pwc-archive/papers-with-abstracts huggingface dataset:
Deep Graph Convolutional Encoders for Structured Data to Text Generation
Authors: Diego Marcheggiani, Laura Perez-Beltrachini
Date: 2018-10-23 00:00:00
Proceeding: WS 2018 11
Tasks: Data-to-Text Generation, Graph-to-Sequence, KG-to-Text Generation, Text Generation

Most previous work on neural text generation from graph-structured data
relies on standard sequence-to-sequence methods. These approaches linearise the
input graph to be fed to a recurrent neural network. In this paper, we propose
an alternative encoder based on graph convolutional networks that directly
exploits the input structure. We report results on two graph-to-sequence
datasets that empirically show the benefits of explicitly encoding the input
graph structure.
Automated Reasoning in Normative Detachment Structures with Ideal Conditions
Authors: Tomer Libal, Matteo Pascucci
Date: 2018-10-23 00:00:00

Systems of deontic logic suffer either from being too expressive and
therefore hard to mechanize, or from being too simple to capture relevant
aspects of normative reasoning. In this article we look for a suitable way in
between: the automation of a simple logic of normative ideality and
sub-ideality that is not affected by many deontic paradoxes and that is
expressive enough to capture contrary-to-duty reason- ing. We show that this
logic is very useful to reason on normative scenarios from which one can
extract a certain kind of argumentative structure, called a Normative
Detachment Structure with Ideal Conditions. The theoretical analysis of the
logic is accompanied by examples of automated reasoning on a concrete legal
text.
Meta-Learning Multi-task Communication
Authors: Pengfei Liu, Xuanjing Huang
Date: 2018-10-23 00:00:00
Tasks: Inductive Bias, Meta-Learning, Multi-Task Learning

In this paper, we describe a general framework: Parameters Read-Write
Networks (PRaWNs) to systematically analyze current neural models for
multi-task learning, in which we find that existing models expect to
disentangle features into different spaces while features learned in practice
are still entangled in shared space, leaving potential hazards for other
training or unseen tasks.
  We propose to alleviate this problem by incorporating an inductive bias into
the process of multi-task learning, that each task can keep informed of not
only the knowledge stored in other tasks but the way how other tasks maintain
their knowledge.
  In practice, we achieve above inductive bias by allowing different tasks to
communicate by passing both hidden variables and gradients explicitly.
  Experimentally, we evaluate proposed methods on three groups of tasks and two
types of settings (\textsc{in-task} and \textsc{out-of-task}). Quantitative and
qualitative results show their effectiveness.
Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients
Authors: Bleema Rosenfeld, Osvaldo Simeone, Bipin Rajendran
Date: 2018-10-23 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Artificial Neural Networks (ANNs) are currently being used as function
approximators in many state-of-the-art Reinforcement Learning (RL) algorithms.
Spiking Neural Networks (SNNs) have been shown to drastically reduce the energy
consumption of ANNs by encoding information in sparse temporal binary spike
streams, hence emulating the communication mechanism of biological neurons. Due
to their low energy consumption, SNNs are considered to be important candidates
as co-processors to be implemented in mobile devices. In this work, the use of
SNNs as stochastic policies is explored under an energy-efficient
first-to-spike action rule, whereby the action taken by the RL agent is
determined by the occurrence of the first spike among the output neurons. A
policy gradient-based algorithm is derived considering a Generalized Linear
Model (GLM) for spiking neurons. Experimental results demonstrate the
capability of online trained SNNs as stochastic policies to gracefully trade
energy consumption, as measured by the number of spikes, and control
performance. Significant gains are shown as compared to the standard approach
of converting an offline trained ANN into an SNN.
Reconciling $λ$-Returns with Experience Replay
Authors: Brett Daley, Christopher Amato
Date: 2018-10-23 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Incremental Learning, Reinforcement Learning

Modern deep reinforcement learning methods have departed from the incremental learning required for eligibility traces, rendering the implementation of the $\lambda$-return difficult in this context. In particular, off-policy methods that utilize experience replay remain problematic because their random sampling of minibatches is not conducive to the efficient calculation of $\lambda$-returns. Yet replay-based methods are often the most sample efficient, and incorporating $\lambda$-returns into them is a viable way to achieve new state-of-the-art performance. Towards this, we propose the first method to enable practical use of $\lambda$-returns in arbitrary replay-based methods without relying on other forms of decorrelation such as asynchronous gradient updates. By promoting short sequences of past transitions into a small cache within the replay memory, adjacent $\lambda$-returns can be efficiently precomputed by sharing Q-values. Computation is not wasted on experiences that are never sampled, and stored $\lambda$-returns behave as stable temporal-difference (TD) targets that replace the target network. Additionally, our method grants the unique ability to observe TD errors prior to sampling; for the first time, transitions can be prioritized by their true significance rather than by a proxy to it. Furthermore, we propose the novel use of the TD error to dynamically select $\lambda$-values that facilitate faster learning. We show that these innovations can enhance the performance of DQN when playing Atari 2600 games, even under partial observability. While our work specifically focuses on $\lambda$-returns, these ideas are applicable to any multi-step return estimator.
Using Deep Learning for price prediction by exploiting stationary limit order book features
Authors: Avraam Tsantekidis, Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis
Date: 2018-10-23 00:00:00
Tasks: Time Series, Time Series Analysis

The recent surge in Deep Learning (DL) research of the past decade has
successfully provided solutions to many difficult problems. The field of
quantitative analysis has been slowly adapting the new methods to its problems,
but due to problems such as the non-stationary nature of financial data,
significant challenges must be overcome before DL is fully utilized. In this
work a new method to construct stationary features, that allows DL models to be
applied effectively, is proposed. These features are thoroughly tested on the
task of predicting mid price movements of the Limit Order Book. Several DL
models are evaluated, such as recurrent Long Short Term Memory (LSTM) networks
and Convolutional Neural Networks (CNN). Finally a novel model that combines
the ability of CNNs to extract useful features and the ability of LSTMs' to
analyze time series, is proposed and evaluated. The combined model is able to
outperform the individual LSTM and CNN models in the prediction horizons that
are tested.
GhostVLAD for set-based face recognition
Authors: Yujie Zhong, Relja Arandjelović, Andrew Zisserman
Date: 2018-10-23 00:00:00
Tasks: Face Recognition, Face Verification

The objective of this paper is to learn a compact representation of image
sets for template-based face recognition. We make the following contributions:
first, we propose a network architecture which aggregates and embeds the face
descriptors produced by deep convolutional neural networks into a compact
fixed-length representation. This compact representation requires minimal
memory storage and enables efficient similarity computation. Second, we propose
a novel GhostVLAD layer that includes {\em ghost clusters}, that do not
contribute to the aggregation. We show that a quality weighting on the input
faces emerges automatically such that informative images contribute more than
those with low quality, and that the ghost clusters enhance the network's
ability to deal with poor quality images. Third, we explore how input feature
dimension, number of clusters and different training techniques affect the
recognition performance. Given this analysis, we train a network that far
exceeds the state-of-the-art on the IJB-B face recognition dataset. This is
currently one of the most challenging public benchmarks, and we surpass the
state-of-the-art on both the identification and verification protocols.
Stepwise Acquisition of Dialogue Act Through Human-Robot Interaction
Authors: Akane Matsushima, Ryosuke Kanajiri, Yusuke Hattori, Chie Fukada, Natsuki Oka
Date: 2018-10-23 00:00:00

A dialogue act (DA) represents the meaning of an utterance at the
illocutionary force level (Austin 1962) such as a question, a request, and a
greeting. Since DAs take charge of the most fundamental part of communication,
we believe that the elucidation of DA learning mechanism is important for
cognitive science and artificial intelligence. The purpose of this study is to
verify that scaffolding takes place when a human teaches a robot, and to let a
robot learn to estimate DAs and to make a response based on them step by step
utilizing scaffolding provided by a human. To realize that, it is necessary for
the robot to detect changes in utterance and rewards given by the partner and
continue learning accordingly. Experimental results demonstrated that
participants who continued interaction for a sufficiently long time often gave
scaffolding for the robot. Although the number of experiments is still
insufficient to obtain a definite conclusion, we observed that 1) the robot
quickly learned to respond to DAs in most cases if the participants only spoke
utterances that match the situation, 2) in the case of participants who builds
scaffolding differently from what we assumed, learning did not proceed quickly,
and 3) the robot could learn to estimate DAs almost exactly if the participants
kept interaction for a sufficiently long time even if the scaffolding was
unexpected.
Object-oriented lexical encoding of multiword expressions: Short and sweet
Authors: Agata Savary, Simon Petitjean, Timm Lichte, Laura Kallmeyer, Jakub Waszczuk
Date: 2018-10-23 00:00:00

Multiword expressions (MWEs) exhibit both regular and idiosyncratic
properties. Their idiosyncrasy requires lexical encoding in parallel with their
component words. Their (at times intricate) regularity, on the other hand,
calls for means of flexible factorization to avoid redundant descriptions of
shared properties. However, so far, non-redundant general-purpose lexical
encoding of MWEs has not received a satisfactory solution. We offer a proof of
concept that this challenge might be effectively addressed within eXtensible
MetaGrammar (XMG), an object-oriented metagrammar framework. We first make an
existing metagrammatical resource, the FrenchTAG grammar, MWE-aware. We then
evaluate the factorization gain during incremental implementation with XMG on a
dataset extracted from an MWE-annotated reference corpus.
Analyzing Neuroimaging Data Through Recurrent Deep Learning Models
Authors: Armin W. Thomas, Hauke R. Heekeren, Klaus-Robert Müller, Wojciech Samek
Date: 2018-10-23 00:00:00
Tasks: Deep Learning

The application of deep learning (DL) models to neuroimaging data poses
several challenges, due to the high dimensionality, low sample size and complex
temporo-spatial dependency structure of these datasets. Even further, DL models
act as as black-box models, impeding insight into the association of cognitive
state and brain activity. To approach these challenges, we introduce the
DeepLight framework, which utilizes long short-term memory (LSTM) based DL
models to analyze whole-brain functional Magnetic Resonance Imaging (fMRI)
data. To decode a cognitive state (e.g., seeing the image of a house),
DeepLight separates the fMRI volume into a sequence of axial brain slices,
which is then sequentially processed by an LSTM. To maintain interpretability,
DeepLight adapts the layer-wise relevance propagation (LRP) technique. Thereby,
decomposing its decoding decision into the contributions of the single input
voxels to this decision. Importantly, the decomposition is performed on the
level of single fMRI volumes, enabling DeepLight to study the associations
between cognitive state and brain activity on several levels of data
granularity, from the level of the group down to the level of single time
points. To demonstrate the versatility of DeepLight, we apply it to a large
fMRI dataset of the Human Connectome Project. We show that DeepLight
outperforms conventional approaches of uni- and multivariate fMRI analysis in
decoding the cognitive states and in identifying the physiologically
appropriate brain regions associated with these states. We further demonstrate
DeepLight's ability to study the fine-grained temporo-spatial variability of
brain activity over sequences of single fMRI samples.
Preprocessor Selection for Machine Learning Pipelines
Authors: Brandon Schoenfeld, Christophe Giraud-Carrier, Mason Poggemann, Jarom Christensen, Kevin Seppi
Date: 2018-10-23 00:00:00
Tasks: BIG-bench Machine Learning, Hyperparameter Optimization

Much of the work in metalearning has focused on classifier selection,
combined more recently with hyperparameter optimization, with little concern
for data preprocessing. Yet, it is generally well accepted that machine
learning applications require not only model building, but also data
preprocessing. In other words, practical solutions consist of pipelines of
machine learning operators rather than single algorithms. Interestingly, our
experiments suggest that, on average, data preprocessing hinders accuracy,
while the best performing pipelines do actually make use of preprocessors.
Here, we conduct an extensive empirical study over a wide range of learning
algorithms and preprocessors, and use metalearning to determine when one should
make use of preprocessors in ML pipeline design.
Brand > Logo: Visual Analysis of Fashion Brands
Authors: M. Hadi Kiapour, Robinson Piramuthu
Date: 2018-10-23 00:00:00
Tasks: Marketing

While lots of people may think branding begins and ends with a logo, fashion
brands communicate their uniqueness through a wide range of visual cues such as
color, patterns and shapes. In this work, we analyze learned visual
representations by deep networks that are trained to recognize fashion brands.
In particular, the activation strength and extent of neurons are studied to
provide interesting insights about visual brand expressions. The proposed
method identifies where a brand stands in the spectrum of branding strategy,
i.e., from trademark-emblazoned goods with bold logos to implicit no logo
marketing. By quantifying attention maps, we are able to interpret the visual
characteristics of a brand present in a single image and model the general
design direction of a brand as a whole. We further investigate versatility of
neurons and discover "specialists" that are highly brand-specific and
"generalists" that detect diverse visual features. A human experiment based on
three main visual scenarios of fashion brands is conducted to verify the
alignment of our quantitative measures with the human perception of brands.
This paper demonstrate how deep networks go beyond logos in order to recognize
clothing brands in an image.
Machine Learning Accelerated Likelihood-Free Event Reconstruction in Dark Matter Direct Detection
Authors: U. Simola, B. Pelssers, D. Barge, J. Conrad, J. Corander
Date: 2018-10-23 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning, Position

Reconstructing the position of an interaction for any dual-phase time
projection chamber (TPC) with the best precision is key to directly detecting
Dark Matter. Using the likelihood-free framework, a new algorithm to
reconstruct the 2-D (x; y) position and the size of the charge signal (e) of an
interaction is presented. The algorithm uses the charge signal (S2) light
distribution obtained by simulating events using a waveform generator. To deal
with the computational effort required by the likelihood-free approach, we
employ the Bayesian Optimization for Likelihood-Free Inference (BOLFI)
algorithm. Together with BOLFI, prior distributions for the parameters of
interest (x; y; e) and highly informative discrepancy measures to perform the
analyses are introduced. We evaluate the quality of the proposed algorithm by a
comparison against the currently existing alternative methods using a
large-scale simulation study. BOLFI provides a natural probabilistic
uncertainty measure for the reconstruction and it improved the accuracy of the
reconstruction over the next best algorithm by up to 15% when focusing on
events over a large radii (R > 30 cm, the outer 37% of the detector). In
addition, BOLFI provides the smallest uncertainties among all the tested
methods.
Learning Classical Planning Strategies with Policy Gradient
Authors: Pawel Gomoluch, Dalal Alrajeh, Alessandra Russo
Date: 2018-10-23 00:00:00

A common paradigm in classical planning is heuristic forward search. Forward
search planners often rely on simple best-first search which remains fixed
throughout the search process. In this paper, we introduce a novel search
framework capable of alternating between several forward search approaches
while solving a particular planning problem. Selection of the approach is
performed using a trainable stochastic policy, mapping the state of the search
to a probability distribution over the approaches. This enables using policy
gradient to learn search strategies tailored to a specific distributions of
planning problems and a selected performance metric, e.g. the IPC score. We
instantiate the framework by constructing a policy space consisting of five
search approaches and a two-dimensional representation of the planner's state.
Then, we train the system on randomly generated problems from five IPC domains
using three different performance metrics. Our experimental results show that
the learner is able to discover domain-specific search strategies, improving
the planner's performance relative to the baselines of plain best-first search
and a uniform policy.
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach
Authors: Alexander Lin, Yingzhuo Zhang, Jeremy Heng, Stephen A. Allsop, Kay M. Tye, Pierre E. Jacob, Demba Ba
Date: 2018-10-23 00:00:00
Tasks: Bayesian Inference, Clustering, State Space Models, Time Series, Time Series Analysis

We propose a general statistical framework for clustering multiple time
series that exhibit nonlinear dynamics into an a-priori-unknown number of
sub-groups. Our motivation comes from neuroscience, where an important problem
is to identify, within a large assembly of neurons, subsets that respond
similarly to a stimulus or contingency. Upon modeling the multiple time series
as the output of a Dirichlet process mixture of nonlinear state-space models,
we derive a Metropolis-within-Gibbs algorithm for full Bayesian inference that
alternates between sampling cluster assignments and sampling parameter values
that form the basis of the clustering. The Metropolis step employs recent
innovations in particle-based methods. We apply the framework to clustering
time series acquired from the prefrontal cortex of mice in an experiment
designed to characterize the neural underpinnings of fear.
Efficient Bayesian Experimental Design for Implicit Models
Authors: Steven Kleinegesse, Michael Gutmann
Date: 2018-10-23 00:00:00
Tasks: Bayesian Optimisation, Experimental Design

Bayesian experimental design involves the optimal allocation of resources in
an experiment, with the aim of optimising cost and performance. For implicit
models, where the likelihood is intractable but sampling from the model is
possible, this task is particularly difficult and therefore largely unexplored.
This is mainly due to technical difficulties associated with approximating
posterior distributions and utility functions. We devise a novel experimental
design framework for implicit models that improves upon previous work in two
ways. First, we use the mutual information between parameters and data as the
utility function, which has previously not been feasible. We achieve this by
utilising Likelihood-Free Inference by Ratio Estimation (LFIRE) to approximate
posterior distributions, instead of the traditional approximate Bayesian
computation or synthetic likelihood methods. Secondly, we use Bayesian
optimisation in order to solve the optimal design problem, as opposed to the
typically used grid search or sampling-based methods. We find that this
increases efficiency and allows us to consider higher design dimensions.
Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)
Authors: Traiko Dinev, Michael U. Gutmann
Date: 2018-10-23 00:00:00
Tasks: Bayesian Inference, Time Series, Time Series Analysis

Parametric statistical models that are implicitly defined in terms of a
stochastic data generating process are used in a wide range of scientific
disciplines because they enable accurate modeling. However, learning the
parameters from observed data is generally very difficult because their
likelihood function is typically intractable. Likelihood-free Bayesian
inference methods have been proposed which include the frameworks of
approximate Bayesian computation (ABC), synthetic likelihood, and its recent
generalization that performs likelihood-free inference by ratio estimation
(LFIRE). A major difficulty in all these methods is choosing summary statistics
that reduce the dimensionality of the data to facilitate inference. While
several methods for choosing summary statistics have been proposed for ABC, the
literature for synthetic likelihood and LFIRE is very thin to date. We here
address this gap in the literature, focusing on the important special case of
time-series models. We show that convolutional neural networks trained to
predict the input parameters from the data provide suitable summary statistics
for LFIRE. On a wide range of time-series models, a single neural network
architecture produced equally or more accurate posteriors than alternative
methods.
A predictive processing model of perception and action for self-other distinction
Authors: Sebastian Kahl, Stefan Kopp
Date: 2018-10-23 00:00:00
Tasks: Attribute

During interaction with others, we perceive and produce social actions in
close temporal distance or even simultaneously. It has been argued that the
motor system is involved in perception and action, playing a fundamental role
in the handling of actions produced by oneself and by others. But how does it
distinguish in this processing between self and other, thus contributing to
self-other distinction? In this paper we propose a hierarchical model of
sensorimotor coordination based on principles of perception-action coupling and
predictive processing in which self-other distinction arises during action and
perception. For this we draw on mechanisms assumed for the integration of cues
for a sense of agency, i.e., the sense that an action is self-generated. We
report results from simulations of different scenarios, showing that the model
is not only able to minimize free energy during perception and action, but also
showing that the model can correctly attribute sense of agency to own actions.