pwc-archive/papers-with-abstracts huggingface dataset:
Complex Fully Convolutional Neural Networks for MR Image Reconstruction
Authors: Muneer Ahmad Dedmari, Sailesh Conjeti, Santiago Estrada, Phillip Ehses, Tony Stöcker, Martin Reuter
Date: 2018-07-09 00:00:00
Tasks: Image Reconstruction

Undersampling the k-space data is widely adopted for acceleration of Magnetic
Resonance Imaging (MRI). Current deep learning based approaches for supervised
learning of MRI image reconstruction employ real-valued operations and
representations by treating complex valued k-space/spatial-space as real
values. In this paper, we propose complex dense fully convolutional neural
network ($\mathbb{C}$DFNet) for learning to de-alias the reconstruction
artifacts within undersampled MRI images. We fashioned a densely-connected
fully convolutional block tailored for complex-valued inputs by introducing
dedicated layers such as complex convolution, batch normalization,
non-linearities etc. $\mathbb{C}$DFNet leverages the inherently complex-valued
nature of input k-space and learns richer representations. We demonstrate
improved perceptual quality and recovery of anatomical structures through
$\mathbb{C}$DFNet in contrast to its real-valued counterparts.
PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
Authors: Peng Tang, Xinggang Wang, Song Bai, Wei Shen, Xiang Bai, Wenyu Liu, Alan Yuille
Date: 2018-07-09 00:00:00
Tasks: image-classification, Multiple Instance Learning, Object, object-detection, Object Detection, Object Recognition, Weakly Supervised Object Detection

Weakly Supervised Object Detection (WSOD), using only image-level annotations
to train object detectors, is of growing importance in object recognition. In
this paper, we propose a novel deep network for WSOD. Unlike previous networks
that transfer the object detection problem to an image classification problem
using Multiple Instance Learning (MIL), our strategy generates proposal
clusters to learn refined instance classifiers by an iterative process. The
proposals in the same cluster are spatially adjacent and associated with the
same object. This prevents the network from concentrating too much on parts of
objects instead of whole objects. We first show that instances can be assigned
object or background labels directly based on proposal clusters for instance
classifier refinement, and then show that treating each cluster as a small new
bag yields fewer ambiguities than the directly assigning label method. The
iterative instance classifier refinement is implemented online using multiple
streams in convolutional neural networks, where the first is an MIL network and
the others are for instance classifier refinement supervised by the preceding
one. Experiments are conducted on the PASCAL VOC, ImageNet detection, and
MS-COCO benchmarks for WSOD. Results show that our method outperforms the
previous state of the art significantly.
Troubling Trends in Machine Learning Scholarship
Authors: Zachary C. Lipton, Jacob Steinhardt
Date: 2018-07-09 00:00:00
Tasks: BIG-bench Machine Learning

Collectively, machine learning (ML) researchers are engaged in the creation
and dissemination of knowledge about data-driven algorithms. In a given paper,
researchers might aspire to any subset of the following goals, among others: to
theoretically characterize what is learnable, to obtain understanding through
empirically rigorous experiments, or to build a working system that has high
predictive accuracy. While determining which knowledge warrants inquiry may be
subjective, once the topic is fixed, papers are most valuable to the community
when they act in service of the reader, creating foundational knowledge and
communicating as clearly as possible.
  Recent progress in machine learning comes despite frequent departures from
these ideals. In this paper, we focus on the following four patterns that
appear to us to be trending in ML scholarship: (i) failure to distinguish
between explanation and speculation; (ii) failure to identify the sources of
empirical gains, e.g., emphasizing unnecessary modifications to neural
architectures when gains actually stem from hyper-parameter tuning; (iii)
mathiness: the use of mathematics that obfuscates or impresses rather than
clarifies, e.g., by confusing technical and non-technical concepts; and (iv)
misuse of language, e.g., by choosing terms of art with colloquial connotations
or by overloading established technical terms.
  While the causes behind these patterns are uncertain, possibilities include
the rapid expansion of the community, the consequent thinness of the reviewer
pool, and the often-misaligned incentives between scholarship and short-term
measures of success (e.g., bibliometrics, attention, and entrepreneurial
opportunity). While each pattern offers a corresponding remedy (don't do it),
we also discuss some speculative suggestions for how the community might combat
these trends.
Adaptive Adversarial Attack on Scene Text Recognition
Authors: Xiaoyong Yuan, Pan He, Xiaolin Andy Li, Dapeng Oliver Wu
Date: 2018-07-09 00:00:00
Tasks: Adversarial Attack, image-classification, Image Classification, Object Detection, Scene Text Detection, Scene Text Recognition

Recent studies have shown that state-of-the-art deep learning models are vulnerable to the inputs with small perturbations (adversarial examples). We observe two critical obstacles in adversarial examples: (i) Strong adversarial attacks (e.g., C&W attack) require manually tuning hyper-parameters and take a long time to construct an adversarial example, making it impractical to attack real-time systems; (ii) Most of the studies focus on non-sequential tasks, such as image classification, yet only a few consider sequential tasks. In this work, we speed up adversarial attacks, especially on sequential learning tasks. By leveraging the uncertainty of each task, we directly learn the adaptive multi-task weightings, without manually searching hyper-parameters. A unified architecture is developed and evaluated for both non-sequential tasks and sequential ones. To validate the effectiveness, we take the scene text recognition task as a case study. To our best knowledge, our proposed method is the first attempt to adversarial attack for scene text recognition. Adaptive Attack achieves over 99.9\% success rate with 3-6X speedup compared to state-of-the-art adversarial attacks.
Spectral Analysis of Jet Substructure with Neural Networks: Boosted Higgs Case
Authors: Sung Hak Lim, Mihoko M. Nojiri
Date: 2018-07-09 00:00:00

Jets from boosted heavy particles have a typical angular scale which can be
used to distinguish them from QCD jets. We introduce a machine learning
strategy for jet substructure analysis using a spectral function on the angular
scale. The angular spectrum allows us to scan energy deposits over the angle
between a pair of particles in a highly visual way. We set up an artificial
neural network (ANN) to find out characteristic shapes of the spectra of the
jets from heavy particle decays. By taking the Higgs jets and QCD jets as
examples, we show that the ANN of the angular spectrum input has similar
performance to existing taggers. In addition, some improvement is seen when
additional extra radiations occur. Notably, the new algorithm automatically
combines the information of the multi-point correlations in the jet.
Foreign English Accent Adjustment by Learning Phonetic Patterns
Authors: Fedor Kitashov, Elizaveta Svitanko, Debojyoti Dutta
Date: 2018-07-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

State-of-the-art automatic speech recognition (ASR) systems struggle with the
lack of data for rare accents. For sufficiently large datasets, neural engines
tend to outshine statistical models in most natural language processing
problems. However, a speech accent remains a challenge for both approaches.
Phonologists manually create general rules describing a speaker's accent, but
their results remain underutilized. In this paper, we propose a model that
automatically retrieves phonological generalizations from a small dataset. This
method leverages the difference in pronunciation between a particular dialect
and General American English (GAE) and creates new accented samples of words.
The proposed model is able to learn all generalizations that previously were
manually obtained by phonologists. We use this statistical method to generate a
million phonological variations of words from the CMU Pronouncing Dictionary
and train a sequence-to-sequence RNN to recognize accented words with 59%
accuracy.
Optimization of a SSP's Header Bidding Strategy using Thompson Sampling
Authors: Grégoire Jauvion, Nicolas Grislain, Pascal Sielenou Dkengne, Aurélien Garivier, Sébastien Gerchinovitz
Date: 2018-07-09 00:00:00
Tasks: Thompson Sampling

Over the last decade, digital media (web or app publishers) generalized the
use of real time ad auctions to sell their ad spaces. Multiple auction
platforms, also called Supply-Side Platforms (SSP), were created. Because of
this multiplicity, publishers started to create competition between SSPs. In
this setting, there are two successive auctions: a second price auction in each
SSP and a secondary, first price auction, called header bidding auction,
between SSPs.In this paper, we consider an SSP competing with other SSPs for ad
spaces. The SSP acts as an intermediary between an advertiser wanting to buy ad
spaces and a web publisher wanting to sell its ad spaces, and needs to define a
bidding strategy to be able to deliver to the advertisers as many ads as
possible while spending as little as possible. The revenue optimization of this
SSP can be written as a contextual bandit problem, where the context consists
of the information available about the ad opportunity, such as properties of
the internet user or of the ad placement.Using classical multi-armed bandit
strategies (such as the original versions of UCB and EXP3) is inefficient in
this setting and yields a low convergence speed, as the arms are very
correlated. In this paper we design and experiment a version of the Thompson
Sampling algorithm that easily takes this correlation into account. We combine
this bayesian algorithm with a particle filter, which permits to handle
non-stationarity by sequentially estimating the distribution of the highest bid
to beat in order to win an auction. We apply this methodology on two real
auction datasets, and show that it significantly outperforms more classical
approaches.The strategy defined in this paper is being developed to be deployed
on thousands of publishers worldwide.
Video Captioning with Boundary-aware Hierarchical Language Decoding and Joint Video Prediction
Authors: Xiangxi Shi, Jianfei Cai, Jiuxiang Gu, Shafiq Joty
Date: 2018-07-08 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, Sentence, Text Generation, Video Captioning, Video Prediction

The explosion of video data on the internet requires effective and efficient
technology to generate captions automatically for people who are not able to
watch the videos. Despite the great progress of video captioning research,
particularly on video feature encoding, the language decoder is still largely
based on the prevailing RNN decoder such as LSTM, which tends to prefer the
frequent word that aligns with the video. In this paper, we propose a
boundary-aware hierarchical language decoder for video captioning, which
consists of a high-level GRU based language decoder, working as a global
(caption-level) language model, and a low-level GRU based language decoder,
working as a local (phrase-level) language model. Most importantly, we
introduce a binary gate into the low-level GRU language decoder to detect the
language boundaries. Together with other advanced components including joint
video prediction, shared soft attention, and boundary-aware video encoding, our
integrated video captioning framework can discover hierarchical language
information and distinguish the subject and the object in a sentence, which are
usually confusing during the language generation. Extensive experiments on two
widely-used video captioning datasets, MSR-Video-to-Text (MSR-VTT)
\cite{xu2016msr} and YouTube-to-Text (MSVD) \cite{chen2011collecting} show that
our method is highly competitive, compared with the state-of-the-art methods.
Interpretable Patient Mortality Prediction with Multi-value Rule Sets
Authors: Tong Wang, Veerajalandhar Allareddy, Sankeerth Rampa, Veerasathpurush Allareddy
Date: 2018-07-06 00:00:00
Tasks: Form, Mortality Prediction, Prediction

We propose a Multi-vAlue Rule Set (MRS) model for in-hospital predicting
patient mortality. Compared to rule sets built from single-valued rules, MRS
adopts a more generalized form of association rules that allows multiple values
in a condition. Rules of this form are more concise than classical
single-valued rules in capturing and describing patterns in data. Our
formulation also pursues a higher efficiency of feature utilization, which
reduces possible cost in data collection and storage. We propose a Bayesian
framework for formulating a MRS model and propose an efficient inference method
for learning a maximum \emph{a posteriori}, incorporating theoretically
grounded bounds to iteratively reduce the search space and improve the search
efficiency. Experiments show that our model was able to achieve better
performance than baseline method including the current system used by the
hospital.
Navigating Diverse Data Science Learning: Critical Reflections Towards Future Practice
Authors: Yehia Elkhatib
Date: 2018-07-05 00:00:00

Data Science is currently a popular field of science attracting expertise from very diverse backgrounds. Current learning practices need to acknowledge this and adapt to it. This paper summarises some experiences relating to such learning approaches from teaching a postgraduate Data Science module, and draws some learned lessons that are of relevance to others teaching Data Science.
Sparse Representation and Non-Negative Matrix Factorization for image denoise
Authors: R. M. Farouk, M. E. Abd El-aziz, A. M. Adam
Date: 2018-07-05 00:00:00
Tasks: Denoising, Dictionary Learning, Image Denoising

Recently, the problem of blind image separation has been widely investigated,
especially the medical image denoise which is the main step in medical
diag-nosis. Removing the noise without affecting relevant features of the image
is the main goal. Sparse decomposition over redundant dictionaries become of
the most used approaches to solve this problem. NMF codes naturally favor
sparse, parts-based representations. In sparse representation, signals
represented as a linear combination of a redundant dictionary atoms. In this
paper, we propose an algorithm based on sparse representation over the
redundant dictionary and Non-Negative Matrix Factorization (N-NMF). The
algorithm initializes a dic-tionary based on training samples constructed from
noised image, then it searches for the best representation for the source by
using the approximate matching pursuit (AMP). The proposed N-NMF gives a better
reconstruction of an image from denoised one. We have compared our numerical
results with different image denoising techniques and we have found the
performance of the proposed technique is promising. Keywords: Image denoising,
sparse representation, dictionary learning, matching pursuit, non-negative
matrix factorization.
Estimating sample-specific regulatory networks
Authors: Marieke Lydia Kuijjer, Matthew Tung, Guo-Cheng Yuan, John Quackenbush, Kimberly Glass
Date: 2015-05-24 00:00:00

Biological systems are driven by intricate interactions among the complex
array of molecules that comprise the cell. Many methods have been developed to
reconstruct network models of those interactions. These methods often draw on
large numbers of samples with measured gene expression profiles to infer
connections between genes (or gene products). The result is an aggregate
network model representing a single estimate for the likelihood of each
interaction, or "edge," in the network. While informative, aggregate models
fail to capture the heterogeneity that is represented in any population. Here
we propose a method to reverse engineer sample-specific networks from aggregate
network models. We demonstrate the accuracy and applicability of our approach
in several data sets, including simulated data, microarray expression data from
synchronized yeast cells, and RNA-seq data collected from human lymphoblastoid
cell lines. We show that these sample-specific networks can be used to study
changes in network topology across time and to characterize shifts in gene
regulation that may not be apparent in expression data. We believe the ability
to generate sample-specific networks will greatly facilitate the application of
network methods to the increasingly large, complex, and heterogeneous
multi-omic data sets that are currently being generated, and ultimately support
the emerging field of precision network medicine.
Phase Retrieval Under a Generative Prior
Authors: Paul Hand, Oscar Leong, Vladislav Voroninski
Date: 2018-07-11 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Retrieval

The phase retrieval problem asks to recover a natural signal $y_0 \in
\mathbb{R}^n$ from $m$ quadratic observations, where $m$ is to be minimized. As
is common in many imaging problems, natural signals are considered sparse with
respect to a known basis, and the generic sparsity prior is enforced via
$\ell_1$ regularization. While successful in the realm of linear inverse
problems, such $\ell_1$ methods have encountered possibly fundamental
limitations, as no computationally efficient algorithm for phase retrieval of a
$k$-sparse signal has been proven to succeed with fewer than $O(k^2\log n)$
generic measurements, exceeding the theoretical optimum of $O(k \log n)$. In
this paper, we propose a novel framework for phase retrieval by 1) modeling
natural signals as being in the range of a deep generative neural network $G :
\mathbb{R}^k \rightarrow \mathbb{R}^n$ and 2) enforcing this prior directly by
optimizing an empirical risk objective over the domain of the generator. Our
formulation has provably favorable global geometry for gradient methods, as
soon as $m = O(kd^2\log n)$, where $d$ is the depth of the network.
Specifically, when suitable deterministic conditions on the generator and
measurement matrix are met, we construct a descent direction for any point
outside of a small neighborhood around the unique global minimizer and its
negative multiple, and show that such conditions hold with high probability
under Gaussian ensembles of multilayer fully-connected generator networks and
measurement matrices. This formulation for structured phase retrieval thus has
two advantages over sparsity based methods: 1) deep generative priors can more
tightly represent natural signals and 2) information theoretically optimal
sample complexity. We corroborate these results with experiments showing that
exploiting generative models in phase retrieval tasks outperforms sparse phase
retrieval methods.
Last-Iterate Convergence: Zero-Sum Games and Constrained Min-Max Optimization
Authors: Constantinos Daskalakis, Ioannis Panageas
Date: 2018-07-11 00:00:00
Tasks: Open-Ended Question Answering

Motivated by applications in Game Theory, Optimization, and Generative Adversarial Networks, recent work of Daskalakis et al \cite{DISZ17} and follow-up work of Liang and Stokes \cite{LiangS18} have established that a variant of the widely used Gradient Descent/Ascent procedure, called "Optimistic Gradient Descent/Ascent (OGDA)", exhibits last-iterate convergence to saddle points in {\em unconstrained} convex-concave min-max optimization problems. We show that the same holds true in the more general problem of {\em constrained} min-max optimization under a variant of the no-regret Multiplicative-Weights-Update method called "Optimistic Multiplicative-Weights Update (OMWU)". This answers an open question of Syrgkanis et al \cite{SALS15}. The proof of our result requires fundamentally different techniques from those that exist in no-regret learning literature and the aforementioned papers. We show that OMWU monotonically improves the Kullback-Leibler divergence of the current iterate to the (appropriately normalized) min-max solution until it enters a neighborhood of the solution. Inside that neighborhood we show that OMWU is locally (asymptotically) stable converging to the exact solution. We believe that our techniques will be useful in the analysis of the last iterate of other learning algorithms.
Estimating Cellular Goals from High-Dimensional Biological Data
Authors: Laurence Yang, Michael A. Saunders, Jean-Christophe Lachance, Bernhard O. Palsson, José Bento
Date: 2018-07-11 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Optimization-based models have been used to predict cellular behavior for over 25 years. The constraints in these models are derived from genome annotations, measured macro-molecular composition of cells, and by measuring the cell's growth rate and metabolism in different conditions. The cellular goal (the optimization problem that the cell is trying to solve) can be challenging to derive experimentally for many organisms, including human or mammalian cells, which have complex metabolic capabilities and are not well understood. Existing approaches to learning goals from data include (a) estimating a linear objective function, or (b) estimating linear constraints that model complex biochemical reactions and constrain the cell's operation. The latter approach is important because often the known/observed biochemical reactions are not enough to explain observations, and hence there is a need to extend automatically the model complexity by learning new chemical reactions. However, this leads to nonconvex optimization problems, and existing tools cannot scale to realistically large metabolic models. Hence, constraint estimation is still used sparingly despite its benefits for modeling cell metabolism, which is important for developing novel antimicrobials against pathogens, discovering cancer drug targets, and producing value-added chemicals. Here, we develop the first approach to estimating constraint reactions from data that can scale to realistically large metabolic models. Previous tools have been used on problems having less than 75 biochemical reactions and 60 metabolites, which limits real-life-size applications. We perform extensive experiments using 75 large-scale metabolic network models for different organisms (including bacteria, yeasts, and mammals) and show that our algorithm can recover cellular constraint reactions, even when some measurements are missing.
DeepMove: Learning Place Representations through Large Scale Movement Data
Authors: Yang Zhou, Yan Huang
Date: 2018-07-11 00:00:00
Tasks: Clustering

Understanding and reasoning about places and their relationships are critical
for many applications. Places are traditionally curated by a small group of
people as place gazetteers and are represented by an ID with spatial extent,
category, and other descriptions. However, a place context is described to a
large extent by movements made from/to other places. Places are linked and
related to each other by these movements. This important context is missing
from the traditional representation.
  We present DeepMove, a novel approach for learning latent representations of
places. DeepMove advances the current deep learning based place representations
by directly model movements between places. We demonstrate DeepMove's latent
representations on place categorization and clustering tasks on large place and
movement datasets with respect to important parameters. Our results show that
DeepMove outperforms state-of-the-art baselines. DeepMove's representations can
provide up to 15% higher than competing methods in matching rate of place
category and result in up to 39% higher silhouette coefficient value for place
clusters.
  DeepMove is spatial and temporal context aware. It is scalable. It
outperforms competing models using much smaller training dataset (a month or
1/12 of data). These qualities make it suitable for a broad class of real-world
applications.