pwc-archive/papers-with-abstracts huggingface dataset:
DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
Authors: Ilke Demir, Krzysztof Koperski, David Lindenbaum, Guan Pang, Jing Huang, Saikat Basu, Forest Hughes, Devis Tuia, Ramesh Raskar
Date: 2018-05-17 00:00:00

We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which
includes three public competitions for segmentation, detection, and
classification tasks on satellite images. Similar to other challenges in
computer vision domain such as DAVIS and COCO, DeepGlobe proposes three
datasets and corresponding evaluation methodologies, coherently bundled in
three competitions with a dedicated workshop co-located with CVPR 2018.
  We observed that satellite imagery is a rich and structured source of
information, yet it is less investigated than everyday images by computer
vision researchers. However, bridging modern computer vision with remote
sensing data analysis could have critical impact to the way we understand our
environment and lead to major breakthroughs in global urban planning or climate
change research. Keeping such bridging objective in mind, DeepGlobe aims to
bring together researchers from different domains to raise awareness of remote
sensing in the computer vision community and vice-versa. We aim to improve and
evaluate state-of-the-art satellite image understanding approaches, which can
hopefully serve as reference benchmarks for future research in the same topic.
In this paper, we analyze characteristics of each dataset, define the
evaluation criteria of the competitions, and provide baselines for each task.
Recurrent Neural Network for Learning DenseDepth and Ego-Motion from Video
Authors: Rui Wang, Jan-Michael Frahm, Stephen M. Pizer
Date: 2018-05-17 00:00:00
Tasks: 3D Reconstruction, Depth Estimation, Motion Estimation

Learning-based, single-view depth estimation often generalizes poorly to
unseen datasets. While learning-based, two-frame depth estimation solves this
problem to some extent by learning to match features across frames, it performs
poorly at large depth where the uncertainty is high. There exists few
learning-based, multi-view depth estimation methods. In this paper, we present
a learning-based, multi-view dense depth map and ego-motion estimation method
that uses Recurrent Neural Networks (RNN). Our model is designed for 3D
reconstruction from video where the input frames are temporally correlated. It
is generalizable to single- or two-view dense depth estimation. Compared to
recent single- or two-view CNN-based depth estimation methods, our model
leverages more views and achieves more accurate results, especially at large
distances. Our method produces superior results to the state-of-the-art
learning-based, single- or two-view depth estimation methods on both indoor and
outdoor benchmark datasets. We also demonstrate that our method can even work
on extremely difficult sequences, such as endoscopic video, where none of the
assumptions (static scene, constant lighting, Lambertian reflection, etc.) from
traditional 3D reconstruction methods hold.
Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples
Authors: Vidur Joshi, Matthew Peters, Mark Hopkins
Date: 2018-05-16 00:00:00
Proceeding: ACL 2018 7
Tasks: Domain Adaptation

We revisit domain adaptation for parsers in the neural era. First we show
that recent advances in word representations greatly diminish the need for
domain adaptation when the target domain is syntactically similar to the source
domain. As evidence, we train a parser on the Wall Street Jour- nal alone that
achieves over 90% F1 on the Brown corpus. For more syntactically dis- tant
domains, we provide a simple way to adapt a parser using only dozens of partial
annotations. For instance, we increase the percentage of error-free
geometry-domain parses in a held-out set from 45% to 73% using approximately
five dozen training examples. In the process, we demon- strate a new
state-of-the-art single model result on the Wall Street Journal test set of
94.3%. This is an absolute increase of 1.7% over the previous state-of-the-art
of 92.6%.
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation
Authors: Juraj Juraska, Panagiotis Karagiannis, Kevin K. Bowden, Marilyn A. Walker
Date: 2018-05-16 00:00:00
Proceeding: NAACL 2018 6
Tasks: Data-to-Text Generation, Text Generation

Natural language generation lies at the core of generative dialogue systems
and conversational agents. We describe an ensemble neural language generator,
and present several novel methods for data representation and augmentation that
yield improved results in our model. We test the model on three datasets in the
restaurant, TV and laptop domains, and report both objective and subjective
evaluations of our best model. Using a range of automatic metrics, as well as
human evaluators, we show that our approach achieves better results than
state-of-the-art models on the same datasets.
Defoiling Foiled Image Captions
Authors: Pranava Madhyastha, Josiah Wang, Lucia Specia
Date: 2018-05-16 00:00:00
Proceeding: NAACL 2018 6
Tasks: Descriptive, Image Captioning, valid

We address the task of detecting foiled image captions, i.e. identifying
whether a caption contains a word that has been deliberately replaced by a
semantically similar word, thus rendering it inaccurate with respect to the
image being described. Solving this problem should in principle require a
fine-grained understanding of images to detect linguistically valid
perturbations in captions. In such contexts, encoding sufficiently descriptive
image information becomes a key challenge. In this paper, we demonstrate that
it is possible to solve this task using simple, interpretable yet powerful
representations based on explicit object information. Our models achieve
state-of-the-art performance on a standard dataset, with scores exceeding those
achieved by humans on the task. We also measure the upper-bound performance of
our models using gold standard annotations. Our analysis reveals that the
simpler model performs well even without image information, suggesting that the
dataset contains strong linguistic bias.
On the convergence properties of a $K$-step averaging stochastic gradient descent algorithm for nonconvex optimization
Authors: Fan Zhou, Guojing Cong
Date: 2017-08-03 00:00:00
Tasks: Avg

Despite their popularity, the practical performance of asynchronous
stochastic gradient descent methods (ASGD) for solving large scale machine
learning problems are not as good as theoretical results indicate. We adopt and
analyze a synchronous K-step averaging stochastic gradient descent algorithm
which we call K-AVG. We establish the convergence results of K-AVG for
nonconvex objectives and explain why the K-step delay is necessary and leads to
better performance than traditional parallel stochastic gradient descent which
is a special case of K-AVG with $K=1$. We also show that K-AVG scales better
than ASGD. Another advantage of K-AVG over ASGD is that it allows larger
stepsizes. On a cluster of $128$ GPUs, K-AVG is faster than ASGD
implementations and achieves better accuracies and faster convergence for
\cifar dataset.
Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification
Authors: Huy Phan, Fernando Andreotti, Navin Cooray, Oliver Y. Chén, Maarten De Vos
Date: 2018-05-16 00:00:00
Tasks: Automatic Sleep Stage Classification, Classification, General Classification, Sleep Stage Detection, Sleep Staging

Correctly identifying sleep stages is important in diagnosing and treating
sleep disorders. This work proposes a joint classification-and-prediction
framework based on CNNs for automatic sleep staging, and, subsequently,
introduces a simple yet efficient CNN architecture to power the framework.
Given a single input epoch, the novel framework jointly determines its label
(classification) and its neighboring epochs' labels (prediction) in the
contextual output. While the proposed framework is orthogonal to the widely
adopted classification schemes, which take one or multiple epochs as contextual
inputs and produce a single classification decision on the target epoch, we
demonstrate its advantages in several ways. First, it leverages the dependency
among consecutive sleep epochs while surpassing the problems experienced with
the common classification schemes. Second, even with a single model, the
framework has the capacity to produce multiple decisions, which are essential
in obtaining a good performance as in ensemble-of-models methods, with very
little induced computational overhead. Probabilistic aggregation techniques are
then proposed to leverage the availability of multiple decisions. We conducted
experiments on two public datasets: Sleep-EDF Expanded with 20 subjects, and
Montreal Archive of Sleep Studies dataset with 200 subjects. The proposed
framework yields an overall classification accuracy of 82.3% and 83.6%,
respectively. We also show that the proposed framework not only is superior to
the baselines based on the common classification schemes but also outperforms
existing deep-learning approaches. To our knowledge, this is the first work
going beyond the standard single-output classification to consider multitask
neural networks for automatic sleep staging. This framework provides avenues
for further studies of different neural-network architectures for automatic
sleep staging.
Omega: An Architecture for AI Unification
Authors: Eray Özkural
Date: 2018-05-16 00:00:00
Tasks: Diversity

We introduce the open-ended, modular, self-improving Omega AI unification
architecture which is a refinement of Solomonoff's Alpha architecture, as
considered from first principles. The architecture embodies several crucial
principles of general intelligence including diversity of representations,
diversity of data types, integrated memory, modularity, and higher-order
cognition. We retain the basic design of a fundamental algorithmic substrate
called an "AI kernel" for problem solving and basic cognitive functions like
memory, and a larger, modular architecture that re-uses the kernel in many
ways. Omega includes eight representation languages and six classes of neural
networks, which are briefly introduced. The architecture is intended to
initially address data science automation, hence it includes many problem
solving methods for statistical tasks. We review the broad software
architecture, higher-order cognition, self-improvement, modular neural
architectures, intelligent agents, the process and memory hierarchy, hardware
abstraction, peer-to-peer computing, and data abstraction facility.
Beyond Structural Causal Models: Causal Constraints Models
Authors: Tineke Blom, Stephan Bongers, Joris M. Mooij
Date: 2018-05-16 00:00:00

Structural Causal Models (SCMs) provide a popular causal modeling framework. In this work, we show that SCMs are not flexible enough to give a complete causal representation of dynamical systems at equilibrium. Instead, we propose a generalization of the notion of an SCM, that we call Causal Constraints Model (CCM), and prove that CCMs do capture the causal semantics of such systems. We show how CCMs can be constructed from differential equations and initial conditions and we illustrate our ideas further on a simple but ubiquitous (bio)chemical reaction. Our framework also allows to model functional laws, such as the ideal gas law, in a sensible and intuitive way.
Are BLEU and Meaning Representation in Opposition?
Authors: Ondřej Cífka, Ondřej Bojar
Date: 2018-05-16 00:00:00
Tasks: General Classification, Machine Translation, NMT, Sentence, Translation

One of possible ways of obtaining continuous-space sentence representations
is by training neural machine translation (NMT) systems. The recent attention
mechanism however removes the single point in the neural network from which the
source sentence representation can be extracted. We propose several variations
of the attentive NMT architecture bringing this meeting point back. Empirical
evaluation suggests that the better the translation quality, the worse the
learned sentence representations serve in a wide range of classification and
similarity tasks.
Modeling Naive Psychology of Characters in Simple Commonsense Stories
Authors: Hannah Rashkin, Antoine Bosselut, Maarten Sap, Kevin Knight, Yejin Choi
Date: 2018-05-16 00:00:00
Proceeding: ACL 2018 7
Tasks: Emotion Classification

Understanding a narrative requires reading between the lines and reasoning
about the unspoken but obvious implications about events and people's mental
states - a capability that is trivial for humans but remarkably hard for
machines. To facilitate research addressing this challenge, we introduce a new
annotation framework to explain naive psychology of story characters as
fully-specified chains of mental states with respect to motivations and
emotional reactions. Our work presents a new large-scale dataset with rich
low-level annotations and establishes baseline performance on several new
tasks, suggesting avenues for future research.
Development and analysis of a Bayesian water balance model for large lake systems
Authors: Joeseph P. Smith, Andrew D. Gronewold
Date: 2017-10-26 00:00:00

Water balance models (WBMs) are often employed to understand regional
hydrologic cycles over various time scales. Most WBMs, however, are
physically-based, and few employ state-of-the-art statistical methods to
reconcile independent input measurement uncertainty and bias. Further, few WBMs
exist for large lakes, and most large lake WBMs perform additive accounting,
with minimal consideration towards input data uncertainty. Here, we introduce a
framework for improving a previously developed large lake statistical water
balance model (L2SWBM). Focusing on the water balances of Lakes Superior and
Michigan-Huron, we demonstrate our new analytical framework, identifying
L2SWBMs from 26 alternatives that adequately close the water balance of the
lakes with satisfactory computation times compared with the prototype model. We
expect our new framework will be used to develop water balance models for other
lakes around the world.
DINFRA: A One Stop Shop for Computing Multilingual Semantic Relatedness
Authors: Siamak Barzegar, Juliano Efson Sales, Andre Freitas, Siegfried Handschuh, Brian Davis
Date: 2018-05-16 00:00:00

This demonstration presents an infrastructure for computing multilingual
semantic relatedness and correlation for twelve natural languages by using
three distributional semantic models (DSMs). Our demonsrator - DInfra
(Distributional Infrastructure) provides researchers and developers with a
highly useful platform for processing large-scale corpora and conducting
experiments with distributional semantics. We integrate several multilingual
DSMs in our webservice so the end user can obtain a result without worrying
about the complexities involved in building DSMs. Our webservice allows the
users to have easy access to a wide range of comparisons of DSMs with different
parameters. In addition, users can configure and access DSM parameters using an
easy to use API.
End-to-end Learning of a Convolutional Neural Network via Deep Tensor Decomposition
Authors: Samet Oymak, Mahdi Soltanolkotabi
Date: 2018-05-16 00:00:00
Tasks: Tensor Decomposition

In this paper we study the problem of learning the weights of a deep
convolutional neural network. We consider a network where convolutions are
carried out over non-overlapping patches with a single kernel in each layer. We
develop an algorithm for simultaneously learning all the kernels from the
training data. Our approach dubbed Deep Tensor Decomposition (DeepTD) is based
on a rank-1 tensor decomposition. We theoretically investigate DeepTD under a
realizable model for the training data where the inputs are chosen i.i.d. from
a Gaussian distribution and the labels are generated according to planted
convolutional kernels. We show that DeepTD is data-efficient and provably works
as soon as the sample size exceeds the total number of convolutional weights in
the network. We carry out a variety of numerical experiments to investigate the
effectiveness of DeepTD and verify our theoretical findings.
Learning to Branch
Authors: Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, Ellen Vitercik
Date: 2018-03-27 00:00:00
Proceeding: ICML 2018 7
Tasks: Variable Selection

Tree search algorithms, such as branch-and-bound, are the most widely used
tools for solving combinatorial and nonconvex problems. For example, they are
the foremost method for solving (mixed) integer programs and constraint
satisfaction problems. Tree search algorithms recursively partition the search
space to find an optimal solution. In order to keep the tree size small, it is
crucial to carefully decide, when expanding a tree node, which question
(typically variable) to branch on at that node in order to partition the
remaining space. Numerous partitioning techniques (e.g., variable selection)
have been proposed, but there is no theory describing which technique is
optimal. We show how to use machine learning to determine an optimal weighting
of any set of partitioning procedures for the instance distribution at hand
using samples from the distribution. We provide the first sample complexity
guarantees for tree search algorithm configuration. These guarantees bound the
number of samples sufficient to ensure that the empirical performance of an
algorithm over the samples nearly matches its expected performance on the
unknown instance distribution. This thorough theoretical investigation
naturally gives rise to our learning algorithm. Via experiments, we show that
learning an optimal weighting of partitioning procedures can dramatically
reduce tree size, and we prove that this reduction can even be exponential.
Through theory and experiments, we show that learning to branch is both
practical and hugely beneficial.
Empirical Risk Minimization in Non-interactive Local Differential Privacy: Efficiency and High Dimensional Case
Authors: Di Wang, Marco Gaboardi, Jinhui Xu
Date: 2018-02-12 00:00:00
Proceeding: NeurIPS 2018

In this paper, we study the Empirical Risk Minimization problem in the
non-interactive local model of differential privacy. In the case of constant or
low dimensionality ($p\ll n$), we first show that if the ERM loss function is
$(\infty, T)$-smooth, then we can avoid a dependence of the sample complexity,
to achieve error $\alpha$, on the exponential of the dimensionality $p$ with
base $1/\alpha$ (i.e., $\alpha^{-p}$), which answers a question in [smith 2017
interaction]. Our approach is based on polynomial approximation. Then, we
propose player-efficient algorithms with $1$-bit communication complexity and
$O(1)$ computation cost for each player. The error bound is asymptotically the
same as the original one. Also with additional assumptions we show a server
efficient algorithm. Next we consider the high dimensional case ($n\ll p$), we
show that if the loss function is Generalized Linear function and convex, then
we could get an error bound which is dependent on the Gaussian width of the
underlying constrained set instead of $p$, which is lower than that in [smith
2017 interaction].
Semantic Relatedness for All (Languages): A Comparative Analysis of Multilingual Semantic Relatedness Using Machine Translation
Authors: Andre Freitas, Siamak Barzegar, Juliano Efson Sales, Siegfried Handschuh, Brian Davis
Date: 2018-05-16 00:00:00
Tasks: All, Machine Translation, Translation

This paper provides a comparative analysis of the performance of four
state-of-the-art distributional semantic models (DSMs) over 11 languages,
contrasting the native language-specific models with the use of machine
translation over English-based DSMs. The experimental results show that there
is a significant improvement (average of 16.7% for the Spearman correlation) by
using state-of-the-art machine translation approaches. The results also show
that the benefit of using the most informative corpus outweighs the possible
errors introduced by the machine translation. For all languages, the
combination of machine translation over the Word2Vec English distributional
model provided the best results consistently (average Spearman correlation of
0.68).
Composite Semantic Relation Classification
Authors: Siamak Barzegar, Andre Freitas, Siegfried Handschuh, Brian Davis
Date: 2018-05-16 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Question Answering, Relation, Relation Classification

Different semantic interpretation tasks such as text entailment and question
answering require the classification of semantic relations between terms or
entities within text. However, in most cases it is not possible to assign a
direct semantic relation between entities/terms. This paper proposes an
approach for composite semantic relation classification, extending the
traditional semantic relation classification task. Different from existing
approaches, which use machine learning models built over lexical and
distributional word vector features, the proposed model uses the combination of
a large commonsense knowledge base of binary relations, a distributional
navigational algorithm and sequence classification to provide a solution for
the composite semantic relation classification problem.
Sim-to-Real: Learning Agile Locomotion For Quadruped Robots
Authors: Jie Tan, Tingnan Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, Vincent Vanhoucke
Date: 2018-04-27 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

Designing agile locomotion for quadruped robots often requires extensive
expertise and tedious manual tuning. In this paper, we present a system to
automate this process by leveraging deep reinforcement learning techniques. Our
system can learn quadruped locomotion from scratch using simple reward signals.
In addition, users can provide an open loop reference to guide the learning
process when more control over the learned gait is needed. The control policies
are learned in a physics simulator and then deployed on real robots. In
robotics, policies trained in simulation often do not transfer to the real
world. We narrow this reality gap by improving the physics simulator and
learning robust policies. We improve the simulation using system
identification, developing an accurate actuator model and simulating latency.
We learn robust controllers by randomizing the physical environments, adding
perturbations and designing a compact observation space. We evaluate our system
on two agile locomotion gaits: trotting and galloping. After learning in
simulation, a quadruped robot can successfully perform both gaits in the real
world.
Learning Strict Identity Mappings in Deep Residual Networks
Authors: Xin Yu, Zhiding Yu, Srikumar Ramalingam
Date: 2018-04-05 00:00:00
Proceeding: CVPR 2018 6
Tasks: object-detection, Object Detection, Semantic Segmentation

A family of super deep networks, referred to as residual networks or ResNet, achieved record-beating performance in various visual tasks such as image recognition, object detection, and semantic segmentation. The ability to train very deep networks naturally pushed the researchers to use enormous resources to achieve the best performance. Consequently, in many applications super deep residual networks were employed for just a marginal improvement in performance. In this paper, we propose epsilon-ResNet that allows us to automatically discard redundant layers, which produces responses that are smaller than a threshold epsilon, with a marginal or no loss in performance. The epsilon-ResNet architecture can be achieved using a few additional rectified linear units in the original ResNet. Our method does not use any additional variables nor numerous trials like other hyper-parameter optimization techniques. The layer selection is achieved using a single training process and the evaluation is performed on CIFAR-10, CIFAR-100, SVHN, and ImageNet datasets. In some instances, we achieve about 80% reduction in the number of parameters.