pwc-archive/papers-with-abstracts huggingface dataset:
Deep learning for extracting protein-protein interactions from biomedical literature
Authors: Yifan Peng, Zhiyong Lu
Date: 2017-06-05 00:00:00
Proceeding: WS 2017 8
Tasks: Benchmarking, Cross-corpus, Deep Learning, Open-Ended Question Answering, Sentence

State-of-the-art methods for protein-protein interaction (PPI) extraction are
primarily feature-based or kernel-based by leveraging lexical and syntactic
information. But how to incorporate such knowledge in the recent deep learning
methods remains an open question. In this paper, we propose a multichannel
dependency-based convolutional neural network model (McDepCNN). It applies one
channel to the embedding vector of each word in the sentence, and another
channel to the embedding vector of the head of the corresponding word.
Therefore, the model can use richer information obtained from different
channels. Experiments on two public benchmarking datasets, AIMed and BioInfer,
demonstrate that McDepCNN compares favorably to the state-of-the-art
rich-feature and single-kernel based methods. In addition, McDepCNN achieves
24.4% relative improvement in F1-score over the state-of-the-art methods on
cross-corpus evaluation and 12% improvement in F1-score over kernel-based
methods on "difficult" instances. These results suggest that McDepCNN
generalizes more easily over different corpora, and is capable of capturing
long distance features in the sentences.
Negative Results in Computer Vision: A Perspective
Authors: Ali Borji
Date: 2017-05-11 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Experimental Design, Two-sample testing

A negative result is when the outcome of an experiment or a model is not what
is expected or when a hypothesis does not hold. Despite being often overlooked
in the scientific community, negative results are results and they carry value.
While this topic has been extensively discussed in other fields such as social
sciences and biosciences, less attention has been paid to it in the computer
vision community. The unique characteristics of computer vision, particularly
its experimental aspect, call for a special treatment of this matter. In this
paper, I will address what makes negative results important, how they should be
disseminated and incentivized, and what lessons can be learned from cognitive
vision research in this regard. Further, I will discuss issues such as computer
vision and human vision interaction, experimental design and statistical
hypothesis testing, explanatory versus predictive modeling, performance
evaluation, model comparison, as well as computer vision research culture.
On the Quantitative Analysis of Decoder-Based Generative Models
Authors: Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger Grosse
Date: 2016-11-14 00:00:00
Tasks: Decoder

The past several years have seen remarkable progress in generative models
which produce convincing samples of images and other modalities. A shared
component of many powerful generative models is a decoder network, a parametric
deep neural net that defines a generative distribution. Examples include
variational autoencoders, generative adversarial networks, and generative
moment matching networks. Unfortunately, it can be difficult to quantify the
performance of these models because of the intractability of log-likelihood
estimation, and inspecting samples can be misleading. We propose to use
Annealed Importance Sampling for evaluating log-likelihoods for decoder-based
models and validate its accuracy using bidirectional Monte Carlo. The
evaluation code is provided at https://github.com/tonywu95/eval_gen. Using this
technique, we analyze the performance of decoder-based models, the
effectiveness of existing log-likelihood estimators, the degree of overfitting,
and the degree to which these models miss important modes of the data
distribution.
Optimal Quantum Sample Complexity of Learning Algorithms
Authors: Srinivasan Arunachalam, Ronald de Wolf
Date: 2016-07-04 00:00:00
Tasks: Learning Theory

$ \newcommand{\eps}{\varepsilon} $In learning theory, the VC dimension of a
concept class $C$ is the most common way to measure its "richness." In the PAC
model $$ \Theta\Big(\frac{d}{\eps} + \frac{\log(1/\delta)}{\eps}\Big) $$
examples are necessary and sufficient for a learner to output, with probability
$1-\delta$, a hypothesis $h$ that is $\eps$-close to the target concept $c$. In
the related agnostic model, where the samples need not come from a $c\in C$, we
know that $$ \Theta\Big(\frac{d}{\eps^2} + \frac{\log(1/\delta)}{\eps^2}\Big)
$$ examples are necessary and sufficient to output an hypothesis $h\in C$ whose
error is at most $\eps$ worse than the best concept in $C$.
  Here we analyze quantum sample complexity, where each example is a coherent
quantum state. This model was introduced by Bshouty and Jackson, who showed
that quantum examples are more powerful than classical examples in some
fixed-distribution settings. However, Atici and Servedio, improved by Zhang,
showed that in the PAC setting, quantum examples cannot be much more powerful:
the required number of quantum examples is $$
\Omega\Big(\frac{d^{1-\eta}}{\eps} + d + \frac{\log(1/\delta)}{\eps}\Big)\mbox{
for all }\eta> 0. $$ Our main result is that quantum and classical sample
complexity are in fact equal up to constant factors in both the PAC and
agnostic models. We give two approaches. The first is a fairly simple
information-theoretic argument that yields the above two classical bounds and
yields the same bounds for quantum sample complexity up to a $\log(d/\eps)$
factor. We then give a second approach that avoids the log-factor loss, based
on analyzing the behavior of the "Pretty Good Measurement" on the quantum state
identification problems that correspond to learning. This shows classical and
quantum sample complexity are equal up to constant factors.
Feature Encoding in Band-limited Distributed Surveillance Systems
Authors: Alireza Rahimpour, Ali Taalimi, Hairong Qi
Date: 2016-12-19 00:00:00

Distributed surveillance systems have become popular in recent years due to
security concerns. However, transmitting high dimensional data in
bandwidth-limited distributed systems becomes a major challenge. In this paper,
we address this issue by proposing a novel probabilistic algorithm based on the
divergence between the probability distributions of the visual features in
order to reduce their dimensionality and thus save the network bandwidth in
distributed wireless smart camera networks. We demonstrate the effectiveness of
the proposed approach through extensive experiments on two surveillance
recognition tasks.
StreetStyle: Exploring world-wide clothing styles from millions of photos
Authors: Kevin Matzen, Kavita Bala, Noah Snavely
Date: 2017-06-06 00:00:00
Tasks: Attribute

Each day billions of photographs are uploaded to photo-sharing services and
social media platforms. These images are packed with information about how
people live around the world. In this paper we exploit this rich trove of data
to understand fashion and style trends worldwide. We present a framework for
visual discovery at scale, analyzing clothing and fashion across millions of
images of people around the world and spanning several years. We introduce a
large-scale dataset of photos of people annotated with clothing attributes, and
use this dataset to train attribute classifiers via deep learning. We also
present a method for discovering visually consistent style clusters that
capture useful visual correlations in this massive dataset. Using these tools,
we analyze millions of photos to derive visual insight, producing a
first-of-its-kind analysis of global and per-city fashion choices and
spatio-temporal trends.
Estimating Shape Parameters of Piecewise Linear-Quadratic Problems
Authors: Peng Zheng, Aleksandr Y. Aravkin, Karthikeyan Natesan Ramamurthy
Date: 2017-06-06 00:00:00

Piecewise Linear-Quadratic (PLQ) penalties are widely used to develop models in statistical inference, signal processing, and machine learning. Common examples of PLQ penalties include least squares, Huber, Vapnik, 1-norm, and their asymmetric generalizations. Properties of these estimators depend on the choice of penalty and its shape parameters, such as degree of asymmetry for the quantile loss, and transition point between linear and quadratic pieces for the Huber function. In this paper, we develop a statistical framework that can help the modeler to automatically tune shape parameters once the shape of the penalty has been chosen. The choice of the parameter is informed by the basic notion that each QS penalty should correspond to a true statistical density. The normalization constant inherent in this requirement helps to inform the optimization over shape parameters, giving a joint optimization problem over these as well as primary parameters of interest. A second contribution is to consider optimization methods for these joint problems. We show that basic first-order methods can be immediately brought to bear, and design specialized extensions of interior point (IP) methods for PLQ problems that can quickly and efficiently solve the joint problem. Synthetic problems and larger-scale practical examples illustrate the potential of the approach.
Marmara Turkish Coreference Corpus and Coreference Resolution Baseline
Authors: Peter Schüller, Kübra Cıngıllı, Ferit Tunçer, Barış Gün Sürmeli, Ayşegül Pekel, Ayşe Hande Karatay, Hacer Ezgi Karakaş
Date: 2017-06-06 00:00:00
Tasks: coreference-resolution, Coreference Resolution

We describe the Marmara Turkish Coreference Corpus, which is an annotation of
the whole METU-Sabanci Turkish Treebank with mentions and coreference chains.
Collecting eight or more independent annotations for each document allowed for
fully automatic adjudication. We provide a baseline system for Turkish mention
detection and coreference resolution and evaluate it on the corpus.
Attributed Network Embedding for Learning in a Dynamic Environment
Authors: Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu
Date: 2017-06-06 00:00:00
Tasks: Attribute, Clustering, Link Prediction, Network Embedding, Node Classification

Network embedding leverages the node proximity manifested to learn a
low-dimensional node vector representation for each node in the network. The
learned embeddings could advance various learning tasks such as node
classification, network clustering, and link prediction. Most, if not all, of
the existing works, are overwhelmingly performed in the context of plain and
static networks. Nonetheless, in reality, network structure often evolves over
time with addition/deletion of links and nodes. Also, a vast majority of
real-world networks are associated with a rich set of node attributes, and
their attribute values are also naturally changing, with the emerging of new
content patterns and the fading of old content patterns. These changing
characteristics motivate us to seek an effective embedding representation to
capture network and attribute evolving patterns, which is of fundamental
importance for learning in a dynamic environment. To our best knowledge, we are
the first to tackle this problem with the following two challenges: (1) the
inherently correlated network and node attributes could be noisy and
incomplete, it necessitates a robust consensus representation to capture their
individual properties and correlations; (2) the embedding learning needs to be
performed in an online fashion to adapt to the changes accordingly. In this
paper, we tackle this problem by proposing a novel dynamic attributed network
embedding framework - DANE. In particular, DANE first provides an offline
method for a consensus embedding and then leverages matrix perturbation theory
to maintain the freshness of the end embedding results in an online manner. We
perform extensive experiments on both synthetic and real attributed networks to
corroborate the effectiveness and efficiency of the proposed framework.
Added value of morphological features to breast lesion diagnosis in ultrasound
Authors: Michał Byra, Katarzyna Dobruch-Sobczak, Hanna Piotrzkowska-Wróblewska, Andrzej Nowicki
Date: 2017-06-06 00:00:00
Tasks: Breast Cancer Detection, Classification, Diagnostic, feature selection, General Classification, Lesion Classification

Ultrasound imaging plays an important role in breast lesion differentiation.
However, diagnostic accuracy depends on ultrasonographer experience. Various
computer aided diagnosis systems has been developed to improve breast cancer
detection and reduce the number of unnecessary biopsies. In this study, our aim
was to improve breast lesion classification based on the BI-RADS (Breast
Imaging - Reporting and Data System). This was accomplished by combining the
BI-RADS with morphological features which assess lesion boundary. A dataset of
214 lesion images was used for analysis. 30 morphological features were
extracted and feature selection scheme was applied to find features which
improve the BI-RADS classification performance. Additionally, the best
performing morphological feature subset was indicated. We obtained a better
classification by combining the BI-RADS with six morphological features. These
features were the extent, overlap ratio, NRL entropy, circularity,
elliptic-normalized circumference and the normalized residual value. The area
under the receiver operating curve calculated with the use of the combined
classifier was 0.986. The best performing morphological feature subset
contained six features: the DWR, NRL entropy, normalized residual value,
overlap ratio, extent and the morphological closing ratio. For this set, the
area under the curve was 0.901. The combination of the radiologist's experience
related to the BI-RADS and the morphological features leads to a more effective
breast lesion classification.
Deep Diagnostics: Applying Convolutional Neural Networks for Vessels Defects Detection
Authors: Stanislav Filippov, Arsenii Moiseev, Andronenko Andrey
Date: 2017-05-17 00:00:00

Coronary angiography is considered to be a safe tool for the evaluation of
coronary artery disease and perform in approximately 12 million patients each
year worldwide. [1] In most cases, angiograms are manually analyzed by a
cardiologist. Actually, there are no clinical practice algorithms which could
improve and automate this work. Neural networks show high efficiency in tasks
of image analysis and they can be used for the analysis of angiograms and
facilitate diagnostics. We have developed an algorithm based on Convolutional
Neural Network and Neural Network U-Net [2] for vessels segmentation and
defects detection such as stenosis. For our research we used anonymized
angiography data obtained from one of the city's hospitals and augmented them
to improve learning efficiency. U-Net usage provided high quality segmentation
and the combination of our algorithm with an ensemble of classifiers shows a
good accuracy in the task of ischemia evaluation on test data. Subsequently,
this approach can be served as a basis for the creation of an analytical system
that could speed up the diagnosis of cardiovascular diseases and greatly
facilitate the work of a specialist.
Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext
Authors: John Wieting, Jonathan Mallinson, Kevin Gimpel
Date: 2017-06-06 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Machine Translation, Sentence, Sentence Embeddings, Translation

We consider the problem of learning general-purpose, paraphrastic sentence
embeddings in the setting of Wieting et al. (2016b). We use neural machine
translation to generate sentential paraphrases via back-translation of
bilingual sentence pairs. We evaluate the paraphrase pairs by their ability to
serve as training data for learning paraphrastic sentence embeddings. We find
that the data quality is stronger than prior work based on bitext and on par
with manually-written English paraphrase pairs, with the advantage that our
approach can scale up to generate large training sets for many languages and
domains. We experiment with several language pairs and data sources, and
develop a variety of data filtering techniques. In the process, we explore how
neural machine translation output differs from human-written sentences, finding
clear differences in length, the amount of repetition, and the use of rare
words.
Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks
Authors: Lawrence Phillips, Nathan Hodas
Date: 2017-06-06 00:00:00
Tasks: Deep Learning, Language Acquisition

Increasingly, cognitive scientists have demonstrated interest in applying
tools from deep learning. One use for deep learning is in language acquisition
where it is useful to know if a linguistic phenomenon can be learned through
domain-general means. To assess whether unsupervised deep learning is
appropriate, we first pose a smaller question: Can unsupervised neural networks
apply linguistic rules productively, using them in novel situations? We draw
from the literature on determiner/noun productivity by training an
unsupervised, autoencoder network measuring its ability to combine nouns with
determiners. Our simple autoencoder creates combinations it has not previously
encountered and produces a degree of overlap matching adults. While this
preliminary work does not provide conclusive evidence for productivity, it
warrants further investigation with more complex models. Further, this work
helps lay the foundations for future collaboration between the deep learning
and cognitive science communities.
Information Bottleneck in Control Tasks with Recurrent Spiking Neural Networks
Authors: Madhavun Candadai Vasu, Eduardo Izquierdo
Date: 2017-06-06 00:00:00

The nervous system encodes continuous information from the environment in the
form of discrete spikes, and then decodes these to produce smooth motor
actions. Understanding how spikes integrate, represent, and process information
to produce behavior is one of the greatest challenges in neuroscience.
Information theory has the potential to help us address this challenge.
Informational analyses of deep and feed-forward artificial neural networks
solving static input-output tasks, have led to the proposal of the
\emph{Information Bottleneck} principle, which states that deeper layers encode
more relevant yet minimal information about the inputs. Such an analyses on
networks that are recurrent, spiking, and perform control tasks is relatively
unexplored. Here, we present results from a Mutual Information analysis of a
recurrent spiking neural network that was evolved to perform the classic
pole-balancing task. Our results show that these networks deviate from the
\emph{Information Bottleneck} principle prescribed for feed-forward networks.
Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning
Authors: Peter Sadowski, Balint Radics, Ananya, Yasunori Yamazaki, Pierre Baldi
Date: 2017-06-06 00:00:00
Tasks: Deep Learning

Antihydrogen is at the forefront of antimatter research at the CERN
Antiproton Decelerator. Experiments aiming to test the fundamental CPT symmetry
and antigravity effects require the efficient detection of antihydrogen
annihilation events, which is performed using highly granular tracking
detectors installed around an antimatter trap. Improving the efficiency of the
antihydrogen annihilation detection plays a central role in the final
sensitivity of the experiments. We propose deep learning as a novel technique
to analyze antihydrogen annihilation data, and compare its performance with a
traditional track and vertex reconstruction method. We report that the deep
learning approach yields significant improvement, tripling event coverage while
simultaneously improving performance by over 5% in terms of Area Under Curve
(AUC).
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space
Authors: José Miguel Hernández-Lobato, James Requeima, Edward O. Pyzer-Knapp, Alán Aspuru-Guzik
Date: 2017-06-06 00:00:00
Proceeding: ICML 2017 8
Tasks: Bayesian Optimization, Thompson Sampling

Chemical space is so large that brute force searches for new interesting
molecules are infeasible. High-throughput virtual screening via computer
cluster simulations can speed up the discovery process by collecting very large
amounts of data in parallel, e.g., up to hundreds or thousands of parallel
measurements. Bayesian optimization (BO) can produce additional acceleration by
sequentially identifying the most useful simulations or experiments to be
performed next. However, current BO methods cannot scale to the large numbers
of parallel measurements and the massive libraries of molecules currently used
in high-throughput screening. Here, we propose a scalable solution based on a
parallel and distributed implementation of Thompson sampling (PDTS). We show
that, in small scale problems, PDTS performs similarly as parallel expected
improvement (EI), a batch version of the most widely used BO heuristic.
Additionally, in settings where parallel EI does not scale, PDTS outperforms
other scalable baselines such as a greedy search, $\epsilon$-greedy approaches
and a random search method. These results show that PDTS is a successful
solution for large-scale parallel BO.
Robust Online Multi-Task Learning with Correlative and Personalized Structures
Authors: Peng Yang, Peilin Zhao, Xin Gao
Date: 2017-06-06 00:00:00
Tasks: Multi-Task Learning

Multi-Task Learning (MTL) can enhance a classifier's generalization
performance by learning multiple related tasks simultaneously. Conventional MTL
works under the offline or batch setting, and suffers from expensive training
cost and poor scalability. To address such inefficiency issues, online learning
techniques have been applied to solve MTL problems. However, most existing
algorithms of online MTL constrain task relatedness into a presumed structure
via a single weight matrix, which is a strict restriction that does not always
hold in practice. In this paper, we propose a robust online MTL framework that
overcomes this restriction by decomposing the weight matrix into two
components: the first one captures the low-rank common structure among tasks
via a nuclear norm and the second one identifies the personalized patterns of
outlier tasks via a group lasso. Theoretical analysis shows the proposed
algorithm can achieve a sub-linear regret with respect to the best linear model
in hindsight. Even though the above framework achieves good performance, the
nuclear norm that simply adds all nonzero singular values together may not be a
good low-rank approximation. To improve the results, we use a log-determinant
function as a non-convex rank approximation. The gradient scheme is applied to
optimize log-determinant function and can obtain a closed-form solution for
this refined problem. Experimental results on a number of real-world
applications verify the efficacy of our method.
Face Alignment Using K-Cluster Regression Forests With Weighted Splitting
Authors: Marek Kowalski, Jacek Naruniec
Date: 2017-06-06 00:00:00
Tasks: Face Alignment, regression

In this work we present a face alignment pipeline based on two novel methods:
weighted splitting for K-cluster Regression Forests and 3D Affine Pose
Regression for face shape initialization. Our face alignment method is based on
the Local Binary Feature framework, where instead of standard regression
forests and pixel difference features used in the original method, we use our
K-cluster Regression Forests with Weighted Splitting (KRFWS) and Pyramid HOG
features. We also use KRFWS to perform Affine Pose Regression (APR) and
3D-Affine Pose Regression (3D-APR), which intend to improve the face shape
initialization. APR applies a rigid 2D transform to the initial face shape that
compensates for inaccuracy in the initial face location, size and in-plane
rotation. 3D-APR estimates the parameters of a 3D transform that additionally
compensates for out-of-plane rotation. The resulting pipeline, consisting of
APR and 3D-APR followed by face alignment, shows an improvement of 20% over
standard LBF on the challenging IBUG dataset, and state-of-theart accuracy on
the entire 300-W dataset.