pwc-archive/papers-with-abstracts huggingface dataset:
Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data
Authors: Shikhar Sharma, Jing He, Kaheer Suleman, Hannes Schulz, Philip Bachman
Date: 2016-06-11 00:00:00
Tasks: Decoder, Sentence, Spoken Dialogue Systems, Text Generation

Natural language generation plays a critical role in spoken dialogue systems.
We present a new approach to natural language generation for task-oriented
dialogue using recurrent neural networks in an encoder-decoder framework. In
contrast to previous work, our model uses both lexicalized and delexicalized
components i.e. slot-value pairs for dialogue acts, with slots and
corresponding values aligned together. This allows our model to learn from all
available data including the slot-value pairing, rather than being restricted
to delexicalized slots. We show that this helps our model generate more natural
sentences with better grammar. We further improve our model's performance by
transferring weights learnt from a pretrained sentence auto-encoder. Human
evaluation of our best-performing model indicates that it generates sentences
which users find more appealing.
Gated-Attention Readers for Text Comprehension
Authors: Bhuwan Dhingra, Hanxiao Liu, Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov
Date: 2016-06-05 00:00:00
Proceeding: ACL 2017 7
Tasks: Answer Selection, Open-Domain Question Answering, Question Answering, Reading Comprehension

In this paper we study the problem of answering cloze-style questions over
documents. Our model, the Gated-Attention (GA) Reader, integrates a multi-hop
architecture with a novel attention mechanism, which is based on multiplicative
interactions between the query embedding and the intermediate states of a
recurrent neural network document reader. This enables the reader to build
query-specific representations of tokens in the document for accurate answer
selection. The GA Reader obtains state-of-the-art results on three benchmarks
for this task--the CNN \& Daily Mail news stories and the Who Did What dataset.
The effectiveness of multiplicative interaction is demonstrated by an ablation
study, and by comparing to alternative compositional operators for implementing
the gated-attention. The code is available at
https://github.com/bdhingra/ga-reader.
Stationary signal processing on graphs
Authors: Nathanaël Perraudin, Pierre Vandergheynst
Date: 2016-01-11 00:00:00
Tasks: Denoising, Translation

Graphs are a central tool in machine learning and information processing as
they allow to conveniently capture the structure of complex datasets. In this
context, it is of high importance to develop flexible models of signals defined
over graphs or networks. In this paper, we generalize the traditional concept
of wide sense stationarity to signals defined over the vertices of arbitrary
weighted undirected graphs. We show that stationarity is expressed through the
graph localization operator reminiscent of translation. We prove that
stationary graph signals are characterized by a well-defined Power Spectral
Density that can be efficiently estimated even for large graphs. We leverage
this new concept to derive Wiener-type estimation procedures of noisy and
partially observed signals and illustrate the performance of this new model for
denoising and regression.
Performance Limits of Stochastic Sub-Gradient Learning, Part I: Single Agent Case
Authors: Bicheng Ying, Ali H. Sayed
Date: 2015-11-24 00:00:00
Tasks: Denoising

In this work and the supporting Part II, we examine the performance of
stochastic sub-gradient learning strategies under weaker conditions than
usually considered in the literature. The new conditions are shown to be
automatically satisfied by several important cases of interest including SVM,
LASSO, and Total-Variation denoising formulations. In comparison, these
problems do not satisfy the traditional assumptions used in prior analyses and,
therefore, conclusions derived from these earlier treatments are not directly
applicable to these problems. The results in this article establish that
stochastic sub-gradient strategies can attain linear convergence rates, as
opposed to sub-linear rates, to the steady-state regime. A realizable
exponential-weighting procedure is employed to smooth the intermediate iterates
and guarantee useful performance bounds in terms of convergence rate and
excessive risk performance. Part I of this work focuses on single-agent
scenarios, which are common in stand-alone learning applications, while Part II
extends the analysis to networked learners. The theoretical conclusions are
illustrated by several examples and simulations, including comparisons with the
FISTA procedure.
Feature selection algorithm based on Catastrophe model to improve the performance of regression analysis
Authors: Mahdi Zarei
Date: 2017-04-21 00:00:00
Tasks: feature selection, regression

In this paper we introduce a new feature selection algorithm to remove the
irrelevant or redundant features in the data sets. In this algorithm the
importance of a feature is based on its fitting to the Catastrophe model.
Akaike information crite- rion value is used for ranking the features in the
data set. The proposed algorithm is compared with well-known RELIEF feature
selection algorithm. Breast Cancer, Parkinson Telemonitoring data and Slice
locality data sets are used to evaluate the model.
A Distributed Approach for Networked Flying Platform Association with Small Cells in 5G+ Networks
Authors: Syed Awais Wahab Shah, Tamer Khattab, Muhammad Zeeshan Shakir, Mazen Omar Hasna
Date: 2017-04-21 00:00:00
Tasks: Management

The densification of small-cell base stations in a 5G architecture is a
promising approach to enhance the coverage area and facilitate the ever
increasing capacity demand of end users. However, the bottleneck is an
intelligent management of a backhaul/fronthaul network for these small-cell
base stations. This involves efficient association and placement of the
backhaul hubs that connects these small-cells with the core network.
Terrestrial hubs suffer from an inefficient non line of sight link limitations
and unavailability of a proper infrastructure in an urban area. Seeing the
popularity of flying platforms, we employ here an idea of using networked
flying platform (NFP) such as unmanned aerial vehicles (UAVs), drones, unmanned
balloons flying at different altitudes, as aerial backhaul hubs. The
association problem of these NFP-hubs and small-cell base stations is
formulated considering backhaul link and NFP related limitations such as
maximum number of supported links and bandwidth. Then, this paper presents an
efficient and distributed solution of the designed problem, which performs a
greedy search in order to maximize the sum rate of the overall network. A
favorable performance is observed via a numerical comparison of our proposed
method with optimal exhaustive search algorithm in terms of sum rate and
run-time speed.
A hybrid spatial data mining approach based on fuzzy topological relations and MOSES evolutionary algorithm
Authors: Amir Hossein Goudarzi, Nasser Ghadiri
Date: 2017-04-21 00:00:00
Tasks: Decision Making, General Classification

Making high-quality decisions in strategic spatial planning is heavily
dependent on extracting knowledge from vast amounts of data. Although many
decision-making problems like developing urban areas require such perception
and reasoning, existing methods in this field usually neglect the deep
knowledge mined from geographic databases and are based on pure statistical
methods. Due to the large volume of data gathered in spatial databases, and the
uncertainty of spatial objects, mining association rules for high-level
knowledge representation is a challenging task. Few algorithms manage
geographical and non-geographical data using topological relations. In this
paper, a novel approach for spatial data mining based on the MOSES evolutionary
framework is presented which improves the classic genetic programming approach.
A hybrid architecture called GGeo is proposed to apply the MOSES mining rules
considering fuzzy topological relations from spatial data. The uncertainty and
fuzziness aspects are addressed using an enriched model of topological
relations by fuzzy region connection calculus. Moreover, to overcome the
problem of time-consuming fuzzy topological relationships calculations, this a
novel data pre-processing method is offered. GGeo analyses and learns from
geographical and non-geographical data and uses topological and distance
parameters, and returns a series of arithmetic-spatial formulas as
classification rules. The proposed approach is resistant to noisy data, and all
its stages run in parallel to increase speed. This approach may be used in
different spatial data classification problems as well as representing an
appropriate method of data analysis and economic policy making.
Improving Classification by Improving Labelling: Introducing Probabilistic Multi-Label Object Interaction Recognition
Authors: Michael Wray, Davide Moltisanti, Walterio Mayol-Cuevas, Dima Damen
Date: 2017-03-24 00:00:00
Tasks: General Classification, Object

This work deviates from easy-to-define class boundaries for object
interactions. For the task of object interaction recognition, often captured
using an egocentric view, we show that semantic ambiguities in verbs and
recognising sub-interactions along with concurrent interactions result in
legitimate class overlaps (Figure 1). We thus aim to model the mapping between
observations and interaction classes, as well as class overlaps, towards a
probabilistic multi-label classifier that emulates human annotators. Given a
video segment containing an object interaction, we model the probability for a
verb, out of a list of possible verbs, to be used to annotate that interaction.
The proba- bility is learnt from crowdsourced annotations, and is tested on two
public datasets, comprising 1405 video sequences for which we provide
annotations on 90 verbs. We outper- form conventional single-label
classification by 11% and 6% on the two datasets respectively, and show that
learning from annotation probabilities outperforms majority voting and enables
discovery of co-occurring labels.
Scientific Article Summarization Using Citation-Context and Article's Discourse Structure
Authors: Arman Cohan, Nazli Goharian
Date: 2017-04-21 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Articles

We propose a summarization approach for scientific articles which takes
advantage of citation-context and the document discourse model. While citations
have been previously used in generating scientific summaries, they lack the
related context from the referenced article and therefore do not accurately
reflect the article's content. Our method overcomes the problem of
inconsistency between the citation summary and the article's content by
providing context for each citation. We also leverage the inherent scientific
article's discourse for producing better summaries. We show that our proposed
method effectively improves over existing summarization approaches (greater
than 30% improvement over the best performing baseline) in terms of
\textsc{Rouge} scores on TAC2014 scientific summarization dataset. While the
dataset we use for evaluation is in the biomedical domain, most of our
approaches are general and therefore adaptable to other domains.
Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities
Authors: Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex
Date: 2017-04-21 00:00:00
Tasks: Specificity

Humans can ground natural language commands to tasks at both abstract and
fine-grained levels of specificity. For instance, a human forklift operator can
be instructed to perform a high-level action, like "grab a pallet" or a
low-level action like "tilt back a little bit." While robots are also capable
of grounding language commands to tasks, previous methods implicitly assume
that all commands and tasks reside at a single, fixed level of abstraction.
Additionally, methods that do not use multiple levels of abstraction encounter
inefficient planning and execution times as they solve tasks at a single level
of abstraction with large, intractable state-action spaces closely resembling
real world complexity. In this work, by grounding commands to all the tasks or
subtasks available in a hierarchical planning framework, we arrive at a model
capable of interpreting language at multiple levels of specificity ranging from
coarse to more granular. We show that the accuracy of the grounding procedure
is improved when simultaneously inferring the degree of abstraction in language
used to communicate the task. Leveraging hierarchy also improves efficiency:
our proposed approach enables a robot to respond to a command within one second
on 90% of our tasks, while baselines take over twenty seconds on half the
tasks. Finally, we demonstrate that a real, physical robot can ground commands
at multiple levels of abstraction allowing it to efficiently plan different
subtasks within the same planning hierarchy.
Making Neural Programming Architectures Generalize via Recursion
Authors: Jonathon Cai, Richard Shin, Dawn Song
Date: 2017-04-21 00:00:00

Empirically, neural networks that attempt to learn programs from data have
exhibited poor generalizability. Moreover, it has traditionally been difficult
to reason about the behavior of these models beyond a certain level of input
complexity. In order to address these issues, we propose augmenting neural
architectures with a key abstraction: recursion. As an application, we
implement recursion in the Neural Programmer-Interpreter framework on four
tasks: grade-school addition, bubble sort, topological sort, and quicksort. We
demonstrate superior generalizability and interpretability with small amounts
of training data. Recursion divides the problem into smaller pieces and
drastically reduces the domain of each neural network component, making it
tractable to prove guarantees about the overall system's behavior. Our
experience suggests that in order for neural architectures to robustly learn
program semantics, it is necessary to incorporate a concept like recursion.
Context-based Object Viewpoint Estimation: A 2D Relational Approach
Authors: Jose Oramas, Luc De Raedt, Tinne Tuytelaars
Date: 2017-04-21 00:00:00
Tasks: Action Recognition, Object, object-detection, Object Detection, Temporal Action Localization, Viewpoint Estimation

The task of object viewpoint estimation has been a challenge since the early
days of computer vision. To estimate the viewpoint (or pose) of an object,
people have mostly looked at object intrinsic features, such as shape or
appearance. Surprisingly, informative features provided by other, extrinsic
elements in the scene, have so far mostly been ignored. At the same time,
contextual cues have been proven to be of great benefit for related tasks such
as object detection or action recognition. In this paper, we explore how
information from other objects in the scene can be exploited for viewpoint
estimation. In particular, we look at object configurations by following a
relational neighbor-based approach for reasoning about object relations. We
show that, starting from noisy object detections and viewpoint estimates,
exploiting the estimated viewpoint and location of other objects in the scene
can lead to improved object viewpoint predictions. Experiments on the KITTI
dataset demonstrate that object configurations can indeed be used as a
complementary cue to appearance-based viewpoint estimation. Our analysis
reveals that the proposed context-based method can improve object viewpoint
estimation by reducing specific types of viewpoint estimation errors commonly
made by methods that only consider local information. Moreover, considering
contextual information produces superior performance in scenes where a high
number of object instances occur. Finally, our results suggest that, following
a cautious relational neighbor formulation brings improvements over its
aggressive counterpart for the task of object viewpoint estimation.
Estimating Nonlinear Dynamics with the ConvNet Smoother
Authors: Luca Ambrogioni, Umut Güçlü, Eric Maris, Marcel van Gerven
Date: 2017-02-17 00:00:00

Estimating the state of a dynamical system from a series of noise-corrupted
observations is fundamental in many areas of science and engineering. The most
well-known method, the Kalman smoother (and the related Kalman filter), relies
on assumptions of linearity and Gaussianity that are rarely met in practice. In
this paper, we introduced a new dynamical smoothing method that exploits the
remarkable capabilities of convolutional neural networks to approximate complex
non-linear functions. The main idea is to generate a training set composed of
both latent states and observations from an ensemble of simulators and to train
the deep network to recover the former from the latter. Importantly, this
method only requires the availability of the simulators and can therefore be
applied in situations in which either the latent dynamical model or the
observation model cannot be easily expressed in closed form. In our simulation
studies, we show that the resulting ConvNet smoother has almost optimal
performance in the Gaussian case even when the parameters are unknown.
Furthermore, the method can be successfully applied to extremely non-linear and
non-Gaussian systems. Finally, we empirically validate our approach via the
analysis of measured brain signals.
Firing Cell: An Artificial Neuron with a Simulation of Long-Term-Potentiation-Related Memory
Authors: Jacek Bialowas, Beata Grzyb, Pawel Poszumski
Date: 2017-04-21 00:00:00

We propose a computational model of neuron, called firing cell (FC),
properties of which cover such phenomena as attenuation of receptors for
external stimuli, delay and decay of postsynaptic potentials, modification of
internal weights due to propagation of postsynaptic potentials through the
dendrite, modification of properties of the analog memory for each input due to
a pattern of short-time synaptic potentiation or long-time synaptic
potentiation (LTP), output-spike generation when the sum of all inputs exceeds
a threshold, and refraction. The cell may take one of the three forms:
excitatory, inhibitory, and receptory. The computer simulations showed that,
depending on the phase of input signals, the artificial neuron's output
frequency may demonstrate various chaotic behaviors.
Panorama to panorama matching for location recognition
Authors: Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Teddy Furon, Ondrej Chum
Date: 2017-04-21 00:00:00
Tasks: Retrieval

Location recognition is commonly treated as visual instance retrieval on
"street view" imagery. The dataset items and queries are panoramic views, i.e.
groups of images taken at a single location. This work introduces a novel
panorama-to-panorama matching process, either by aggregating features of
individual images in a group or by explicitly constructing a larger panorama.
In either case, multiple views are used as queries. We reach near perfect
location recognition on a standard benchmark with only four query views.
Neural Belief Tracker: Data-Driven Dialogue State Tracking
Authors: Nikola Mrkšić, Diarmuid Ó Séaghdha, Tsung-Hsien Wen, Blaise Thomson, Steve Young
Date: 2016-06-12 00:00:00
Proceeding: ACL 2017 7
Tasks: Dialogue State Tracking, Representation Learning, Spoken Dialogue Systems, Spoken Language Understanding

One of the core components of modern spoken dialogue systems is the belief
tracker, which estimates the user's goal at every step of the dialogue.
However, most current approaches have difficulty scaling to larger, more
complex dialogue domains. This is due to their dependency on either: a) Spoken
Language Understanding models that require large amounts of annotated training
data; or b) hand-crafted lexicons for capturing some of the linguistic
variation in users' language. We propose a novel Neural Belief Tracking (NBT)
framework which overcomes these problems by building on recent advances in
representation learning. NBT models reason over pre-trained word vectors,
learning to compose them into distributed representations of user utterances
and dialogue context. Our evaluation on two datasets shows that this approach
surpasses past limitations, matching the performance of state-of-the-art models
which rely on hand-crafted semantic lexicons and outperforming them when such
lexicons are not provided.
Governing Governance: A Formal Framework for Analysing Institutional Design and Enactment Governance
Authors: Thomas C. King
Date: 2017-04-21 00:00:00

This dissertation is motivated by the need, in today's globalist world, for a
precise way to enable governments, organisations and other regulatory bodies to
evaluate the constraints they place on themselves and others. An organisation's
modus operandi is enacting and fulfilling contracts between itself and its
participants. Yet, organisational contracts should respect external laws, such
as those setting out data privacy rights and liberties. Contracts can only be
enacted by following contract law processes, which often require bilateral
agreement and consideration. Governments need to legislate whilst understanding
today's context of national and international governance hierarchy where law
makers shun isolationism and seek to influence one another. Governments should
avoid punishment by respecting constraints from international treaties and
human rights charters. Governments can only enact legislation by following
their own, pre-existing, law making procedures. In other words, institutions,
such as laws and contracts are designed and enacted under constraints.
Attention Strategies for Multi-Source Sequence-to-Sequence Learning
Authors: Jindřich Libovický, Jindřich Helcl
Date: 2017-04-21 00:00:00
Tasks: Automatic Post-Editing, Translation

Modeling attention in neural multi-source sequence-to-sequence learning
remains a relatively unexplored area, despite its usefulness in tasks that
incorporate multiple source languages or modalities. We propose two novel
approaches to combine the outputs of attention mechanisms over each source
sequence, flat and hierarchical. We compare the proposed methods with existing
techniques and present results of systematic evaluation of those methods on the
WMT16 Multimodal Translation and Automatic Post-editing tasks. We show that the
proposed methods achieve competitive results on both tasks.
PQTable: Non-exhaustive Fast Search for Product-quantized Codes using Hash Tables
Authors: Yusuke Matsui, Toshihiko Yamasaki, Kiyoharu Aizawa
Date: 2017-04-21 00:00:00
Tasks: CPU, Quantization

In this paper, we propose a product quantization table (PQTable); a fast
search method for product-quantized codes via hash-tables. An identifier of
each database vector is associated with the slot of a hash table by using its
PQ-code as a key. For querying, an input vector is PQ-encoded and hashed, and
the items associated with that code are then retrieved. The proposed PQTable
produces the same results as a linear PQ scan, and is 10^2 to 10^5 times
faster. Although state-of-the-art performance can be achieved by previous
inverted-indexing-based approaches, such methods require manually-designed
parameter setting and significant training; our PQTable is free of these
limitations, and therefore offers a practical and effective solution for
real-world problems. Specifically, when the vectors are highly compressed, our
PQTable achieves one of the fastest search performances on a single CPU to date
with significantly efficient memory usage (0.059 ms per query over 10^9 data
points with just 5.5 GB memory consumption). Finally, we show that our proposed
PQTable can naturally handle the codes of an optimized product quantization
(OPQTable).