pwc-archive/papers-with-abstracts huggingface dataset:
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
Authors: Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei Wang
Date: 2017-05-22 00:00:00
Proceeding: ICLR 2018 1
Tasks: Information Retrieval, Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval

We frame Question Answering (QA) as a Reinforcement Learning task, an
approach that we call Active Question Answering. We propose an agent that sits
between the user and a black box QA system and learns to reformulate questions
to elicit the best possible answers. The agent probes the system with,
potentially many, natural language reformulations of an initial question and
aggregates the returned evidence to yield the best answer. The reformulation
system is trained end-to-end to maximize answer quality using policy gradient.
We evaluate on SearchQA, a dataset of complex questions extracted from
Jeopardy!. The agent outperforms a state-of-the-art base model, playing the
role of the environment, and other benchmarks. We also analyze the language
that the agent has learned while interacting with the question answering
system. We find that successful question reformulations look quite different
from natural language paraphrases. The agent is able to discover non-trivial
reformulation strategies that resemble classic information retrieval techniques
such as term re-weighting (tf-idf) and stemming.
Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs
Authors: Andres Milioto, Philipp Lottes, Cyrill Stachniss
Date: 2017-09-20 00:00:00
Tasks: Real-Time Semantic Segmentation, Semantic Segmentation

Precision farming robots, which target to reduce the amount of herbicides
that need to be brought out in the fields, must have the ability to identify
crops and weeds in real time to trigger weeding actions. In this paper, we
address the problem of CNN-based semantic segmentation of crop fields
separating sugar beet plants, weeds, and background solely based on RGB data.
We propose a CNN that exploits existing vegetation indexes and provides a
classification in real time. Furthermore, it can be effectively re-trained to
so far unseen fields with a comparably small amount of training data. We
implemented and thoroughly evaluated our system on a real agricultural robot
operating in different fields in Germany and Switzerland. The results show that
our system generalizes well, can operate at around 20Hz, and is suitable for
online operation in the fields.
Separators and Adjustment Sets in Causal Graphs: Complete Criteria and an Algorithmic Framework
Authors: Benito van der Zander, Maciej Liśkiewicz, Johannes Textor
Date: 2018-02-28 00:00:00

Principled reasoning about the identifiability of causal effects from
non-experimental data is an important application of graphical causal models.
This paper focuses on effects that are identifiable by covariate adjustment, a
commonly used estimation approach. We present an algorithmic framework for
efficiently testing, constructing, and enumerating $m$-separators in ancestral
graphs (AGs), a class of graphical causal models that can represent uncertainty
about the presence of latent confounders. Furthermore, we prove a reduction
from causal effect identification by covariate adjustment to $m$-separation in
a subgraph for directed acyclic graphs (DAGs) and maximal ancestral graphs
(MAGs). Jointly, these results yield constructive criteria that characterize
all adjustment sets as well as all minimal and minimum adjustment sets for
identification of a desired causal effect with multivariate exposures and
outcomes in the presence of latent confounding. Our results extend several
existing solutions for special cases of these problems. Our efficient
algorithms allowed us to empirically quantify the identifiability gap between
covariate adjustment and the do-calculus in random DAGs and MAGs, covering a
wide range of scenarios. Implementations of our algorithms are provided in the
R package dagitty.
DEMorphy, German Language Morphological Analyzer
Authors: Duygu Altinok
Date: 2018-03-02 00:00:00

DEMorphy is a morphological analyzer for German. It is built onto large,
compactified lexicons from German Morphological Dictionary. A guesser based on
German declension suffixed is also provided. For German, we provided a
state-of-art morphological analyzer. DEMorphy is implemented in Python with
ease of usability and accompanying documentation. The package is suitable for
both academic and commercial purposes wit a permissive licence.
Impact of Biases in Big Data
Authors: Patrick Glauner, Petko Valtchev, Radu State
Date: 2018-03-02 00:00:00
Tasks: BIG-bench Machine Learning

The underlying paradigm of big data-driven machine learning reflects the
desire of deriving better conclusions from simply analyzing more data, without
the necessity of looking at theory and models. Is having simply more data
always helpful? In 1936, The Literary Digest collected 2.3M filled in
questionnaires to predict the outcome of that year's US presidential election.
The outcome of this big data prediction proved to be entirely wrong, whereas
George Gallup only needed 3K handpicked people to make an accurate prediction.
Generally, biases occur in machine learning whenever the distributions of
training set and test set are different. In this work, we provide a review of
different sorts of biases in (big) data sets in machine learning. We provide
definitions and discussions of the most commonly appearing biases in machine
learning: class imbalance and covariate shift. We also show how these biases
can be quantified and corrected. This work is an introductory text for both
researchers and practitioners to become more aware of this topic and thus to
derive more reliable models for their learning problems.
Probabilistic design of a molybdenum-base alloy using a neural network
Authors: B. D. Conduit, N. G. Jones, H. J. Stone, G. J. Conduit
Date: 2018-03-02 00:00:00

An artificial intelligence tool is exploited to discover and characterize a
new molybdenum-base alloy that is the most likely to simultaneously satisfy
targets of cost, phase stability, precipitate content, yield stress, and
hardness. Experimental testing demonstrates that the proposed alloy fulfils the
computational predictions, and furthermore the physical properties exceed those
of other commercially available Mo-base alloys for forging-die applications.
Consequentialist conditional cooperation in social dilemmas with imperfect information
Authors: Alexander Peysakhovich, Adam Lerer
Date: 2017-10-19 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, Reinforcement Learning

Social dilemmas, where mutual cooperation can lead to high payoffs but
participants face incentives to cheat, are ubiquitous in multi-agent
interaction. We wish to construct agents that cooperate with pure cooperators,
avoid exploitation by pure defectors, and incentivize cooperation from the
rest. However, often the actions taken by a partner are (partially) unobserved
or the consequences of individual actions are hard to predict. We show that in
a large class of games good strategies can be constructed by conditioning one's
behavior solely on outcomes (ie. one's past rewards). We call this
consequentialist conditional cooperation. We show how to construct such
strategies using deep reinforcement learning techniques and demonstrate, both
analytically and experimentally, that they are effective in social dilemmas
beyond simple matrix games. We also show the limitations of relying purely on
consequences and discuss the need for understanding both the consequences of
and the intentions behind an action.
Generative Statistical Models with Self-Emergent Grammar of Chord Sequences
Authors: Hiroaki Tsushima, Eita Nakamura, Katsutoshi Itoyama, Kazuyoshi Yoshii
Date: 2017-08-07 00:00:00

Generative statistical models of chord sequences play crucial roles in music
processing. To capture syntactic similarities among certain chords (e.g. in C
major key, between G and G7 and between F and Dm), we study hidden Markov
models and probabilistic context-free grammar models with latent variables
describing syntactic categories of chord symbols and their unsupervised
learning techniques for inducing the latent grammar from data. Surprisingly, we
find that these models often outperform conventional Markov models in
predictive power, and the self-emergent categories often correspond to
traditional harmonic functions. This implies the need for chord categories in
harmony models from the informatics perspective.
Syntax-Aware Language Modeling with Recurrent Neural Networks
Authors: Duncan Blythe, Alan Akbik, Roland Vollgraf
Date: 2018-03-02 00:00:00
Tasks: Language Modeling, Language Modelling

Neural language models (LMs) are typically trained using only lexical
features, such as surface forms of words. In this paper, we argue this deprives
the LM of crucial syntactic signals that can be detected at high confidence
using existing parsers. We present a simple but highly effective approach for
training neural LMs using both lexical and syntactic information, and a novel
approach for applying such LMs to unparsed text using sequential Monte Carlo
sampling. In experiments on a range of corpora and corpus sizes, we show our
approach consistently outperforms standard lexical LMs in character-level
language modeling; on the other hand, for word-level models the models are on a
par with standard language models. These results indicate potential for
expanding LMs beyond lexical surface features to higher-level NLP features for
character-level models.
Maintaining cooperation in complex social dilemmas using deep reinforcement learning
Authors: Adam Lerer, Alexander Peysakhovich
Date: 2017-07-04 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Social dilemmas are situations where individuals face a temptation to
increase their payoffs at a cost to total welfare. Building artificially
intelligent agents that achieve good outcomes in these situations is important
because many real world interactions include a tension between selfish
interests and the welfare of others. We show how to modify modern reinforcement
learning methods to construct agents that act in ways that are simple to
understand, nice (begin by cooperating), provokable (try to avoid being
exploited), and forgiving (try to return to mutual cooperation). We show both
theoretically and experimentally that such agents can maintain cooperation in
Markov social dilemmas. Our construction does not require training methods
beyond a modification of self-play, thus if an environment is such that good
strategies can be constructed in the zero-sum case (eg. Atari) then we can
construct agents that solve social dilemmas in this environment.
Can we steal your vocal identity from the Internet?: Initial investigation of cloning Obama's voice using GAN, WaveNet and low-quality found data
Authors: Jaime Lorenzo-Trueba, Fuming Fang, Xin Wang, Isao Echizen, Junichi Yamagishi, Tomi Kinnunen
Date: 2018-03-02 00:00:00
Tasks: Generative Adversarial Network, Speech Enhancement, Speech Synthesis, text-to-speech, Text to Speech, Voice Conversion

Thanks to the growing availability of spoofing databases and rapid advances
in using them, systems for detecting voice spoofing attacks are becoming more
and more capable, and error rates close to zero are being reached for the
ASVspoof2015 database. However, speech synthesis and voice conversion paradigms
that are not considered in the ASVspoof2015 database are appearing. Such
examples include direct waveform modelling and generative adversarial networks.
We also need to investigate the feasibility of training spoofing systems using
only low-quality found data. For that purpose, we developed a generative
adversarial network-based speech enhancement system that improves the quality
of speech data found in publicly available sources. Using the enhanced data, we
trained state-of-the-art text-to-speech and voice conversion models and
evaluated them in terms of perceptual speech quality and speaker similarity.
The results show that the enhancement models significantly improved the SNR of
low-quality degraded data found in publicly available sources and that they
significantly improved the perceptual cleanliness of the source speech without
significantly degrading the naturalness of the voice. However, the results also
show limitations when generating speech with the low-quality found data.
Quantum distance-based classifier with constant size memory, distributed knowledge and state recycling
Authors: Przemysław Sadowski
Date: 2018-03-02 00:00:00
Tasks: Classification, General Classification

In this work we examine recently proposed distance-based classification
method designed for near-term quantum processing units with limited resources.
We further study possibilities to reduce the quantum resources without any
efficiency decrease. We show that only a part of the information undergoes
coherent evolution and this fact allows us to introduce an algorithm with
significantly reduced quantum memory size. Additionally, considering only
partial information at a time, we propose a classification protocol with
information distributed among a number of agents. Finally, we show that the
information evolution during a measurement can lead to a better solution and
that accuracy of the algorithm can be improved by harnessing the state after
the final measurement.
Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding
Authors: Anjishnu Kumar, Arpit Gupta, Julian Chan, Sam Tucker, Bjorn Hoffmeister, Markus Dreyer, Stanislav Peshterliev, Ankur Gandhe, Denis Filiminov, Ariya Rastrow, Christian Monson, Agnika Kumar
Date: 2017-11-01 00:00:00
Tasks: Spoken Language Understanding

This paper presents the design of the machine learning architecture that
underlies the Alexa Skills Kit (ASK) a large scale Spoken Language
Understanding (SLU) Software Development Kit (SDK) that enables developers to
extend the capabilities of Amazon's virtual assistant, Alexa. At Amazon, the
infrastructure powers over 25,000 skills deployed through the ASK, as well as
AWS's Amazon Lex SLU Service. The ASK emphasizes flexibility, predictability
and a rapid iteration cycle for third party developers. It imposes inductive
biases that allow it to learn robust SLU models from extremely small and sparse
datasets and, in doing so, removes significant barriers to entry for software
developers and dialogue systems researchers.
Long-term Visual Localization using Semantically Segmented Images
Authors: Erik Stenborg, Carl Toft, Lars Hammarstrand
Date: 2018-01-16 00:00:00
Tasks: Autonomous Vehicles, Descriptive, Semantic Segmentation, Visual Localization, Visual Navigation

Robust cross-seasonal localization is one of the major challenges in
long-term visual navigation of autonomous vehicles. In this paper, we exploit
recent advances in semantic segmentation of images, i.e., where each pixel is
assigned a label related to the type of object it represents, to attack the
problem of long-term visual localization. We show that semantically labeled 3-D
point maps of the environment, together with semantically segmented images, can
be efficiently used for vehicle localization without the need for detailed
feature descriptors (SIFT, SURF, etc.). Thus, instead of depending on
hand-crafted feature descriptors, we rely on the training of an image
segmenter. The resulting map takes up much less storage space compared to a
traditional descriptor based map. A particle filter based semantic localization
solution is compared to one based on SIFT-features, and even with large
seasonal variations over the year we perform on par with the larger and more
descriptive SIFT-features, and are able to localize with an error below 1 m
most of the time.
Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares
Authors: Rong Zhu
Date: 2018-03-02 00:00:00
Proceeding: NeurIPS 2016 12

In modern data analysis, random sampling is an efficient and widely-used
strategy to overcome the computational difficulties brought by large sample
size. In previous studies, researchers conducted random sampling which is
according to the input data but independent on the response variable, however
the response variable may also be informative for sampling. In this paper we
propose an adaptive sampling called the gradient-based sampling which is
dependent on both the input data and the output for fast solving of
least-square (LS) problems. We draw the data points by random sampling from the
full data according to their gradient values. This sampling is computationally
saving, since the running time of computing the sampling probabilities is
reduced to O(nd) where n is the full sample size and d is the dimension of the
input. Theoretically, we establish an error bound analysis of the general
importance sampling with respect to LS solution from full data. The result
establishes an improved performance of the use of our gradient- based sampling.
Synthetic and real data sets are used to empirically argue that the
gradient-based sampling has an obvious advantage over existing sampling methods
from two aspects of statistical efficiency and computational saving.
Pose-Robust Face Recognition via Deep Residual Equivariant Mapping
Authors: Kaidi Cao, Yu Rong, Cheng Li, Xiaoou Tang, Chen Change Loy
Date: 2018-03-02 00:00:00
Proceeding: CVPR 2018 6
Tasks: Face Identification, Face Recognition, Face Verification, Robust Face Recognition

Face recognition achieves exceptional success thanks to the emergence of deep
learning. However, many contemporary face recognition models still perform
relatively poor in processing profile faces compared to frontal faces. A key
reason is that the number of frontal and profile training faces are highly
imbalanced - there are extensively more frontal training samples compared to
profile ones. In addition, it is intrinsically hard to learn a deep
representation that is geometrically invariant to large pose variations. In
this study, we hypothesize that there is an inherent mapping between frontal
and profile faces, and consequently, their discrepancy in the deep
representation space can be bridged by an equivariant mapping. To exploit this
mapping, we formulate a novel Deep Residual EquivAriant Mapping (DREAM) block,
which is capable of adaptively adding residuals to the input deep
representation to transform a profile face representation to a canonical pose
that simplifies recognition. The DREAM block consistently enhances the
performance of profile face recognition for many strong deep networks,
including ResNet models, without deliberately augmenting training data of
profile faces. The block is easy to use, light-weight, and can be implemented
with a negligible computational overhead.
A multi-instance deep neural network classifier: application to Higgs boson CP measurement
Authors: P. Bialas, D. Nemeth, E. Richter-Wąs
Date: 2018-03-02 00:00:00
Tasks: General Classification

We investigate properties of a classifier applied to the measurements of the
CP state of the Higgs boson in $H\rightarrow\tau\tau$ decays. The problem is
framed as binary classifier applied to individual instances. Then the prior
knowledge that the instances belong to the same class is used to define the
multi-instance classifier. Its final score is calculated as multiplication of
single instance scores for a given series of instances. In the paper we discuss
properties of such classifier, notably its dependence on the number of
instances in the series. This classifier exhibits very strong random dependence
on the number of epochs used for training and requires careful tuning of the
classification threshold. We derive formula for this optimal threshold.
Towards a Question Answering System over the Semantic Web
Authors: Dennis Diefenbach, Andreas Both, Kamal Singh, Pierre Maret
Date: 2018-03-02 00:00:00
Tasks: Question Answering

Thanks to the development of the Semantic Web, a lot of new structured data
has become available on the Web in the form of knowledge bases (KBs). Making
this valuable data accessible and usable for end-users is one of the main goals
of Question Answering (QA) over KBs. Most current QA systems query one KB, in
one language (namely English). The existing approaches are not designed to be
easily adaptable to new KBs and languages. We first introduce a new approach
for translating natural language questions to SPARQL queries. It is able to
query several KBs simultaneously, in different languages, and can easily be
ported to other KBs and languages. In our evaluation, the impact of our
approach is proven using 5 different well-known and large KBs: Wikidata,
DBpedia, MusicBrainz, DBLP and Freebase as well as 5 different languages namely
English, German, French, Italian and Spanish. Second, we show how we integrated
our approach, to make it easily accessible by the research community and by
end-users. To summarize, we provided a conceptional solution for multilingual,
KB-agnostic Question Answering over the Semantic Web. The provided first
approximation validates this concept.
Lexico-acoustic Neural-based Models for Dialog Act Classification
Authors: Daniel Ortega, Ngoc Thang Vu
Date: 2018-03-02 00:00:00
Tasks: Classification, Dialog Act Classification, General Classification

Recent works have proposed neural models for dialog act classification in
spoken dialogs. However, they have not explored the role and the usefulness of
acoustic information. We propose a neural model that processes both lexical and
acoustic features for classification. Our results on two benchmark datasets
reveal that acoustic features are helpful in improving the overall accuracy.
Finally, a deeper analysis shows that acoustic features are valuable in three
cases: when a dialog act has sufficient data, when lexical information is
limited and when strong lexical cues are not present.
Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift
Authors: Ruijia Xu, Ziliang Chen, WangMeng Zuo, Junjie Yan, Liang Lin
Date: 2018-03-02 00:00:00
Proceeding: CVPR 2018 6
Tasks: Domain Adaptation, Multi-Source Unsupervised Domain Adaptation, Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) conventionally assumes labeled source
samples coming from a single underlying source distribution. Whereas in
practical scenario, labeled data are typically collected from diverse sources.
The multiple sources are different not only from the target but also from each
other, thus, domain adaptater should not be modeled in the same way. Moreover,
those sources may not completely share their categories, which further brings a
new transfer challenge called category shift. In this paper, we propose a deep
cocktail network (DCTN) to battle the domain and category shifts among multiple
sources. Motivated by the theoretical results in \cite{mansour2009domain}, the
target distribution can be represented as the weighted combination of source
distributions, and, the multi-source unsupervised domain adaptation via DCTN is
then performed as two alternating steps: i) It deploys multi-way adversarial
learning to minimize the discrepancy between the target and each of the
multiple source domains, which also obtains the source-specific perplexity
scores to denote the possibilities that a target sample belongs to different
source domains. ii) The multi-source category classifiers are integrated with
the perplexity scores to classify target sample, and the pseudo-labeled target
samples together with source samples are utilized to update the multi-source
category classifier and the feature extractor. We evaluate DCTN in three domain
adaptation benchmarks, which clearly demonstrate the superiority of our
framework.