pwc-archive/papers-with-abstracts huggingface dataset:
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
Authors: He He, Anusha Balakrishnan, Mihail Eric, Percy Liang
Date: 2017-04-24 00:00:00
Proceeding: ACL 2017 7
Tasks: Knowledge Graph Embeddings

We study a symmetric collaborative dialogue setting in which two agents, each
with private knowledge, must strategically communicate to achieve a common
goal. The open-ended dialogue state in this setting poses new challenges for
existing dialogue systems. We collected a dataset of 11K human-human dialogues,
which exhibits interesting lexical, semantic, and strategic elements. To model
both structured knowledge and unstructured language, we propose a neural model
with dynamic knowledge graph embeddings that evolve as the dialogue progresses.
Automatic and human evaluations show that our model is both more effective at
achieving the goal and more human-like than baseline neural and rule-based
models.
An Analysis of Action Recognition Datasets for Language and Vision Tasks
Authors: Spandana Gella, Frank Keller
Date: 2017-04-24 00:00:00
Proceeding: ACL 2017 7
Tasks: Action Recognition, Image Retrieval, Retrieval, Temporal Action Localization

A large amount of recent research has focused on tasks that combine language
and vision, resulting in a proliferation of datasets and methods. One such task
is action recognition, whose applications include image annotation, scene
under- standing and image retrieval. In this survey, we categorize the existing
ap- proaches based on how they conceptualize this problem and provide a
detailed review of existing datasets, highlighting their di- versity as well as
advantages and disad- vantages. We focus on recently devel- oped datasets which
link visual informa- tion with linguistic resources and provide a fine-grained
syntactic and semantic anal- ysis of actions in images.
Graying the black box: Understanding DQNs
Authors: Tom Zahavy, Nir Ben Zrihem, Shie Mannor
Date: 2016-02-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years there is a growing interest in using deep representations for
reinforcement learning. In this paper, we present a methodology and tools to
analyze Deep Q-networks (DQNs) in a non-blind matter. Moreover, we propose a
new model, the Semi Aggregated Markov Decision Process (SAMDP), and an
algorithm that learns it automatically. The SAMDP model allows us to identify
spatio-temporal abstractions directly from features and may be used as a
sub-goal detector in future work. Using our tools we reveal that the features
learned by DQNs aggregate the state space in a hierarchical fashion, explaining
its success. Moreover, we are able to understand and describe the policies
learned by DQNs for three different Atari2600 games and suggest ways to
interpret, debug and optimize deep neural networks in reinforcement learning.
Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures
Authors: Raffaella Bernardi, Ruket Cakici, Desmond Elliott, Aykut Erdem, Erkut Erdem, Nazli Ikizler-Cinbis, Frank Keller, Adrian Muscat, Barbara Plank
Date: 2016-01-15 00:00:00
Tasks: Image Description, Retrieval

Automatic description generation from natural images is a challenging problem
that has recently received a large amount of interest from the computer vision
and natural language processing communities. In this survey, we classify the
existing approaches based on how they conceptualize this problem, viz., models
that cast description as either generation problem or as a retrieval problem
over a visual or multimodal representational space. We provide a detailed
review of existing models, highlighting their advantages and disadvantages.
Moreover, we give an overview of the benchmark image datasets and the
evaluation measures that have been developed to assess the quality of
machine-generated image descriptions. Finally we extrapolate future directions
in the area of automatic image description generation.
Modeling Human Reading with Neural Attention
Authors: Michael Hahn, Frank Keller
Date: 2016-08-19 00:00:00
Proceeding: EMNLP 2016 11
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

When humans read text, they fixate some words and skip others. However, there
have been few attempts to explain skipping behavior with computational models,
as most existing work has focused on predicting reading times (e.g.,~using
surprisal). In this paper, we propose a novel approach that models both
skipping and reading, using an unsupervised architecture that combines a neural
attention with autoencoding, trained on raw text using reinforcement learning.
Our model explains human reading behavior as a tradeoff between precision of
language understanding (encoding the input accurately) and economy of attention
(fixating as few words as possible). We evaluate the model on the Dundee
eye-tracking corpus, showing that it accurately predicts skipping behavior and
reading times, is competitive with surprisal, and captures known qualitative
features of human reading.
Times series averaging and denoising from a probabilistic perspective on time-elastic kernels
Authors: Pierre-François Marteau
Date: 2016-11-28 00:00:00
Tasks: Denoising, Dynamic Time Warping, Gesture Recognition, Time Series, Time Series Analysis, Time Series Denoising

In the light of regularized dynamic time warping kernels, this paper
re-considers the concept of time elastic centroid for a setof time series. We
derive a new algorithm based on a probabilistic interpretation of kernel
alignment matrices. This algorithm expressesthe averaging process in terms of a
stochastic alignment automata. It uses an iterative agglomerative heuristic
method for averagingthe aligned samples, while also averaging the times of
occurrence of the aligned samples. By comparing classification accuracies for45
heterogeneous time series datasets obtained by first nearest centroid/medoid
classifiers we show that: i) centroid-basedapproaches significantly outperform
medoid-based approaches, ii) for the considered datasets, our algorithm that
combines averagingin the sample space and along the time axes, emerges as the
most significantly robust model for time-elastic averaging with apromising
noise reduction capability. We also demonstrate its benefit in an isolated
gesture recognition experiment and its ability tosignificantly reduce the size
of training instance sets. Finally we highlight its denoising capability using
demonstrative synthetic data:we show that it is possible to retrieve, from few
noisy instances, a signal whose components are scattered in a wide spectral
band.
Poisson multi-Bernoulli conjugate prior for multiple extended object filtering
Authors: Karl Granstrom, Maryam Fatemi, Lennart Svensson
Date: 2016-05-20 00:00:00

This paper presents a Poisson multi-Bernoulli mixture (PMBM) conjugate prior for multiple extended object filtering. A Poisson point process is used to describe the existence of yet undetected targets, while a multi-Bernoulli mixture describes the distribution of the targets that have been detected. The prediction and update equations are presented for the standard transition density and measurement likelihood. Both the prediction and the update preserve the PMBM form of the density, and in this sense the PMBM density is a conjugate prior. However, the unknown data associations lead to an intractably large number of terms in the PMBM density, and approximations are necessary for tractability. A gamma Gaussian inverse Wishart implementation is presented, along with methods to handle the data association problem. A simulation study shows that the extended target PMBM filter performs well in comparison to the extended target d-GLMB and LMB filters. An experiment with Lidar data illustrates the benefit of tracking both detected and undetected targets.
Neural Semantic Parsing over Multiple Knowledge-bases
Authors: Jonathan Herzig, Jonathan Berant
Date: 2017-02-06 00:00:00
Proceeding: ACL 2017 7
Tasks: Form, Semantic Parsing

A fundamental challenge in developing semantic parsers is the paucity of
strong supervision in the form of language utterances annotated with logical
form. In this paper, we propose to exploit structural regularities in language
in different domains, and train semantic parsers over multiple knowledge-bases
(KBs), while sharing information across datasets. We find that we can
substantially improve parsing accuracy by training a single
sequence-to-sequence model over multiple KBs, when providing an encoding of the
domain at decoding time. Our model achieves state-of-the-art performance on the
Overnight dataset (containing eight domains), improves performance over a
single KB baseline from 75.6% to 79.6%, while obtaining a 7x reduction in the
number of model parameters.
Target Oriented High Resolution SAR Image Formation via Semantic Information Guided Regularizations
Authors: Biao Hou, Zaidao Wen, Licheng Jiao, Qian Wu
Date: 2017-04-24 00:00:00
Tasks: Decision Making

Sparsity-regularized synthetic aperture radar (SAR) imaging framework has
shown its remarkable performance to generate a feature enhanced high resolution
image, in which a sparsity-inducing regularizer is involved by exploiting the
sparsity priors of some visual features in the underlying image. However, since
the simple prior of low level features are insufficient to describe different
semantic contents in the image, this type of regularizer will be incapable of
distinguishing between the target of interest and unconcerned background
clutters. As a consequence, the features belonging to the target and clutters
are simultaneously affected in the generated image without concerning their
underlying semantic labels. To address this problem, we propose a novel
semantic information guided framework for target oriented SAR image formation,
which aims at enhancing the interested target scatters while suppressing the
background clutters. Firstly, we develop a new semantics-specific regularizer
for image formation by exploiting the statistical properties of different
semantic categories in a target scene SAR image. In order to infer the semantic
label for each pixel in an unsupervised way, we moreover induce a novel
high-level prior-driven regularizer and some semantic causal rules from the
prior knowledge. Finally, our regularized framework for image formation is
further derived as a simple iteratively reweighted $\ell_1$ minimization
problem which can be conveniently solved by many off-the-shelf solvers.
Experimental results demonstrate the effectiveness and superiority of our
framework for SAR image formation in terms of target enhancement and clutters
suppression, compared with the state of the arts. Additionally, the proposed
framework opens a new direction of devoting some machine learning strategies to
image formation, which can benefit the subsequent decision making tasks.
Exploiting Multi-layer Graph Factorization for Multi-attributed Graph Matching
Authors: Han-Mu Park, Kuk-Jin Yoon
Date: 2017-04-24 00:00:00
Tasks: Attribute, Graph Matching

Multi-attributed graph matching is a problem of finding correspondences
between two sets of data while considering their complex properties described
in multiple attributes. However, the information of multiple attributes is
likely to be oversimplified during a process that makes an integrated
attribute, and this degrades the matching accuracy. For that reason, a
multi-layer graph structure-based algorithm has been proposed recently. It can
effectively avoid the problem by separating attributes into multiple layers.
Nonetheless, there are several remaining issues such as a scalability problem
caused by the huge matrix to describe the multi-layer structure and a
back-projection problem caused by the continuous relaxation of the quadratic
assignment problem. In this work, we propose a novel multi-attributed graph
matching algorithm based on the multi-layer graph factorization. We reformulate
the problem to be solved with several small matrices that are obtained by
factorizing the multi-layer structure. Then, we solve the problem using a
convex-concave relaxation procedure for the multi-layer structure. The proposed
algorithm exhibits better performance than state-of-the-art algorithms based on
the single-layer structure.
Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing
Authors: Raluca D. Gaina, Jialin Liu, Simon M. Lucas, Diego Perez-Liebana
Date: 2017-04-24 00:00:00
Tasks: Evolutionary Algorithms

Monte Carlo Tree Search techniques have generally dominated General Video
Game Playing, but recent research has started looking at Evolutionary
Algorithms and their potential at matching Tree Search level of play or even
outperforming these methods. Online or Rolling Horizon Evolution is one of the
options available to evolve sequences of actions for planning in General Video
Game Playing, but no research has been done up to date that explores the
capabilities of the vanilla version of this algorithm in multiple games. This
study aims to critically analyse the different configurations regarding
population size and individual length in a set of 20 games from the General
Video Game AI corpus. Distinctions are made between deterministic and
stochastic games, and the implications of using superior time budgets are
studied. Results show that there is scope for the use of these techniques,
which in some configurations outperform Monte Carlo Tree Search, and also
suggest that further research in these methods could boost their performance.
Selective Encoding for Abstractive Sentence Summarization
Authors: Qingyu Zhou, Nan Yang, Furu Wei, Ming Zhou
Date: 2017-04-24 00:00:00
Proceeding: ACL 2017 7
Tasks: Decoder, Sentence, Sentence Summarization

We propose a selective encoding model to extend the sequence-to-sequence
framework for abstractive sentence summarization. It consists of a sentence
encoder, a selective gate network, and an attention equipped decoder. The
sentence encoder and decoder are built with recurrent neural networks. The
selective gate network constructs a second level sentence representation by
controlling the information flow from encoder to decoder. The second level
representation is tailored for sentence summarization task, which leads to
better performance. We evaluate our model on the English Gigaword, DUC 2004 and
MSR abstractive sentence summarization datasets. The experimental results show
that the proposed selective encoding model outperforms the state-of-the-art
baseline models.
Efficient Regression in Metric Spaces via Approximate Lipschitz Extension
Authors: Lee-Ad Gottlieb, Aryeh Kontorovich, Robert Krauthgamer
Date: 2011-11-18 00:00:00
Tasks: regression

We present a framework for performing efficient regression in general metric
spaces. Roughly speaking, our regressor predicts the value at a new point by
computing a Lipschitz extension --- the smoothest function consistent with the
observed data --- after performing structural risk minimization to avoid
overfitting. We obtain finite-sample risk bounds with minimal structural and
noise assumptions, and a natural speed-precision tradeoff. The offline
(learning) and online (prediction) stages can be solved by convex programming,
but this naive approach has runtime complexity $O(n^3)$, which is prohibitive
for large datasets. We design instead a regression algorithm whose speed and
generalization performance depend on the intrinsic dimension of the data, to
which the algorithm adapts. While our main innovation is algorithmic, the
statistical results may also be of independent interest.
Evaluating and Modelling Hanabi-Playing Agents
Authors: Joseph Walton-Rivers, Piers R. Williams, Richard Bartle, Diego Perez-Liebana, Simon M. Lucas
Date: 2017-04-24 00:00:00
Tasks: Game of Hanabi

Agent modelling involves considering how other agents will behave, in order
to influence your own actions. In this paper, we explore the use of agent
modelling in the hidden-information, collaborative card game Hanabi. We
implement a number of rule-based agents, both from the literature and of our
own devising, in addition to an Information Set Monte Carlo Tree Search
(IS-MCTS) agent. We observe poor results from IS-MCTS, so construct a new,
predictor version that uses a model of the agents with which it is paired. We
observe a significant improvement in game-playing strength from this agent in
comparison to IS-MCTS, resulting from its consideration of what the other
agents in a game would do. In addition, we create a flawed rule-based agent to
highlight the predictor's capabilities with such an agent.
Diffusion geometry unravels the emergence of functional clusters in collective phenomena
Authors: Manlio De Domenico
Date: 2017-04-24 00:00:00

Collective phenomena emerge from the interaction of natural or artificial
units with a complex organization. The interplay between structural patterns
and dynamics might induce functional clusters that, in general, are different
from topological ones. In biological systems, like the human brain, the overall
functionality is often favored by the interplay between connectivity and
synchronization dynamics, with functional clusters that do not coincide with
anatomical modules in most cases. In social, socio-technical and engineering
systems, the quest for consensus favors the emergence of clusters.
  Despite the unquestionable evidence for mesoscale organization of many
complex systems and the heterogeneity of their inter-connectivity, a way to
predict and identify the emergence of functional modules in collective
phenomena continues to elude us. Here, we propose an approach based on random
walk dynamics to define the diffusion distance between any pair of units in a
networked system. Such a metric allows to exploit the underlying diffusion
geometry to provide a unifying framework for the intimate relationship between
metastable synchronization, consensus and random search dynamics in complex
networks, pinpointing the functional mesoscale organization of synthetic and
biological systems.
A Dual Sparse Decomposition Method for Image Denoising
Authors: Hong Sun, Chen-Guang Liu, Cheng-Wei Sang
Date: 2017-04-24 00:00:00
Tasks: Denoising, Image Denoising

This article addresses the image denoising problem in the situations of
strong noise. We propose a dual sparse decomposition method. This method makes
a sub-dictionary decomposition on the over-complete dictionary in the sparse
decomposition. The sub-dictionary decomposition makes use of a novel criterion
based on the occurrence frequency of atoms of the over-complete dictionary over
the data set. The experimental results demonstrate that the
dual-sparse-decomposition method surpasses state-of-art denoising performance
in terms of both peak-signal-to-noise ratio and
structural-similarity-index-metric, and also at subjective visual quality.
Memory-augmented Attention Modelling for Videos
Authors: Rasool Fakoor, Abdel-rahman Mohamed, Margaret Mitchell, Sing Bing Kang, Pushmeet Kohli
Date: 2016-11-07 00:00:00
Tasks: Video Description

We present a method to improve video description generation by modeling
higher-order interactions between video frames and described concepts. By
storing past visual attention in the video associated to previously generated
words, the system is able to decide what to look at and describe in light of
what it has already looked at and described. This enables not only more
effective local attention, but tractable consideration of the video sequence
while generating each word. Evaluation on the challenging and popular MSVD and
Charades datasets demonstrates that the proposed architecture outperforms
previous video description approaches without requiring external temporal video
features.
k-FFNN: A priori knowledge infused Feed-forward Neural Networks
Authors: Sri Harsha Dumpala, Rupayan Chakraborty, Sunil Kumar Kopparapu
Date: 2017-04-24 00:00:00

Recurrent neural network (RNN) are being extensively used over feed-forward
neural networks (FFNN) because of their inherent capability to capture temporal
relationships that exist in the sequential data such as speech. This aspect of
RNN is advantageous especially when there is no a priori knowledge about the
temporal correlations within the data. However, RNNs require large amount of
data to learn these temporal correlations, limiting their advantage in low
resource scenarios. It is not immediately clear (a) how a priori temporal
knowledge can be used in a FFNN architecture (b) how a FFNN performs when
provided with this knowledge about temporal correlations (assuming available)
during training. The objective of this paper is to explore k-FFNN, namely a
FFNN architecture that can incorporate the a priori knowledge of the temporal
relationships within the data sequence during training and compare k-FFNN
performance with RNN in a low resource scenario. We evaluate the performance of
k-FFNN and RNN by extensive experimentation on MediaEval 2016 audio data
("Emotional Impact of Movies" task). Experimental results show that the
performance of k-FFNN is comparable to RNN, and in some scenarios k-FFNN
performs better than RNN when temporal knowledge is injected into FFNN
architecture. The main contributions of this paper are (a) fusing a priori
knowledge into FFNN architecture to construct a k-FFNN and (b) analyzing the
performance of k-FFNN with respect to RNN for different size of training data.
On the Discrepancy Between Kleinberg's Clustering Axioms and $k$-Means Clustering Algorithm Behavior
Authors: Robert Kłopotek, Mieczysław Kłopotek
Date: 2017-02-15 00:00:00
Tasks: Clustering, valid

This paper investigates the validity of Kleinberg's axioms for clustering
functions with respect to the quite popular clustering algorithm called
$k$-means. While Kleinberg's axioms have been discussed heavily in the past, we
concentrate here on the case predominantly relevant for $k$-means algorithm,
that is behavior embedded in Euclidean space. We point at some contradictions
and counter intuitiveness aspects of this axiomatic set within $\mathbb{R}^m$
that were evidently not discussed so far. Our results suggest that apparently
without defining clearly what kind of clusters we expect we will not be able to
construct a valid axiomatic system. In particular we look at the shape and the
gaps between the clusters. Finally we demonstrate that there exist several ways
to reconcile the formulation of the axioms with their intended meaning and that
under this reformulation the axioms stop to be contradictory and the real-world
$k$-means algorithm conforms to this axiomatic system.