pwc-archive/papers-with-abstracts huggingface dataset:
A Cell-Division Search Technique for Inversion with Application to Picture-Discovery and Magnetotellurics
Authors: Bradley Alexander, Yang Heng Lee
Date: 2018-04-21 00:00:00

Solving inverse problems in natural sciences often requires a search pro-
cess to find explanatory models that match collected field data. Inverse
problems are often under-determined meaning that there are many poten- tial
explanatory models for the same data. In such cases using stochastic search,
through providing multiple solutions, can help characterise which model
features that are most persistent and therefore likely to be real.
Unfortunately, in some fields, large parameter spaces can make stochas- tic
search intractable. In this work we improve upon previous work by defining a
compact and expressive representation and search process able to describe and
discover two and three dimensional spatial models. The search process takes
place in stages starting with greedy search, followed by alternating stages of
evolutionary search and a novel model-splitting process inspired by
cell-division. We apply this framework to two prob- lems - magnetotellurics and
picture discovery. We show that our improved representation and search process
is able to produce detailed models with low error residuals.
Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation
Authors: Chichen Fu, Soonam Lee, David Joon Ho, Shuo Han, Paul Salama, Kenneth W. Dunn, Edward J. Delp
Date: 2018-01-22 00:00:00
Tasks: Deep Learning, Image Generation, Segmentation

Advances in fluorescence microscopy enable acquisition of 3D image volumes
with better image quality and deeper penetration into tissue. Segmentation is a
required step to characterize and analyze biological structures in the images
and recent 3D segmentation using deep learning has achieved promising results.
One issue is that deep learning techniques require a large set of groundtruth
data which is impractical to annotate manually for large 3D microscopy volumes.
This paper describes a 3D deep learning nuclei segmentation method using
synthetic 3D volumes for training. A set of synthetic volumes and the
corresponding groundtruth are generated using spatially constrained
cycle-consistent adversarial networks. Segmentation results demonstrate that
our proposed method is capable of segmenting nuclei successfully for various
data sets.
Dynamic Ensemble Selection VS K-NN: why and when Dynamic Selection obtains higher classification performance?
Authors: Rafael M. O. Cruz, Hiba H. Zakane, Robert Sabourin, George D. C. Cavalcanti
Date: 2018-04-21 00:00:00
Tasks: Classification, General Classification

Multiple classifier systems focus on the combination of classifiers to obtain
better performance than a single robust one. These systems unfold three major
phases: pool generation, selection and integration. One of the most promising
MCS approaches is Dynamic Selection (DS), which relies on finding the most
competent classifier or ensemble of classifiers to predict each test sample.
The majority of the DS techniques are based on the K-Nearest Neighbors (K-NN)
definition, and the quality of the neighborhood has a huge impact on the
performance of DS methods. In this paper, we perform an analysis comparing the
classification results of DS techniques and the K-NN classifier under different
conditions. Experiments are performed on 18 state-of-the-art DS techniques over
30 classification datasets and results show that DS methods present a
significant boost in classification accuracy even though they use the same
neighborhood as the K-NN. The reasons behind the outperformance of DS
techniques over the K-NN classifier reside in the fact that DS techniques can
deal with samples with a high degree of instance hardness (samples that are
located close to the decision border) as opposed to the K-NN. In this paper,
not only we explain why DS techniques achieve higher classification performance
than the K-NN but also when DS should be used.
Graph Autoencoder-Based Unsupervised Feature Selection with Broad and Local Data Structure Preservation
Authors: Siwei Feng, Marco F. Duarte
Date: 2018-01-07 00:00:00
Tasks: Dimensionality Reduction, feature selection, Sparse Learning

Feature selection is a dimensionality reduction technique that selects a
subset of representative features from high dimensional data by eliminating
irrelevant and redundant features. Recently, feature selection combined with
sparse learning has attracted significant attention due to its outstanding
performance compared with traditional feature selection methods that ignores
correlation between features. These works first map data onto a low-dimensional
subspace and then select features by posing a sparsity constraint on the
transformation matrix. However, they are restricted by design to linear data
transformation, a potential drawback given that the underlying correlation
structures of data are often non-linear. To leverage a more sophisticated
embedding, we propose an autoencoder-based unsupervised feature selection
approach that leverages a single-layer autoencoder for a joint framework of
feature selection and manifold learning. More specifically, we enforce column
sparsity on the weight matrix connecting the input layer and the hidden layer,
as in previous work. Additionally, we include spectral graph analysis on the
projected data into the learning process to achieve local data geometry
preservation from the original data space to the low-dimensional feature space.
Extensive experiments are conducted on image, audio, text, and biological data.
The promising experimental results validate the superiority of the proposed
method.
Event Extraction with Generative Adversarial Imitation Learning
Authors: Tongtao Zhang, Heng Ji
Date: 2018-04-21 00:00:00
Tasks: Event Extraction, Feature Engineering, Generative Adversarial Network, Imitation Learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a new method for event extraction (EE) task based on an imitation
learning framework, specifically, inverse reinforcement learning (IRL) via
generative adversarial network (GAN). The GAN estimates proper rewards
according to the difference between the actions committed by the expert (or
ground truth) and the agent among complicated states in the environment. EE
task benefits from these dynamic rewards because instances and labels yield to
various extents of difficulty and the gains are expected to be diverse -- e.g.,
an ambiguous but correctly detected trigger or argument should receive high
gains -- while the traditional RL models usually neglect such differences and
pay equal attention on all instances. Moreover, our experiments also
demonstrate that the proposed framework outperforms state-of-the-art methods,
without explicit feature engineering.
Massively Parallel Cross-Lingual Learning in Low-Resource Target Language Translation
Authors: Zhong Zhou, Matthias Sperber, Alex Waibel
Date: 2018-04-21 00:00:00
Proceeding: WS 2018 10
Tasks: Cross-Lingual Transfer, Translation

We work on translation from rich-resource languages to low-resource
languages. The main challenges we identify are the lack of low-resource
language data, effective methods for cross-lingual transfer, and the
variable-binding problem that is common in neural systems. We build a
translation system that addresses these challenges using eight European
language families as our test ground. Firstly, we add the source and the target
family labels and study intra-family and inter-family influences for effective
cross-lingual transfer. We achieve an improvement of +9.9 in BLEU score for
English-Swedish translation using eight families compared to the single-family
multi-source multi-target baseline. Moreover, we find that training on two
neighboring families closest to the low-resource language is often enough.
Secondly, we construct an ablation study and find that reasonably good results
can be achieved even with considerably less target data. Thirdly, we address
the variable-binding problem by building an order-preserving named entity
translation model. We obtain 60.6% accuracy in qualitative evaluation where our
translations are akin to human translations in a preliminary study.
Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding
Authors: Lifu Huang, Kyunghyun Cho, Boliang Zhang, Heng Ji, Kevin Knight
Date: 2018-04-21 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Clustering, Word Alignment

We construct a multilingual common semantic space based on distributional
semantics, where words from multiple languages are projected into a shared
space to enable knowledge and resource transfer across languages. Beyond word
alignment, we introduce multiple cluster-level alignments and enforce the word
clusters to be consistently distributed across multiple languages. We exploit
three signals for clustering: (1) neighbor words in the monolingual word
embedding space; (2) character-level information; and (3) linguistic properties
(e.g., apposition, locative suffix) derived from linguistic structure knowledge
bases available for thousands of languages. We introduce a new
cluster-consistent correlational neural network to construct the common
semantic space by aligning words as well as clusters. Intrinsic evaluation on
monolingual and multilingual QVEC tasks shows our approach achieves
significantly higher correlation with linguistic features than state-of-the-art
multi-lingual embedding learning methods do. Using low-resource language name
tagging as a case study for extrinsic evaluation, our approach achieves up to
24.5\% absolute F-score gain over the state of the art.
3D Human Pose Estimation on a Configurable Bed from a Pressure Image
Authors: Henry M. Clever, Ariel Kapusta, Daehyung Park, Zackory Erickson, Yash Chitalia, Charles C. Kemp
Date: 2018-04-21 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation

Robots have the potential to assist people in bed, such as in healthcare
settings, yet bedding materials like sheets and blankets can make observation
of the human body difficult for robots. A pressure-sensing mat on a bed can
provide pressure images that are relatively insensitive to bedding materials.
However, prior work on estimating human pose from pressure images has been
restricted to 2D pose estimates and flat beds. In this work, we present two
convolutional neural networks to estimate the 3D joint positions of a person in
a configurable bed from a single pressure image. The first network directly
outputs 3D joint positions, while the second outputs a kinematic model that
includes estimated joint angles and limb lengths. We evaluated our networks on
data from 17 human participants with two bed configurations: supine and seated.
Our networks achieved a mean joint position error of 77 mm when tested with
data from people outside the training set, outperforming several baselines. We
also present a simple mechanical model that provides insight into ambiguity
associated with limbs raised off of the pressure mat, and demonstrate that
Monte Carlo dropout can be used to estimate pose confidence in these
situations. Finally, we provide a demonstration in which a mobile manipulator
uses our network's estimated kinematic model to reach a location on a person's
body in spite of the person being seated in a bed and covered by a blanket.
Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size
Authors: Ian Goodfellow
Date: 2018-04-21 00:00:00

A key problem in research on adversarial examples is that vulnerability to
adversarial examples is usually measured by running attack algorithms. Because
the attack algorithms are not optimal, the attack algorithms are prone to
overestimating the size of perturbation needed to fool the target model. In
other words, the attack-based methodology provides an upper-bound on the size
of a perturbation that will fool the model, but security guarantees require a
lower bound. CLEVER is a proposed scoring method to estimate a lower bound.
Unfortunately, an estimate of a bound is not a bound. In this report, we show
that gradient masking, a common problem that causes attack methodologies to
provide only a very loose upper bound, causes CLEVER to overestimate the size
of perturbation needed to fool the model. In other words, CLEVER does not
resolve the key problem with the attack-based methodology, because it fails to
provide a lower bound.
Occluded object reconstruction for first responders with augmented reality glasses using conditional generative adversarial networks
Authors: Kyongsik Yun, Thomas Lu, Edward Chow
Date: 2018-04-20 00:00:00
Tasks: Object, Object Reconstruction

Firefighters suffer a variety of life-threatening risks, including
line-of-duty deaths, injuries, and exposures to hazardous substances. Support
for reducing these risks is important. We built a partially occluded object
reconstruction method on augmented reality glasses for first responders. We
used a deep learning based on conditional generative adversarial networks to
train associations between the various images of flammable and hazardous
objects and their partially occluded counterparts. Our system then
reconstructed an image of a new flammable object. Finally, the reconstructed
image was superimposed on the input image to provide "transparency". The system
imitates human learning about the laws of physics through experience by
learning the shape of flammable objects and the flame characteristics.
Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild
Authors: Zhen-Hua Feng, Patrik Huber, Josef Kittler, Peter JB Hancock, Xiao-Jun Wu, Qijun Zhao, Paul Koppen, Matthias Rätsch
Date: 2018-03-14 00:00:00
Tasks: 3D Face Reconstruction, 3D Reconstruction, Face Reconstruction, Gesture Recognition

This paper investigates the evaluation of dense 3D face reconstruction from a
single 2D image in the wild. To this end, we organise a competition that
provides a new benchmark dataset that contains 2000 2D facial images of 135
subjects as well as their 3D ground truth face scans. In contrast to previous
competitions or challenges, the aim of this new benchmark dataset is to
evaluate the accuracy of a 3D dense face reconstruction algorithm using real,
accurate and high-resolution 3D ground truth face scans. In addition to the
dataset, we provide a standard protocol as well as a Python script for the
evaluation. Last, we report the results obtained by three state-of-the-art 3D
face reconstruction systems on the new benchmark dataset. The competition is
organised along with the 2018 13th IEEE Conference on Automatic Face & Gesture
Recognition.
Subgoal Discovery for Hierarchical Dialogue Policy Learning
Authors: Da Tang, Xiujun Li, Jianfeng Gao, Chong Wang, Lihong Li, Tony Jebara
Date: 2018-04-20 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Hierarchical Reinforcement Learning, Reinforcement Learning

Developing agents to engage in complex goal-oriented dialogues is challenging
partly because the main learning signals are very sparse in long conversations.
In this paper, we propose a divide-and-conquer approach that discovers and
exploits the hidden structure of the task to enable efficient policy learning.
First, given successful example dialogues, we propose the Subgoal Discovery
Network (SDN) to divide a complex goal-oriented task into a set of simpler
subgoals in an unsupervised fashion. We then use these subgoals to learn a
multi-level policy by hierarchical reinforcement learning. We demonstrate our
method by building a dialogue agent for the composite task of travel planning.
Experiments with simulated and real users show that our approach performs
competitively against a state-of-the-art method that requires human-defined
subgoals. Moreover, we show that the learned subgoals are often human
comprehensible.
What's Going On in Neural Constituency Parsers? An Analysis
Authors: David Gaddy, Mitchell Stern, Dan Klein
Date: 2018-04-20 00:00:00
Proceeding: NAACL 2018 6
Tasks: Constituency Parsing

A number of differences have emerged between modern and classic approaches to
constituency parsing in recent years, with structural components like grammars
and feature-rich lexicons becoming less central while recurrent neural network
representations rise in popularity. The goal of this work is to analyze the
extent to which information provided directly by the model structure in
classical systems is still being captured by neural methods. To this end, we
propose a high-performance neural model (92.08 F1 on PTB) that is
representative of recent work and perform a series of investigative
experiments. We find that our model implicitly learns to encode much of the
same information that was explicitly provided by grammars and lexicons in the
past, indicating that this scaffolding can largely be subsumed by powerful
general-purpose neural machinery.
Loss Functions for Neural Networks for Image Processing
Authors: Hang Zhao, Orazio Gallo, Iuri Frosio, Jan Kautz
Date: 2015-11-28 00:00:00
Tasks: Image Restoration

Neural networks are becoming central in several areas of computer vision and
image processing and different architectures have been proposed to solve
specific problems. The impact of the loss layer of neural networks, however,
has not received much attention in the context of image processing: the default
and virtually only choice is L2. In this paper, we bring attention to
alternative choices for image restoration. In particular, we show the
importance of perceptually-motivated losses when the resulting image is to be
evaluated by a human observer. We compare the performance of several losses,
and propose a novel, differentiable error function. We show that the quality of
the results improves significantly with better loss functions, even when the
network architecture is left unchanged.
DeepPET: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem
Authors: Ida Häggström, C. Ross Schmidtlein, Gabriele Campanella, Thomas J. Fuchs
Date: 2018-04-20 00:00:00
Tasks: Decoder, Image Reconstruction

Positron emission tomography (PET) is a cornerstone of modern radiology. The
ability to detect cancer and metastases in whole body scans fundamentally
changed cancer diagnosis and treatment. One of the main bottlenecks in the
clinical application is the time it takes to reconstruct the anatomical image
from the deluge of data in PET imaging. State-of-the art methods based on
expectation maximization can take hours for a single patient and depend on
manual fine-tuning. This results not only in financial burden for hospitals but
more importantly leads to less efficient patient handling, evaluation, and
ultimately diagnosis and treatment for patients. To overcome this problem we
present a novel PET image reconstruction technique based on a deep
convolutional encoder-decoder network, that takes PET sinogram data as input
and directly outputs full PET images. Using realistic simulated data, we
demonstrate that our network is able to reconstruct images >100 times faster,
and with comparable image quality (in terms of root mean squared error)
relative to conventional iterative reconstruction techniques.
Mutual Information Maximization for Simple and Accurate Part-Of-Speech Induction
Authors: Karl Stratos
Date: 2018-04-20 00:00:00
Proceeding: NAACL 2019 6
Tasks: Clustering, POS

We address part-of-speech (POS) induction by maximizing the mutual
information between the induced label and its context. We focus on two training
objectives that are amenable to stochastic gradient descent (SGD): a novel
generalization of the classical Brown clustering objective and a recently
proposed variational lower bound. While both objectives are subject to noise in
gradient updates, we show through analysis and experiments that the variational
lower bound is robust whereas the generalized Brown objective is vulnerable. We
obtain competitive performance on a multitude of datasets and languages with a
simple architecture that encodes morphology and context.
Joint entity recognition and relation extraction as a multi-head selection problem
Authors: Giannis Bekoulis, Johannes Deleu, Thomas Demeester, Chris Develder
Date: 2018-04-20 00:00:00
Tasks: POS, Relation, Relation Extraction

State-of-the-art models for joint entity recognition and relation extraction
strongly rely on external natural language processing (NLP) tools such as POS
(part-of-speech) taggers and dependency parsers. Thus, the performance of such
joint models depends on the quality of the features obtained from these NLP
tools. However, these features are not always accurate for various languages
and contexts. In this paper, we propose a joint neural model which performs
entity recognition and relation extraction simultaneously, without the need of
any manually extracted features or the use of any external tool. Specifically,
we model the entity recognition task using a CRF (Conditional Random Fields)
layer and the relation extraction task as a multi-head selection problem (i.e.,
potentially identify multiple relations for each entity). We present an
extensive experimental setup, to demonstrate the effectiveness of our method
using datasets from various contexts (i.e., news, biomedical, real estate) and
languages (i.e., English, Dutch). Our model outperforms the previous neural
models that use automatically extracted features, while it performs within a
reasonable margin of feature-based neural models, or even beats them.
CactusNets: Layer Applicability as a Metric for Transfer Learning
Authors: Edward Collier, Robert DiBiano, Supratik Mukhopadhyay
Date: 2018-04-20 00:00:00
Tasks: Transfer Learning

Deep neural networks trained over large datasets learn features that are both
generic to the whole dataset, and specific to individual classes in the
dataset. Learned features tend towards generic in the lower layers and specific
in the higher layers of a network. Methods like fine-tuning are made possible
because of the ability for one filter to apply to multiple target classes. Much
like the human brain this behavior, can also be used to cluster and separate
classes. However, to the best of our knowledge there is no metric for how
applicable learned features are to specific classes. In this paper we propose a
definition and metric for measuring the applicability of learned features to
individual classes, and use this applicability metric to estimate input
applicability and produce a new method of unsupervised learning we call the
CactusNet.
Online Improper Learning with an Approximation Oracle
Authors: Elad Hazan, Wei Hu, Yuanzhi Li, Zhiyuan Li
Date: 2018-04-20 00:00:00
Proceeding: NeurIPS 2018 12

We revisit the question of reducing online learning to approximate
optimization of the offline problem. In this setting, we give two algorithms
with near-optimal performance in the full information setting: they guarantee
optimal regret and require only poly-logarithmically many calls to the
approximation oracle per iteration. Furthermore, these algorithms apply to the
more general improper learning problems. In the bandit setting, our algorithm
also significantly improves the best previously known oracle complexity while
maintaining the same regret.