pwc-archive/papers-with-abstracts huggingface dataset:
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator
Authors: Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang
Date: 2018-07-04 00:00:00
Proceeding: NeurIPS 2018
Tasks: Stochastic Optimization

In this paper, we propose a new technique named \textit{Stochastic
Path-Integrated Differential EstimatoR} (SPIDER), which can be used to track
many deterministic quantities of interest with significantly reduced
computational cost. We apply SPIDER to two tasks, namely the stochastic
first-order and zeroth-order methods. For stochastic first-order method,
combining SPIDER with normalized gradient descent, we propose two new
algorithms, namely SPIDER-SFO and SPIDER-SFO\textsuperscript{+}, that solve
non-convex stochastic optimization problems using stochastic gradients only. We
provide sharp error-bound results on their convergence rates. In special, we
prove that the SPIDER-SFO and SPIDER-SFO\textsuperscript{+} algorithms achieve
a record-breaking gradient computation cost of $\mathcal{O}\left( \min( n^{1/2}
\epsilon^{-2}, \epsilon^{-3} ) \right)$ for finding an $\epsilon$-approximate
first-order and $\tilde{\mathcal{O}}\left( \min( n^{1/2}
\epsilon^{-2}+\epsilon^{-2.5}, \epsilon^{-3} ) \right)$ for finding an
$(\epsilon, \mathcal{O}(\epsilon^{0.5}))$-approximate second-order stationary
point, respectively. In addition, we prove that SPIDER-SFO nearly matches the
algorithmic lower bound for finding approximate first-order stationary points
under the gradient Lipschitz assumption in the finite-sum setting. For
stochastic zeroth-order method, we prove a cost of $\mathcal{O}( d \min(
n^{1/2} \epsilon^{-2}, \epsilon^{-3}) )$ which outperforms all existing
results.
Building Damage Annotation on Post-Hurricane Satellite Imagery Based on Convolutional Neural Networks
Authors: Quoc Dung Cao, Youngjun Choe
Date: 2018-07-04 00:00:00
Tasks: Building Damage Assessment, General Classification, image-classification, Image Classification

After a hurricane, damage assessment is critical to emergency managers for efficient response and resource allocation. One way to gauge the damage extent is to quantify the number of flooded/damaged buildings, which is traditionally done by ground survey. This process can be labor-intensive and time-consuming. In this paper, we propose to improve the efficiency of building damage assessment by applying image classification algorithms to post-hurricane satellite imagery. At the known building coordinates (available from public data), we extract square-sized images from the satellite imagery to create training, validation, and test datasets. Each square-sized image contains a building to be classified as either 'Flooded/Damaged' (labeled by volunteers in a crowd-sourcing project) or 'Undamaged'. We design and train a convolutional neural network from scratch and compare it with an existing neural network used widely for common object classification. We demonstrate the promise of our damage annotation model (over 97% accuracy) in the case study of building damage assessment in the Greater Houston area affected by 2017 Hurricane Harvey.
Generating Mandarin and Cantonese F0 Contours with Decision Trees and BLSTMs
Authors: Weidong Yuan, Alan W. black
Date: 2018-07-04 00:00:00

This paper models the fundamental frequency contours on both Mandarin and
Cantonese speech with decision trees and DNNs (deep neural networks). Different
kinds of f0 representations and model architectures are tested for decision
trees and DNNs. A new model called Additive-BLSTM (additive bidirectional long
short term memory) that predicts a base f0 contour and a residual f0 contour
with two BLSTMs is proposed. With respect to objective measures of RMSE and
correlation, applying tone-dependent trees together with sample normalization
and delta feature regularization within decision tree framework performs best.
While the new Additive-BLSTM model with delta feature regularization performs
even better. Subjective listening tests on both Mandarin and Cantonese
comparing Random Forest model (multiple decision trees) and the Additive-BLSTM
model were also held and confirmed the advantage of the new model according to
the listeners' preference.
BCSAT : A Benchmark Corpus for Sentiment Analysis in Telugu Using Word-level Annotations
Authors: Sreekavitha Parupalli, Vijjini Anvesh Rao, Radhika Mamidi
Date: 2018-07-04 00:00:00
Proceeding: ACL 2018 7
Tasks: Sentiment Analysis

The presented work aims at generating a systematically annotated corpus that
can support the enhancement of sentiment analysis tasks in Telugu using
word-level sentiment annotations. From OntoSenseNet, we extracted 11,000
adjectives, 253 adverbs, 8483 verbs and sentiment annotation is being done by
language experts. We discuss the methodology followed for the polarity
annotations and validate the developed resource. This work aims at developing a
benchmark corpus, as an extension to SentiWordNet, and baseline accuracy for a
model where lexeme annotations are applied for sentiment predictions. The
fundamental aim of this paper is to validate and study the possibility of
utilizing machine learning algorithms, word-level sentiment annotations in the
task of automated sentiment identification. Furthermore, accuracy is improved
by annotating the bi-grams extracted from the target corpus.
Towards Automation of Sense-type Identification of Verbs in OntoSenseNet(Telugu)
Authors: Sreekavitha Parupalli, Vijjini Anvesh Rao, Radhika Mamidi
Date: 2018-07-04 00:00:00
Tasks: Vocal Bursts Type Prediction

In this paper, we discuss the enrichment of a manually developed resource of
Telugu lexicon, OntoSenseNet. OntoSenseNet is a ontological sense annotated
lexicon that marks each verb of Telugu with a primary and a secondary sense.
The area of research is relatively recent but has a large scope of development.
We provide an introductory work to enrich the OntoSenseNet to promote further
research in Telugu. Classifiers are adopted to learn the sense relevant
features of the words in the resource and also to automate the tagging of
sense-types for verbs. We perform a comparative analysis of different
classifiers applied on OntoSenseNet. The results of the experiment prove that
automated enrichment of the resource is effective using SVM classifiers and
Adaboost ensemble.
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
Authors: Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee
Date: 2018-07-04 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: continuous-control, Continuous Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Integrating model-free and model-based approaches in reinforcement learning has the potential to achieve the high performance of model-free algorithms with low sample complexity. However, this is difficult because an imperfect dynamics model can degrade the performance of the learning algorithm, and in sufficiently complex environments, the dynamics model will almost always be imperfect. As a result, a key challenge is to combine model-based approaches with model-free learning in such a way that errors in the model do not degrade performance. We propose stochastic ensemble value expansion (STEVE), a novel model-based technique that addresses this issue. By dynamically interpolating between model rollouts of various horizon lengths for each individual example, STEVE ensures that the model is only utilized when doing so does not introduce significant errors. Our approach outperforms model-free baselines on challenging continuous control benchmarks with an order-of-magnitude increase in sample efficiency, and in contrast to previous model-based approaches, performance does not degrade in complex environments.
Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization
Authors: Alexandre Laterre, Yunguan Fu, Mohamed Khalil Jabri, Alain-Sam Cohen, David Kas, Karl Hajjar, Torbjorn S. Dahl, Amine Kerkeni, Karim Beguir
Date: 2018-07-04 00:00:00
Tasks: Combinatorial Optimization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Adversarial self-play in two-player games has delivered impressive results
when used with reinforcement learning algorithms that combine deep neural
networks and tree search. Algorithms like AlphaZero and Expert Iteration learn
tabula-rasa, producing highly informative training data on the fly. However,
the self-play training strategy is not directly applicable to single-player
games. Recently, several practically important combinatorial optimisation
problems, such as the travelling salesman problem and the bin packing problem,
have been reformulated as reinforcement learning problems, increasing the
importance of enabling the benefits of self-play beyond two-player games. We
present the Ranked Reward (R2) algorithm which accomplishes this by ranking the
rewards obtained by a single agent over multiple games to create a relative
performance metric. Results from applying the R2 algorithm to instances of a
two-dimensional and three-dimensional bin packing problems show that it
outperforms generic Monte Carlo tree search, heuristic algorithms and integer
programming solvers. We also present an analysis of the ranked reward
mechanism, in particular, the effects of problem instances with varying
difficulty and different ranking thresholds.
Encoding Spatial Relations from Natural Language
Authors: Tiago Ramalho, Tomáš Kočiský, Frederic Besse, S. M. Ali Eslami, Gábor Melis, Fabio Viola, Phil Blunsom, Karl Moritz Hermann
Date: 2018-07-04 00:00:00
Tasks: Spatial Reasoning

Natural language processing has made significant inroads into learning the
semantics of words through distributional approaches, however representations
learnt via these methods fail to capture certain kinds of information implicit
in the real world. In particular, spatial relations are encoded in a way that
is inconsistent with human spatial reasoning and lacking invariance to
viewpoint changes. We present a system capable of capturing the semantics of
spatial relations such as behind, left of, etc from natural language. Our key
contributions are a novel multi-modal objective based on generating images of
scenes from their textual descriptions, and a new dataset on which to train it.
We demonstrate that internal representations are robust to meaning preserving
transformations of descriptions (paraphrase invariance), while viewpoint
invariance is an emergent property of the system.
MIXGAN: Learning Concepts from Different Domains for Mixture Generation
Authors: Guang-Yuan Hao, Hong-Xing Yu, Wei-Shi Zheng
Date: 2018-07-04 00:00:00
Tasks: Generative Adversarial Network, Translation

In this work, we present an interesting attempt on mixture generation:
absorbing different image concepts (e.g., content and style) from different
domains and thus generating a new domain with learned concepts. In particular,
we propose a mixture generative adversarial network (MIXGAN). MIXGAN learns
concepts of content and style from two domains respectively, and thus can join
them for mixture generation in a new domain, i.e., generating images with
content from one domain and style from another. MIXGAN overcomes the limitation
of current GAN-based models which either generate new images in the same domain
as they observed in training stage, or require off-the-shelf content templates
for transferring or translation. Extensive experimental results demonstrate the
effectiveness of MIXGAN as compared to related state-of-the-art GAN-based
models.
Latent Space Autoregression for Novelty Detection
Authors: Davide Abati, Angelo Porrello, Simone Calderara, Rita Cucchiara
Date: 2018-07-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Anomaly Detection, Novelty Detection, Video Anomaly Detection

Novelty detection is commonly referred to as the discrimination of
observations that do not conform to a learned model of regularity. Despite its
importance in different application settings, designing a novelty detector is
utterly complex due to the unpredictable nature of novelties and its
inaccessibility during the training procedure, factors which expose the
unsupervised nature of the problem. In our proposal, we design a general
framework where we equip a deep autoencoder with a parametric density estimator
that learns the probability distribution underlying its latent representations
through an autoregressive procedure. We show that a maximum likelihood
objective, optimized in conjunction with the reconstruction of normal samples,
effectively acts as a regularizer for the task at hand, by minimizing the
differential entropy of the distribution spanned by latent vectors. In addition
to providing a very general formulation, extensive experiments of our model on
publicly available datasets deliver on-par or superior performances if compared
to state-of-the-art methods in one-class and video anomaly detection settings.
Differently from prior works, our proposal does not make any assumption about
the nature of the novelties, making our work readily applicable to diverse
contexts.
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences
Authors: Borja Balle, Gilles Barthe, Marco Gaboardi
Date: 2018-07-04 00:00:00
Proceeding: NeurIPS 2018 12

Differential privacy comes equipped with multiple analytical tools for the
design of private data analyses. One important tool is the so-called "privacy
amplification by subsampling" principle, which ensures that a differentially
private mechanism run on a random subsample of a population provides higher
privacy guarantees than when run on the entire population. Several instances of
this principle have been studied for different random subsampling methods, each
with an ad-hoc analysis. In this paper we present a general method that
recovers and improves prior analyses, yields lower bounds and derives new
instances of privacy amplification by subsampling. Our method leverages a
characterization of differential privacy as a divergence which emerged in the
program verification community. Furthermore, it introduces new tools, including
advanced joint convexity and privacy profiles, which might be of independent
interest.
Neonatal Pain Expression Recognition Using Transfer Learning
Authors: Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Yu Sun
Date: 2018-07-04 00:00:00
Tasks: Face Recognition, General Classification, image-classification, Image Classification, Transfer Learning

Transfer learning using pre-trained Convolutional Neural Networks (CNNs) has
been successfully applied to images for different classification tasks. In this
paper, we propose a new pipeline for pain expression recognition in neonates
using transfer learning. Specifically, we propose to exploit a pre-trained CNN
that was originally trained on a relatively similar dataset for face
recognition (VGG Face) as well as CNNs that were pre-trained on a relatively
different dataset for image classification (iVGG F,M, and S) to extract deep
features from neonates' faces. In the final stage, several supervised machine
learning classifiers are trained to classify neonates' facial expression into
pain or no pain expression. The proposed pipeline achieved, on a testing
dataset, 0.841 AUC and 90.34 accuracy, which is approx. 7 higher than the
accuracy of handcrafted traditional features. We also propose to combine deep
features with traditional features and hypothesize that the mixed features
would improve pain classification performance. Combining deep features with
traditional features achieved 92.71 accuracy and 0.948 AUC. These results show
that transfer learning, which is a faster and more practical option than
training CNN from the scratch, can be used to extract useful features for pain
expression recognition in neonates. It also shows that combining deep features
with traditional handcrafted features is a good practice to improve the
performance of pain expression recognition and possibly the performance of
similar applications.
Using Reinforcement Learning with Partial Vehicle Detection for Intelligent Traffic Signal Control
Authors: Rusheng Zhang, Akihiro Ishikawa, Wenli Wang, Benjamin Striner, Ozan Tonguz
Date: 2018-07-04 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Traffic Signal Control, vehicle detection

Intelligent Transportation Systems (ITS) have attracted the attention of researchers and the general public alike as a means to alleviate traffic congestion. Recently, the maturity of wireless technology has enabled a cost-efficient way to achieve ITS by detecting vehicles using Vehicle to Infrastructure (V2I) communications. Traditional ITS algorithms, in most cases, assume that every vehicle is observed, such as by a camera or a loop detector, but a V2I implementation would detect only those vehicles with wireless communications capability. We examine a family of transportation systems, which we will refer to as `Partially Detected Intelligent Transportation Systems'. An algorithm that can act well under a small detection rate is highly desirable due to gradual penetration rates of the underlying wireless technologies such as Dedicated Short Range Communications (DSRC) technology. Artificial Intelligence (AI) techniques for Reinforcement Learning (RL) are suitable tools for finding such an algorithm due to utilizing varied inputs and not requiring explicit analytic understanding or modeling of the underlying system dynamics. In this paper, we report a RL algorithm for partially observable ITS based on DSRC. The performance of this system is studied under different car flows, detection rates, and topologies of the road network. Our system is able to efficiently reduce the average waiting time of vehicles at an intersection, even with a low detection rate.
Modeling outcomes of soccer matches
Authors: Alkeos Tsokos, Santhosh Narayanan, Ioannis Kosmidis, Gianluca Baio, Mihai Cucuringu, Gavin Whitaker, Franz J. Király
Date: 2018-07-04 00:00:00

We compare various extensions of the Bradley-Terry model and a hierarchical
Poisson log-linear model in terms of their performance in predicting the
outcome of soccer matches (win, draw, or loss). The parameters of the
Bradley-Terry extensions are estimated by maximizing the log-likelihood, or an
appropriately penalized version of it, while the posterior densities of the
parameters of the hierarchical Poisson log-linear model are approximated using
integrated nested Laplace approximations. The prediction performance of the
various modeling approaches is assessed using a novel, context-specific
framework for temporal validation that is found to deliver accurate estimates
of the test error. The direct modeling of outcomes via the various
Bradley-Terry extensions and the modeling of match scores using the
hierarchical Poisson log-linear model demonstrate similar behavior in terms of
predictive performance.
Neural Processes
Authors: Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami, Yee Whye Teh
Date: 2018-07-04 00:00:00

A neural network (NN) is a parameterised function that can be tuned via
gradient descent to approximate a labelled collection of data with high
precision. A Gaussian process (GP), on the other hand, is a probabilistic model
that defines a distribution over possible functions, and is updated in light of
data via the rules of probabilistic inference. GPs are probabilistic,
data-efficient and flexible, however they are also computationally intensive
and thus limited in their applicability. We introduce a class of neural latent
variable models which we call Neural Processes (NPs), combining the best of
both worlds. Like GPs, NPs define distributions over functions, are capable of
rapid adaptation to new observations, and can estimate the uncertainty in their
predictions. Like NNs, NPs are computationally efficient during training and
evaluation but also learn to adapt their priors to data. We demonstrate the
performance of NPs on a range of learning tasks, including regression and
optimisation, and compare and contrast with related models in the literature.
Ensemble learning with Conformal Predictors: Targeting credible predictions of conversion from Mild Cognitive Impairment to Alzheimer's Disease
Authors: Telma Pereira, Sandra Cardoso, Dina Silva, Manuela Guerreiro, Alexandre de Mendonça, Sara C. Madeira
Date: 2018-07-04 00:00:00
Tasks: BIG-bench Machine Learning, Ensemble Learning, General Classification

Most machine learning classifiers give predictions for new examples
accurately, yet without indicating how trustworthy predictions are. In the
medical domain, this hampers their integration in decision support systems,
which could be useful in the clinical practice. We use a supervised learning
approach that combines Ensemble learning with Conformal Predictors to predict
conversion from Mild Cognitive Impairment to Alzheimer's Disease. Our goal is
to enhance the classification performance (Ensemble learning) and complement
each prediction with a measure of credibility (Conformal Predictors). Our
results showed the superiority of the proposed approach over a similar ensemble
framework with standard classifiers.
Conditional Neural Processes
Authors: Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami
Date: 2018-07-04 00:00:00
Proceeding: ICML 2018 7
Tasks: Gaussian Processes

Deep neural networks excel at function approximation, yet they are typically
trained from scratch for each new function. On the other hand, Bayesian
methods, such as Gaussian Processes (GPs), exploit prior knowledge to quickly
infer the shape of a new function at test time. Yet GPs are computationally
expensive, and it can be hard to design appropriate priors. In this paper we
propose a family of neural models, Conditional Neural Processes (CNPs), that
combine the benefits of both. CNPs are inspired by the flexibility of
stochastic processes such as GPs, but are structured as neural networks and
trained via gradient descent. CNPs make accurate predictions after observing
only a handful of training data points, yet scale to complex functions and
large datasets. We demonstrate the performance and versatility of the approach
on a range of canonical machine learning tasks, including regression,
classification and image completion.
Sensors, SLAM and Long-term Autonomy: A Review
Authors: Mubariz Zaffar, Shoaib Ehsan, Rustam Stolkin, Klaus McDonald Maier
Date: 2018-07-04 00:00:00
Tasks: Simultaneous Localization and Mapping

Simultaneous Localization and Mapping, commonly known as SLAM, has been an
active research area in the field of Robotics over the past three decades. For
solving the SLAM problem, every robot is equipped with either a single sensor
or a combination of similar/different sensors. This paper attempts to review,
discuss, evaluate and compare these sensors. Keeping an eye on future, this
paper also assesses the characteristics of these sensors against factors
critical to the long-term autonomy challenge.