pwc-archive/papers-with-abstracts huggingface dataset:
Bimodal network architectures for automatic generation of image annotation from text
Authors: Mehdi Moradi, Ali Madani, Yaniv Gur, Yufan Guo, Tanveer Syeda-Mahmood
Date: 2018-09-05 00:00:00
Tasks: Medical Image Analysis

Medical image analysis practitioners have embraced big data methodologies.
This has created a need for large annotated datasets. The source of big data is
typically large image collections and clinical reports recorded for these
images. In many cases, however, building algorithms aimed at segmentation and
detection of disease requires a training dataset with markings of the areas of
interest on the image that match with the described anomalies. This process of
annotation is expensive and needs the involvement of clinicians. In this work
we propose two separate deep neural network architectures for automatic marking
of a region of interest (ROI) on the image best representing a finding
location, given a textual report or a set of keywords. One architecture
consists of LSTM and CNN components and is trained end to end with images,
matching text, and markings of ROIs for those images. The output layer
estimates the coordinates of the vertices of a polygonal region. The second
architecture uses a network pre-trained on a large dataset of the same image
types for learning feature representations of the findings of interest. We show
that for a variety of findings from chest X-ray images, both proposed
architectures learn to estimate the ROI, as validated by clinical annotations.
There is a clear advantage obtained from the architecture with pre-trained
imaging network. The centroids of the ROIs marked by this network were on
average at a distance equivalent to 5.1% of the image width from the centroids
of the ground truth ROIs.
Anomaly Detection in the Presence of Missing Values
Authors: Thomas G. Dietterich, Tadesse Zemicheal
Date: 2018-09-05 00:00:00
Tasks: Anomaly Detection, Imputation, Missing Values

Standard methods for anomaly detection assume that all features are observed
at both learning time and prediction time. Such methods cannot process data
containing missing values. This paper studies five strategies for handling
missing values in test queries: (a) mean imputation, (b) MAP imputation, (c)
reduction (reduced-dimension anomaly detectors via feature bagging), (d)
marginalization (for density estimators only), and (e) proportional
distribution (for tree-based methods only). Our analysis suggests that MAP
imputation and proportional distribution should give better results than mean
imputation, reduction, and marginalization. These hypotheses are largely
confirmed by experimental studies on synthetic data and on anomaly detection
benchmark data sets using the Isolation Forest (IF), LODA, and EGMM anomaly
detection algorithms. However, marginalization worked surprisingly well for
EGMM, and there are exceptions where reduction works well on some benchmark
problems. We recommend proportional distribution for IF, MAP imputation for
LODA, and marginalization for EGMM.
Merging datasets through deep learning
Authors: Kavitha Srinivas, Abraham Gale, Julian Dolby
Date: 2018-09-05 00:00:00
Tasks: Deep Learning, Management, Metric Learning

Merging datasets is a key operation for data analytics. A frequent
requirement for merging is joining across columns that have different surface
forms for the same entity (e.g., the name of a person might be represented as
"Douglas Adams" or "Adams, Douglas"). Similarly, ontology alignment can require
recognizing distinct surface forms of the same entity, especially when
ontologies are independently developed. However, data management systems are
currently limited to performing merges based on string equality, or at best
using string similarity. We propose an approach to performing merges based on
deep learning models. Our approach depends on (a) creating a deep learning
model that maps surface forms of an entity into a set of vectors such that
alternate forms for the same entity are closest in vector space, (b) indexing
these vectors using a nearest neighbors algorithm to find the forms that can be
potentially joined together. To build these models, we had to adapt techniques
from metric learning due to the characteristics of the data; specifically we
describe novel sample selection techniques and loss functions that work for
this problem. To evaluate our approach, we used Wikidata as ground truth and
built models from datasets with approximately 1.1M people's names (200K
identities) and 130K company names (70K identities). We developed models that
allow for joins with precision@1 of .75-.81 and recall of .74-.81. We make the
models available for aligning people or companies across multiple datasets.
A Bayesian framework for the analog reconstruction of kymographs from fluorescence microscopy data
Authors: Denis K. Samuylov, Gábor Székely, Grégory Paul
Date: 2018-09-05 00:00:00

Kymographs are widely used to represent and anal- yse spatio-temporal
dynamics of fluorescence markers along curvilinear biological compartments.
These objects have a sin- gular geometry, thus kymograph reconstruction is
inherently an analog image processing task. However, the existing approaches
are essentially digital: the kymograph photometry is sampled directly from the
time-lapse images. As a result, such kymographs rely on raw image data that
suffer from the degradations entailed by the image formation process and the
spatio-temporal resolution of the imaging setup. In this work, we address these
limitations and introduce a well-grounded Bayesian framework for the analog
reconstruction of kymographs. To handle the movement of the object, we
introduce an intrinsic description of kymographs using differential geometry: a
kymograph is a photometry defined on a parameter space that is embedded in
physical space by a time-varying map that follows the object geometry. We model
the kymograph photometry as a L\'evy innovation process, a flexible class of
non-parametric signal priors. We account for the image formation process using
the virtual microscope framework. We formulate a computationally tractable
representation of the associated maximum a posteriori problem and solve it
using a class of efficient and modular algorithms based on the alternating
split Bregman. We assess the performance of our Bayesian framework on synthetic
data and apply it to reconstruct the fluorescence dynamics along microtubules
in vivo in the budding yeast S. cerevisiae. We demonstrate that our framework
allows revealing patterns from single time-lapse data that are invisible on
standard digital kymographs.
Learning Paths from Signature Tensors
Authors: Max Pfeffer, Anna Seigal, Bernd Sturmfels
Date: 2018-09-05 00:00:00
Tasks: Tensor Decomposition

Matrix congruence extends naturally to the setting of tensors. We apply
methods from tensor decomposition, algebraic geometry and numerical
optimization to this group action. Given a tensor in the orbit of another
tensor, we compute a matrix which transforms one to the other. Our primary
application is an inverse problem from stochastic analysis: the recovery of
paths from their third order signature tensors. We establish identifiability
results, both exact and numerical, for piecewise linear paths, polynomial
paths, and generic dictionaries. Numerical optimization is applied for recovery
from inexact data. We also compute the shortest path with a given signature
tensor.
GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation
Authors: Minsuk Kahng, Nikhil Thorat, Duen Horng Chau, Fernanda Viégas, Martin Wattenberg
Date: 2018-09-05 00:00:00
Tasks: Deep Learning

Recent success in deep learning has generated immense interest among
practitioners and students, inspiring many to learn about this new technology.
While visual and interactive approaches have been successfully developed to
help people more easily learn deep learning, most existing tools focus on
simpler models. In this work, we present GAN Lab, the first interactive
visualization tool designed for non-experts to learn and experiment with
Generative Adversarial Networks (GANs), a popular class of complex deep
learning models. With GAN Lab, users can interactively train generative models
and visualize the dynamic training process's intermediate results. GAN Lab
tightly integrates an model overview graph that summarizes GAN's structure, and
a layered distributions view that helps users interpret the interplay between
submodels. GAN Lab introduces new interactive experimentation features for
learning complex deep learning models, such as step-by-step training at
multiple levels of abstraction for understanding intricate training dynamics.
Implemented using TensorFlow.js, GAN Lab is accessible to anyone via modern web
browsers, without the need for installation or specialized hardware, overcoming
a major practical challenge in deploying interactive tools for deep learning.
Modelling Point Spread Function in Fluorescence Microscopy with a Sparse Combination of Gaussian Mixture: Trade-off between Accuracy and Efficiency
Authors: Denis K. Samuylov, Prateek Purwar, Gábor Székely, Grégory Paul
Date: 2018-09-05 00:00:00
Tasks: Computational Efficiency, Deblurring, Super-Resolution

Deblurring is a fundamental inverse problem in bioimaging. It requires
modelling the point spread function (PSF), which captures the optical
distortions entailed by the image formation process. The PSF limits the spatial
resolution attainable for a given microscope. However, recent applications
require a higher resolution, and have prompted the development of
super-resolution techniques to achieve sub-pixel accuracy. This requirement
restricts the class of suitable PSF models to analog ones. In addition,
deblurring is computationally intensive, hence further requiring
computationally efficient models. A custom candidate fitting both requirements
is the Gaussian model. However, this model cannot capture the rich tail
structures found in both theoretical and empirical PSFs. In this paper, we aim
at improving the reconstruction accuracy beyond the Gaussian model, while
preserving its computational efficiency. We introduce a new class of analog PSF
models based on Gaussian mixtures. The number of Gaussian kernels controls both
the modelling accuracy and the computational efficiency of the model: the lower
the number of kernels, the lower accuracy and the higher efficiency. To explore
the accuracy--efficiency trade-off, we propose a variational formulation of the
PSF calibration problem, where a convex sparsity-inducing penalty on the number
of Gaussian kernels allows trading accuracy for efficiency. We derive an
efficient algorithm based on a fully-split formulation of alternating split
Bregman. We assess our framework on synthetic and real data and demonstrate a
better reconstruction accuracy in both geometry and photometry in point source
localisation---a fundamental inverse problem in fluorescence microscopy.
Document-Level Neural Machine Translation with Hierarchical Attention Networks
Authors: Lesly Miculicich, Dhananjay Ram, Nikolaos Pappas, James Henderson
Date: 2018-09-05 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, Machine Translation, NMT, Translation

Neural Machine Translation (NMT) can be improved by including document-level
contextual information. For this purpose, we propose a hierarchical attention
model to capture the context in a structured and dynamic manner. The model is
integrated in the original NMT architecture as another level of abstraction,
conditioning on the NMT model's own previous hidden states. Experiments show
that hierarchical attention significantly improves the BLEU score over a strong
NMT baseline with the state-of-the-art in context-aware methods, and that both
the encoder and decoder benefit from context in complementary ways.
Stance Prediction for Russian: Data and Analysis
Authors: Nikita Lozhnikov, Leon Derczynski, Manuel Mazzara
Date: 2018-09-05 00:00:00
Tasks: Classification, General Classification, Prediction, Stance Classification, Stance Detection, text-classification

Stance detection is a critical component of rumour and fake news
identification. It involves the extraction of the stance a particular author
takes related to a given claim, both expressed in text. This paper investigates
stance classification for Russian. It introduces a new dataset, RuStance, of
Russian tweets and news comments from multiple sources, covering multiple
stories, as well as text classification approaches to stance detection as
benchmarks over this data in this language. As well as presenting this
openly-available dataset, the first of its kind for Russian, the paper presents
a baseline for stance prediction in the language.
Knowledge Integrated Classifier Design Based on Utility Optimization
Authors: Shaohan Chen, Chuanhou Gao
Date: 2018-09-05 00:00:00
Tasks: General Classification

This paper proposes a systematic framework to design a classification model
that yields a classifier which optimizes a utility function based on prior
knowledge. Specifically, as the data size grows, we prove that the produced
classifier asymptotically converges to the optimal classifier, an extended
version of the Bayes rule, which maximizes the utility function. Therefore, we
provide a meaningful theoretical interpretation for modeling with the knowledge
incorporated. Our knowledge incorporation method allows domain experts to guide
the classifier towards correctly classifying data that they think to be more
significant.
Deep Depth from Defocus: how can defocus blur improve 3D estimation using dense neural networks?
Authors: Marcela Carvalho, Bertrand Le Saux, Pauline Trouvé-Peloux, Andrés Almansa, Frédéric Champagnat
Date: 2018-09-05 00:00:00
Tasks: 3D Reconstruction, Depth Estimation, Depth Prediction, Scene Understanding

Depth estimation is of critical interest for scene understanding and accurate
3D reconstruction. Most recent approaches in depth estimation with deep
learning exploit geometrical structures of standard sharp images to predict
corresponding depth maps. However, cameras can also produce images with defocus
blur depending on the depth of the objects and camera settings. Hence, these
features may represent an important hint for learning to predict depth. In this
paper, we propose a full system for single-image depth prediction in the wild
using depth-from-defocus and neural networks. We carry out thorough experiments
to test deep convolutional networks on real and simulated defocused images
using a realistic model of blur variation with respect to depth. We also
investigate the influence of blur on depth prediction observing model
uncertainty with a Bayesian neural network approach. From these studies, we
show that out-of-focus blur greatly improves the depth-prediction network
performances. Furthermore, we transfer the ability learned on a synthetic,
indoor dataset to real, indoor and outdoor images. For this purpose, we present
a new dataset containing real all-focus and defocused images from a Digital
Single-Lens Reflex (DSLR) camera, paired with ground truth depth maps obtained
with an active 3D sensor for indoor scenes. The proposed approach is
successfully validated on both this new dataset and standard ones as NYUv2 or
Depth-in-the-Wild. Code and new datasets are available at
https://github.com/marcelampc/d3net_depth_estimation
Traffic Density Estimation using a Convolutional Neural Network
Authors: Julian Nubert, Nicholas Giai Truong, Abel Lim, Herbert Ilhan Tanujaya, Leah Lim, Mai Anh Vu
Date: 2018-09-05 00:00:00
Tasks: BIG-bench Machine Learning, Density Estimation, Traffic Signal Control

The goal of this project is to introduce and present a machine learning
application that aims to improve the quality of life of people in Singapore. In
particular, we investigate the use of machine learning solutions to tackle the
problem of traffic congestion in Singapore. In layman's terms, we seek to make
Singapore (or any other city) a smoother place. To accomplish this aim, we
present an end-to-end system comprising of 1. A traffic density estimation
algorithm at traffic lights/junctions and 2. a suitable traffic signal control
algorithms that make use of the density information for better traffic control.
Traffic density estimation can be obtained from traffic junction images using
various machine learning techniques (combined with CV tools). After research
into various advanced machine learning methods, we decided on convolutional
neural networks (CNNs). We conducted experiments on our algorithms, using the
publicly available traffic camera dataset published by the Land Transport
Authority (LTA) to demonstrate the feasibility of this approach. With these
traffic density estimates, different traffic algorithms can be applied to
minimize congestion at traffic junctions in general.
Reinforcement Learning under Threats
Authors: Victor Gallego, Roi Naveiro, David Rios Insua
Date: 2018-09-05 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In several reinforcement learning (RL) scenarios, mainly in security settings, there may be adversaries trying to interfere with the reward generating process. In this paper, we introduce Threatened Markov Decision Processes (TMDPs), which provide a framework to support a decision maker against a potential adversary in RL. Furthermore, we propose a level-$k$ thinking scheme resulting in a new learning framework to deal with TMDPs. After introducing our framework and deriving theoretical results, relevant empirical evidence is given via extensive experiments, showing the benefits of accounting for adversaries while the agent learns.
Dynamically Context-Sensitive Time-Decay Attention for Dialogue Modeling
Authors: Shang-Yu Su, Pei-Chieh Yuan, Yun-Nung Chen
Date: 2018-09-05 00:00:00
Tasks: Dialogue State Tracking, Spoken Language Understanding

Spoken language understanding (SLU) is an essential component in
conversational systems. Considering that contexts provide informative cues for
better understanding, history can be leveraged for contextual SLU. However,
most prior work only paid attention to the related content in history
utterances and ignored the temporal information. In dialogues, it is intuitive
that the most recent utterances are more important than the least recent ones,
and time-aware attention should be in a decaying manner. Therefore, this paper
allows the model to automatically learn a time-decay attention function where
the attentional weights can be dynamically decided based on the content of each
role's contexts, which effectively integrates both content-aware and time-aware
perspectives and demonstrates remarkable flexibility to complex dialogue
contexts. The experiments on the benchmark Dialogue State Tracking Challenge
(DSTC4) dataset show that the proposed dynamically context-sensitive time-decay
attention mechanisms significantly improve the state-of-the-art model for
contextual understanding performance.
CNNs-based Acoustic Scene Classification using Multi-Spectrogram Fusion and Label Expansions
Authors: Weiping Zheng, Zhenyao Mo, Xiaotao Xing, Gansen Zhao
Date: 2018-09-05 00:00:00
Tasks: Acoustic Scene Classification, Classification, General Classification, Scene Classification

Spectrograms have been widely used in Convolutional Neural Networks based
schemes for acoustic scene classification, such as the STFT spectrogram and the
MFCC spectrogram, etc. They have different time-frequency characteristics,
contributing to their own advantages and disadvantages in recognizing acoustic
scenes. In this letter, a novel multi-spectrogram fusion framework is proposed,
making the spectrograms complement each other. In the framework, a single CNN
architecture is applied onto multiple spectrograms for feature extraction. The
deep features extracted from multiple spectrograms are then fused to
discriminate the acoustic scenes. Moreover, motivated by the inter-class
similarities in acoustic scene datasets, a label expansion method is further
proposed in which super-class labels are constructed upon the original classes.
On the help of the expanded labels, the CNN models are transformed into the
multitask learning form to improve the acoustic scene classification by
appending the auxiliary task of super-class classification. To verify the
effectiveness of the proposed methods, intensive experiments have been
performed on the DCASE2017 and the LITIS Rouen datasets. Experimental results
show that the proposed method can achieve promising accuracies on both
datasets. Specifically, accuracies of 0.9744, 0.8865 and 0.7778 are obtained
for the LITIS Rouen dataset, the DCASE Development set and Evaluation set
respectively.
Copenhagen at CoNLL--SIGMORPHON 2018: Multilingual Inflection in Context with Explicit Morphosyntactic Decoding
Authors: Yova Kementchedjhieva, Johannes Bjerva, Isabelle Augenstein
Date: 2018-09-05 00:00:00
Proceeding: CONLL 2018 10
Tasks: Decoder, LEMMA, Morphological Inflection, Multi-Task Learning, Task 2

This paper documents the Team Copenhagen system which placed first in the
CoNLL--SIGMORPHON 2018 shared task on universal morphological reinflection,
Task 2 with an overall accuracy of 49.87. Task 2 focuses on morphological
inflection in context: generating an inflected word form, given the lemma of
the word and the context it occurs in. Previous SIGMORPHON shared tasks have
focused on context-agnostic inflection---the "inflection in context" task was
introduced this year. We approach this with an encoder-decoder architecture
over character sequences with three core innovations, all contributing to an
improvement in performance: (1) a wide context window; (2) a multi-task
learning approach with the auxiliary task of MSD prediction; (3) training
models in a multilingual fashion.
Modeling human intuitions about liquid flow with particle-based simulation
Authors: Christopher J. Bates, Ilker Yildirim, Joshua B. Tenenbaum, Peter Battaglia
Date: 2018-09-05 00:00:00
Tasks: Scene Understanding

Humans can easily describe, imagine, and, crucially, predict a wide variety
of behaviors of liquids--splashing, squirting, gushing, sloshing, soaking,
dripping, draining, trickling, pooling, and pouring--despite tremendous
variability in their material and dynamical properties. Here we propose and
test a computational model of how people perceive and predict these liquid
dynamics, based on coarse approximate simulations of fluids as collections of
interacting particles. Our model is analogous to a "game engine in the head",
drawing on techniques for interactive simulations (as in video games) that
optimize for efficiency and natural appearance rather than physical accuracy.
In two behavioral experiments, we found that the model accurately captured
people's predictions about how liquids flow among complex solid obstacles, and
was significantly better than two alternatives based on simple heuristics and
deep neural networks. Our model was also able to explain how people's
predictions varied as a function of the liquids' properties (e.g., viscosity
and stickiness). Together, the model and empirical results extend the recent
proposal that human physical scene understanding for the dynamics of rigid,
solid objects can be supported by approximate probabilistic simulation, to the
more complex and unexplored domain of fluid dynamics.