pwc-archive/papers-with-abstracts huggingface dataset:
Towards Understanding Linear Word Analogies
Authors: Kawin Ethayarajh, David Duvenaud, Graeme Hirst
Date: 2018-10-11 00:00:00
Proceeding: ACL 2019 7

A surprising property of word vectors is that word analogies can often be solved with vector arithmetic. However, it is unclear why arithmetic operators correspond to non-linear embedding models such as skip-gram with negative sampling (SGNS). We provide a formal explanation of this phenomenon without making the strong assumptions that past theories have made about the vector space and word distribution. Our theory has several implications. Past work has conjectured that linear substructures exist in vector spaces because relations can be represented as ratios; we prove that this holds for SGNS. We provide novel justification for the addition of SGNS word vectors by showing that it automatically down-weights the more frequent word, as weighting schemes do ad hoc. Lastly, we offer an information theoretic interpretation of Euclidean distance in vector spaces, justifying its use in capturing word dissimilarity.
Monitoring spatial sustainable development: Semi-automated analysis of satellite and aerial images for energy transition and sustainability indicators
Authors: R. L. Curier, T. J. A. De Jong, Katharina Strauch, Katharina Cramer, Natalie Rosenski, Clara Schartner, M. Debusschere, Hannah Ziemons, Deniz Iren, Stefano Bromuri
Date: 2018-10-11 00:00:00

Solar panels are installed by a large and growing number of households due to
the convenience of having cheap and renewable energy to power house appliances.
In contrast to other energy sources solar installations are distributed very
decentralized and spread over hundred-thousands of locations. On a global level
more than 25% of solar photovoltaic (PV) installations were decentralized. The
effect of the quick energy transition from a carbon based economy to a green
economy is though still very difficult to quantify. As a matter of fact the
quick adoption of solar panels by households is difficult to track, with local
registries that miss a large number of the newly built solar panels. This makes
the task of assessing the impact of renewable energies an impossible task.
Although models of the output of a region exist, they are often black box
estimations. This project's aim is twofold: First automate the process to
extract the location of solar panels from aerial or satellite images and
second, produce a map of solar panels along with statistics on the number of
solar panels. Further, this project takes place in a wider framework which
investigates how official statistics can benefit from new digital data sources.
At project completion, a method for detecting solar panels from aerial images
via machine learning will be developed and the methodology initially developed
for BE, DE and NL will be standardized for application to other EU countries.
In practice, machine learning techniques are used to identify solar panels in
satellite and aerial images for the province of Limburg (NL), Flanders (BE) and
North Rhine-Westphalia (DE).
Generating Shared Latent Variables for Robots to Imitate Human Movements and Understand their Physical Limitations
Authors: Maxime Devanne, Sao Mai Nguyen
Date: 2018-10-11 00:00:00

Assistive robotics and particularly robot coaches may be very helpful for rehabilitation healthcare. In this context, we propose a method based on Gaussian Process Latent Variable Model (GP-LVM) to transfer knowledge between a physiotherapist, a robot coach and a patient. Our model is able to map visual human body features to robot data in order to facilitate the robot learning and imitation. In addition , we propose to extend the model to adapt robots' understanding to patient's physical limitations during the assessment of rehabilitation exercises. Experimental evaluation demonstrates promising results for both robot imitation and model adaptation according to the patients' limitations.
Learning a Set of Interrelated Tasks by Using Sequences of Motor Policies for a Strategic Intrinsically Motivated Learner
Authors: Nicolas Duminy, Sao Mai Nguyen, Dominique Duhaut
Date: 2018-10-11 00:00:00
Tasks: Active Learning

We propose an active learning architecture for robots, capable of organizing
its learning process to achieve a field of complex tasks by learning sequences
of motor policies, called Intrinsically Motivated Procedure Babbling (IM-PB).
The learner can generalize over its experience to continuously learn new tasks.
It chooses actively what and how to learn based by empirical measures of its
own progress. In this paper, we are considering the learning of a set of
interrelated tasks outcomes hierarchically organized. We introduce a framework
called 'procedures', which are sequences of policies defined by the combination
of previously learned skills. Our algorithmic architecture uses the procedures
to autonomously discover how to combine simple skills to achieve complex goals.
It actively chooses between 2 strategies of goal-directed exploration :
exploration of the policy space or the procedural space. We show on a simulated
environment that our new architecture is capable of tackling the learning of
complex motor policies, to adapt the complexity of its policies to the task at
hand. We also show that our 'procedures' framework helps the learner to tackle
difficult hierarchical tasks.
Deep Bi-Dense Networks for Image Super-Resolution
Authors: Yucheng Wang, Jialiang Shen, Jian Zhang
Date: 2018-10-11 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

This paper proposes Deep Bi-Dense Networks (DBDN) for single image
super-resolution. Our approach extends previous intra-block dense connection
approaches by including novel inter-block dense connections. In this way,
feature information propagates from a single dense block to all subsequent
blocks, instead of to a single successor. To build a DBDN, we firstly construct
intra-dense blocks, which extract and compress abundant local features via
densely connected convolutional layers and compression layers for further
feature learning. Then, we use an inter-block dense net to connect intra-dense
blocks, which allow each intra-dense block propagates its own local features to
all successors. Additionally, our bi-dense construction connects each block to
the output, alleviating the vanishing gradient problems in training. The
evaluation of our proposed method on five benchmark datasets shows that our
DBDN outperforms the state of the art in SISR with a moderate number of network
parameters.
A Data-Efficient Framework for Training and Sim-to-Real Transfer of Navigation Policies
Authors: Homanga Bharadhwaj, Zihan Wang, Yoshua Bengio, Liam Paull
Date: 2018-10-11 00:00:00
Tasks: Meta-Learning

Learning effective visuomotor policies for robots purely from data is
challenging, but also appealing since a learning-based system should not
require manual tuning or calibration. In the case of a robot operating in a
real environment the training process can be costly, time-consuming, and even
dangerous since failures are common at the start of training. For this reason,
it is desirable to be able to leverage \textit{simulation} and
\textit{off-policy} data to the extent possible to train the robot. In this
work, we introduce a robust framework that plans in simulation and transfers
well to the real environment. Our model incorporates a gradient-descent based
planning module, which, given the initial image and goal image, encodes the
images to a lower dimensional latent state and plans a trajectory to reach the
goal. The model, consisting of the encoder and planner modules, is trained
through a meta-learning strategy in simulation first. We subsequently perform
adversarial domain transfer on the encoder by using a bank of unlabelled but
random images from the simulation and real environments to enable the encoder
to map images from the real and simulated environments to a similarly
distributed latent representation. By fine tuning the entire model (encoder +
planner) with far fewer real world expert demonstrations, we show successful
planning performances in different navigation tasks.
Sequence-to-Sequence Models for Data-to-Text Natural Language Generation: Word- vs. Character-based Processing and Output Diversity
Authors: Glorianna Jagfeld, Sabrina Jenne, Ngoc Thang Vu
Date: 2018-10-11 00:00:00
Proceeding: WS 2018 11
Tasks: Diversity, Text Generation

We present a comparison of word-based and character-based
sequence-to-sequence models for data-to-text natural language generation, which
generate natural language descriptions for structured inputs. On the datasets
of two recent generation challenges, our models achieve comparable or better
automatic evaluation results than the best challenge submissions. Subsequent
detailed statistical and human analyses shed light on the differences between
the two input representations and the diversity of the generated texts. In a
controlled experiment with synthetic training data generated from templates, we
demonstrate the ability of neural models to learn novel combinations of the
templates and thereby generalize beyond the linguistic structures they were
trained on.
Classification using margin pursuit
Authors: Matthew J. Holland
Date: 2018-10-11 00:00:00
Tasks: Classification, General Classification

In this work, we study a new approach to optimizing the margin distribution
realized by binary classifiers. The classical approach to this problem is
simply maximization of the expected margin, while more recent proposals
consider simultaneous variance control and proxy objectives based on robust
location estimates, in the vein of keeping the margin distribution sharply
concentrated in a desirable region. While conceptually appealing, these new
approaches are often computationally unwieldy, and theoretical guarantees are
limited. Given this context, we propose an algorithm which searches the
hypothesis space in such a way that a pre-set "margin level" ends up being a
distribution-robust estimator of the margin location. This procedure is easily
implemented using gradient descent, and admits finite-sample bounds on the
excess risk under unbounded inputs. Empirical tests on real-world benchmark
data reinforce the basic principles highlighted by the theory, and are
suggestive of a promising new technique for classification.
Policy Design for Active Sequential Hypothesis Testing using Deep Learning
Authors: Dhruva Kartik, Ekraam Sabir, Urbashi Mitra, Prem Natarajan
Date: 2018-10-11 00:00:00
Tasks: Deep Learning, Deep Reinforcement Learning, Reinforcement Learning, Two-sample testing

Information theory has been very successful in obtaining performance limits
for various problems such as communication, compression and hypothesis testing.
Likewise, stochastic control theory provides a characterization of optimal
policies for Partially Observable Markov Decision Processes (POMDPs) using
dynamic programming. However, finding optimal policies for these problems is
computationally hard in general and thus, heuristic solutions are employed in
practice. Deep learning can be used as a tool for designing better heuristics
in such problems. In this paper, the problem of active sequential hypothesis
testing is considered. The goal is to design a policy that can reliably infer
the true hypothesis using as few samples as possible by adaptively selecting
appropriate queries. This problem can be modeled as a POMDP and bounds on its
value function exist in literature. However, optimal policies have not been
identified and various heuristics are used. In this paper, two new heuristics
are proposed: one based on deep reinforcement learning and another based on a
KL-divergence zero-sum game. These heuristics are compared with
state-of-the-art solutions and it is demonstrated using numerical experiments
that the proposed heuristics can achieve significantly better performance than
existing methods in some scenarios.
PANDA: AdaPtive Noisy Data Augmentation for Regularization of Undirected Graphical Models
Authors: Yi-Nan Li, Xiao Liu, Fang Liu
Date: 2018-10-11 00:00:00
Tasks: Data Augmentation, Variable Selection

We propose an AdaPtive Noise Augmentation (PANDA) technique to regularize the estimation and construction of undirected graphical models. PANDA iteratively optimizes the objective function given the noise augmented data until convergence to achieve regularization on model parameters. The augmented noises can be designed to achieve various regularization effects on graph estimation, such as the bridge (including lasso and ridge), elastic net, adaptive lasso, and SCAD penalization; it also realizes the group lasso and fused ridge. We examine the tail bound of the noise-augmented loss function and establish that the noise-augmented loss function and its minimizer converge almost surely to the expected penalized loss function and its minimizer, respectively. We derive the asymptotic distributions for the regularized parameters through PANDA in generalized linear models, based on which, inferences for the parameters can be obtained simultaneously with variable selection. We show the non-inferior performance of PANDA in constructing graphs of different types in simulation studies and apply PANDA to an autism spectrum disorder data to construct a mixed-node graph. We also show that the inferences based on the asymptotic distribution of regularized parameter estimates via PANDA achieve nominal or near-nominal coverage and are far more efficient, compared to some existing post-selection procedures. Computationally, PANDA can be easily programmed in software that implements (GLMs) without resorting to complicated optimization techniques.
Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviour
Authors: Sandeep Mathias, Diptesh Kanojia, Kevin Patel, Samarth Agarwal, Abhijit Mishra, Pushpak Bhattacharyya
Date: 2018-10-11 00:00:00
Proceeding: ACL 2018 7

Predicting a reader's rating of text quality is a challenging task that
involves estimating different subjective aspects of the text, like structure,
clarity, etc. Such subjective aspects are better handled using cognitive
information. One such source of cognitive information is gaze behaviour. In
this paper, we show that gaze behaviour does indeed help in effectively
predicting the rating of text quality. To do this, we first model text quality
as a function of three properties - organization, coherence and cohesion. Then,
we demonstrate how capturing gaze behaviour helps in predicting each of these
properties, and hence the overall quality, by reporting improvements obtained
by adding gaze features to traditional textual features for score prediction.
We also hypothesize that if a reader has fully understood the text, the
corresponding gaze behaviour would give a better indication of the assigned
rating, as opposed to partial understanding. Our experiments validate this
hypothesis by showing greater agreement between the given rating and the
predicted rating when the reader has a full understanding of the text.
Towards the Targeted Environment-Specific Evolution of Robot Components
Authors: Jack Collins, Wade Geles, David Howard, Frederic Maire
Date: 2018-10-11 00:00:00

This research considers the task of evolving the physical structure of a
robot to enhance its performance in various environments, which is a
significant problem in the field of Evolutionary Robotics. Inspired by the
fields of evolutionary art and sculpture, we evolve only targeted parts of a
robot, which simplifies the optimisation problem compared to traditional
approaches that must simultaneously evolve both (actuated) body and brain.
Exploration fidelity is emphasised in areas of the robot most likely to benefit
from shape optimisation, whilst exploiting existing robot structure and
control. Our approach uses a Genetic Algorithm to optimise collections of
Bezier splines that together define the shape of a legged robot's tibia, and
leg performance is evaluated in parallel in a high-fidelity simulator. The leg
is represented in the simulator as 3D-printable file, and as such can be
readily instantiated in reality. Provisional experiments in three distinct
environments show the evolution of environment-specific leg structures that are
both high-performing and notably different to those evolved in the other
environments. This proof-of-concept represents an important step towards the
environment-dependent optimisation of performance-critical components for a
range of ubiquitous, standard, and already-capable robots that can carry out a
wide variety of tasks.
VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking
Authors: Quan Wang, Hannah Muckenhirn, Kevin Wilson, Prashant Sridhar, Zelin Wu, John Hershey, Rif A. Saurous, Ron J. Weiss, Ye Jia, Ignacio Lopez Moreno
Date: 2018-10-11 00:00:00
Tasks: Speaker Recognition, Speaker Separation, Speech Enhancement, speech-recognition, Speech Recognition

In this paper, we present a novel system that separates the voice of a target speaker from multi-speaker signals, by making use of a reference signal from the target speaker. We achieve this by training two separate neural networks: (1) A speaker recognition network that produces speaker-discriminative embeddings; (2) A spectrogram masking network that takes both noisy spectrogram and speaker embedding as input, and produces a mask. Our system significantly reduces the speech recognition WER on multi-speaker signals, with minimal WER degradation on single-speaker signals.
A Blended Deep Learning Approach for Predicting User Intended Actions
Authors: Fei Tan, Zhi Wei, Jun He, Xiang Wu, Bo Peng, Haoran Liu, Zhenyu Yan
Date: 2018-10-11 00:00:00
Tasks: Deep Learning

User intended actions are widely seen in many areas. Forecasting these
actions and taking proactive measures to optimize business outcome is a crucial
step towards sustaining the steady business growth. In this work, we focus on
pre- dicting attrition, which is one of typical user intended actions.
Conventional attrition predictive modeling strategies suffer a few inherent
drawbacks. To overcome these limitations, we propose a novel end-to-end
learning scheme to keep track of the evolution of attrition patterns for the
predictive modeling. It integrates user activity logs, dynamic and static user
profiles based on multi-path learning. It exploits historical user records by
establishing a decaying multi-snapshot technique. And finally it employs the
precedent user intentions via guiding them to the subsequent learning
procedure. As a result, it addresses all disadvantages of conventional methods.
We evaluate our methodology on two public data repositories and one private
user usage dataset provided by Adobe Creative Cloud. The extensive experiments
demonstrate that it can offer the appealing performance in comparison with
several existing approaches as rated by different popular metrics. Furthermore,
we introduce an advanced interpretation and visualization strategy to
effectively characterize the periodicity of user activity logs. It can help to
pinpoint important factors that are critical to user attrition and retention
and thus suggests actionable improvement targets for business practice. Our
work will provide useful insights into the prediction and elucidation of other
user intended actions as well.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Authors: Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova
Date: 2018-10-11 00:00:00
Proceeding: NAACL 2019 6
Tasks: Citation Intent Classification, Common Sense Reasoning, Conversational Response Selection, Coreference Resolution, Cross-Lingual Natural Language Inference, Emotion Recognition in Conversation, Linear-Probe Classification, Linguistic Acceptability, Multimodal Intent Recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Inference, Natural Language Understanding, Paraphrase Identification, Question Answering, Semantic Textual Similarity, Sentence Classification, Sentiment Analysis, Stock Market Prediction, Text Classification, Type prediction

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).
Applications of Graph Integration to Function Comparison and Malware Classification
Authors: Michael A. Slawinski, Andy Wortman
Date: 2018-10-11 00:00:00
Tasks: Classification, General Classification, Malware Classification

We classify .NET files as either benign or malicious by examining directed graphs derived from the set of functions comprising the given file. Each graph is viewed probabilistically as a Markov chain where each node represents a code block of the corresponding function, and by computing the PageRank vector (Perron vector with transport), a probability measure can be defined over the nodes of the given graph. Each graph is vectorized by computing Lebesgue antiderivatives of hand-engineered functions defined on the vertex set of the given graph against the PageRank measure. Files are subsequently vectorized by aggregating the set of vectors corresponding to the set of graphs resulting from decompiling the given file. The result is a fast, intuitive, and easy-to-compute glass-box vectorization scheme, which can be leveraged for training a standalone classifier or to augment an existing feature space. We refer to this vectorization technique as PageRank Measure Integration Vectorization (PMIV). We demonstrate the efficacy of PMIV by training a vanilla random forest on 2.5 million samples of decompiled .NET, evenly split between benign and malicious, from our in-house corpus and compare this model to a baseline model which leverages a text-only feature space. The median time needed for decompilation and scoring was 24ms.
Offline Multi-Action Policy Learning: Generalization and Optimization
Authors: Zhengyuan Zhou, Susan Athey, Stefan Wager
Date: 2018-10-10 00:00:00

In many settings, a decision-maker wishes to learn a rule, or policy, that
maps from observable characteristics of an individual to an action. Examples
include selecting offers, prices, advertisements, or emails to send to
consumers, as well as the problem of determining which medication to prescribe
to a patient. While there is a growing body of literature devoted to this
problem, most existing results are focused on the case where data comes from a
randomized experiment, and further, there are only two possible actions, such
as giving a drug to a patient or not. In this paper, we study the offline
multi-action policy learning problem with observational data and where the
policy may need to respect budget constraints or belong to a restricted policy
class such as decision trees. We build on the theory of efficient
semi-parametric inference in order to propose and implement a policy learning
algorithm that achieves asymptotically minimax-optimal regret. To the best of
our knowledge, this is the first result of this type in the multi-action setup,
and it provides a substantial performance improvement over the existing
learning algorithms. We then consider additional computational challenges that
arise in implementing our method for the case where the policy is restricted to
take the form of a decision tree. We propose two different approaches, one
using a mixed integer program formulation and the other using a tree-search
based algorithm.