00:00:00
Tasks: Patch Matching

Although the recent progress in the deep neural network has led to the
development of learnable local feature descriptors, there is no explicit answer
for estimation of the necessary size of a neural network. Specifically, the
local feature is represented in a low dimensional space, so the neural network
should have more compact structure. The small networks required for local
feature descriptor learning may be sensitive to initial conditions and learning
parameters and more likely to become trapped in local minima. In order to
address the above problem, we introduce an adaptive pruning Siamese
Architecture based on neuron activation to learn local feature descriptors,
making the network more computationally efficient with an improved recognition
rate over more complex networks. Our experiments demonstrate that our learned
local feature descriptors outperform the state-of-art methods in patch
matching.