pwc-archive/papers-with-abstracts huggingface dataset:
Zero-shot Neural Transfer for Cross-lingual Entity Linking
Authors: Shruti Rijhwani, Jiateng Xie, Graham Neubig, Jaime Carbonell
Date: 2018-11-09 00:00:00
Tasks: Cross-Lingual Entity Linking, Entity Linking

Cross-lingual entity linking maps an entity mention in a source language to
its corresponding entry in a structured knowledge base that is in a different
(target) language. While previous work relies heavily on bilingual lexical
resources to bridge the gap between the source and the target languages, these
resources are scarce or unavailable for many low-resource languages. To address
this problem, we investigate zero-shot cross-lingual entity linking, in which
we assume no bilingual lexical resources are available in the source
low-resource language. Specifically, we propose pivot-based entity linking,
which leverages information from a high-resource "pivot" language to train
character-level neural entity linking models that are transferred to the source
low-resource language in a zero-shot manner. With experiments on 9 low-resource
languages and transfer through a total of 54 languages, we show that our
proposed pivot-based framework improves entity linking accuracy 17% (absolute)
on average over the baseline systems, for the zero-shot scenario. Further, we
also investigate the use of language-universal phonological representations
which improves average accuracy (absolute) by 36% when transferring between
languages that use different scripts.
Design Rule Violation Hotspot Prediction Based on Neural Network Ensembles
Authors: Wei Zeng, Azadeh Davoodi, Yu Hen Hu
Date: 2018-11-09 00:00:00
Tasks: Ensemble Learning

Design rule check is a critical step in the physical design of integrated
circuits to ensure manufacturability. However, it can be done only after a
time-consuming detailed routing procedure, which adds drastically to the time
of design iterations. With advanced technology nodes, the outcomes of global
routing and detailed routing become less correlated, which adds to the
difficulty of predicting design rule violations from earlier stages. In this
paper, a framework based on neural network ensembles is proposed to predict
design rule violation hotspots using information from placement and global
routing. A soft voting structure and a PCA-based subset selection scheme are
developed on top of a baseline neural network from a recent work. Experimental
results show that the proposed architecture achieves significant improvement in
model performance compared to the baseline case. For half of test cases, the
performance is even better than random forest, a commonly-used ensemble
learning model.
Complex Unitary Recurrent Neural Networks using Scaled Cayley Transform
Authors: Kehelwala D. G. Maduranga, Kyle E. Helfrich, Qiang Ye
Date: 2018-11-09 00:00:00

Recurrent neural networks (RNNs) have been successfully used on a wide range
of sequential data problems. A well known difficulty in using RNNs is the
\textit{vanishing or exploding gradient} problem. Recently, there have been
several different RNN architectures that try to mitigate this issue by
maintaining an orthogonal or unitary recurrent weight matrix. One such
architecture is the scaled Cayley orthogonal recurrent neural network (scoRNN)
which parameterizes the orthogonal recurrent weight matrix through a scaled
Cayley transform. This parametrization contains a diagonal scaling matrix
consisting of positive or negative one entries that can not be optimized by
gradient descent. Thus the scaling matrix is fixed before training and a
hyperparameter is introduced to tune the matrix for each particular task. In
this paper, we develop a unitary RNN architecture based on a complex scaled
Cayley transform. Unlike the real orthogonal case, the transformation uses a
diagonal scaling matrix consisting of entries on the complex unit circle which
can be optimized using gradient descent and no longer requires the tuning of a
hyperparameter. We also provide an analysis of a potential issue of the modReLU
activiation function which is used in our work and several other unitary RNNs.
In the experiments conducted, the scaled Cayley unitary recurrent neural
network (scuRNN) achieves comparable or better results than scoRNN and other
unitary RNNs without fixing the scaling matrix.
The GaussianSketch for Almost Relative Error Kernel Distance
Authors: Jeff M. Phillips, Wai Ming Tai
Date: 2018-11-09 00:00:00

We introduce two versions of a new sketch for approximately embedding the Gaussian kernel into Euclidean inner product space. These work by truncating infinite expansions of the Gaussian kernel, and carefully invoking the RecursiveTensorSketch [Ahle et al. SODA 2020]. After providing concentration and approximation properties of these sketches, we use them to approximate the kernel distance between points sets. These sketches yield almost $(1+\varepsilon)$-relative error, but with a small additive $\alpha$ term. In the first variants the dependence on $1/\alpha$ is poly-logarithmic, but has higher degree of polynomial dependence on the original dimension $d$. In the second variant, the dependence on $1/\alpha$ is still poly-logarithmic, but the dependence on $d$ is linear.
Integrating Recurrence Dynamics for Speech Emotion Recognition
Authors: Efthymios Tzinis, Georgios Paraskevopoulos, Christos Baziotis, Alexandros Potamianos
Date: 2018-11-09 00:00:00
Tasks: Emotion Recognition, Emotion Recognition in Conversation, Speech Emotion Recognition

We investigate the performance of features that can capture nonlinear
recurrence dynamics embedded in the speech signal for the task of Speech
Emotion Recognition (SER). Reconstruction of the phase space of each speech
frame and the computation of its respective Recurrence Plot (RP) reveals
complex structures which can be measured by performing Recurrence
Quantification Analysis (RQA). These measures are aggregated by using
statistical functionals over segment and utterance periods. We report SER
results for the proposed feature set on three databases using different
classification methods. When fusing the proposed features with traditional
feature sets, we show an improvement in unweighted accuracy of up to 5.7% and
10.7% on Speaker-Dependent (SD) and Speaker-Independent (SI) SER tasks,
respectively, over the baseline. Following a segment-based approach we
demonstrate state-of-the-art performance on IEMOCAP using a Bidirectional
Recurrent Neural Network.
Reasoning over RDF Knowledge Bases using Deep Learning
Authors: Monireh Ebrahimi, Md. Kamruzzaman Sarker, Federico Bianchi, Ning Xie, Derek Doran, Pascal Hitzler
Date: 2018-11-09 00:00:00
Tasks: Deep Learning, Knowledge Graphs

Semantic Web knowledge representation standards, and in particular RDF and
OWL, often come endowed with a formal semantics which is considered to be of
fundamental importance for the field. Reasoning, i.e., the drawing of logical
inferences from knowledge expressed in such standards, is traditionally based
on logical deductive methods and algorithms which can be proven to be sound and
complete and terminating, i.e. correct in a very strong sense. For various
reasons, though, in particular, the scalability issues arising from the
ever-increasing amounts of Semantic Web data available and the inability of
deductive algorithms to deal with noise in the data, it has been argued that
alternative means of reasoning should be investigated which bear high promise
for high scalability and better robustness. From this perspective, deductive
algorithms can be considered the gold standard regarding correctness against
which alternative methods need to be tested. In this paper, we show that it is
possible to train a Deep Learning system on RDF knowledge graphs, such that it
is able to perform reasoning over new RDF knowledge graphs, with high precision
and recall compared to the deductive gold standard.
STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification
Authors: Yang Fu, Xiaoyang Wang, Yunchao Wei, Thomas Huang
Date: 2018-11-09 00:00:00
Tasks: Large-Scale Person Re-Identification, Person Re-Identification, Video-Based Person Re-Identification

In this work, we propose a novel Spatial-Temporal Attention (STA) approach to
tackle the large-scale person re-identification task in videos. Different from
the most existing methods, which simply compute representations of video clips
using frame-level aggregation (e.g. average pooling), the proposed STA adopts a
more effective way for producing robust clip-level feature representation.
Concretely, our STA fully exploits those discriminative parts of one target
person in both spatial and temporal dimensions, which results in a 2-D
attention score matrix via inter-frame regularization to measure the
importances of spatial parts across different frames. Thus, a more robust
clip-level feature representation can be generated according to a weighted sum
operation guided by the mined 2-D attention score matrix. In this way, the
challenging cases for video-based person re-identification such as pose
variation and partial occlusion can be well tackled by the STA. We conduct
extensive experiments on two large-scale benchmarks, i.e. MARS and
DukeMTMC-VideoReID. In particular, the mAP reaches 87.7% on MARS, which
significantly outperforms the state-of-the-arts with a large margin of more
than 11.6%.
Policy Regret in Repeated Games
Authors: Raman Arora, Michael Dinitz, Teodor V. Marinov, Mehryar Mohri
Date: 2018-11-09 00:00:00
Proceeding: NeurIPS 2018 12

The notion of \emph{policy regret} in online learning is a well defined? performance measure for the common scenario of adaptive adversaries, which more traditional quantities such as external regret do not take into account. We revisit the notion of policy regret and first show that there are online learning settings in which policy regret and external regret are incompatible: any sequence of play that achieves a favorable regret with respect to one definition must do poorly with respect to the other. We then focus on the game-theoretic setting where the adversary is a self-interested agent. In that setting, we show that external regret and policy regret are not in conflict and, in fact, that a wide class of algorithms can ensure a favorable regret with respect to both definitions, so long as the adversary is also using such an algorithm. We also show that the sequence of play of no-policy regret algorithms converges to a \emph{policy equilibrium}, a new notion of equilibrium that we introduce. Relating this back to external regret, we show that coarse correlated equilibria, which no-external regret players converge to, are a strict subset of policy equilibria. Thus, in game-theoretic settings, every sequence of play with no external regret also admits no policy regret, but the converse does not hold.
Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration
Authors: Helge Spieker, Arnaud Gotlieb, Dusica Marijan, Morten Mossige
Date: 2018-11-09 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Testing in Continuous Integration (CI) involves test case prioritization,
selection, and execution at each cycle. Selecting the most promising test cases
to detect bugs is hard if there are uncertainties on the impact of committed
code changes or, if traceability links between code and tests are not
available. This paper introduces Retecs, a new method for automatically
learning test case selection and prioritization in CI with the goal to minimize
the round-trip time between code commits and developer feedback on failed test
cases. The Retecs method uses reinforcement learning to select and prioritize
test cases according to their duration, previous last execution and failure
history. In a constantly changing environment, where new test cases are created
and obsolete test cases are deleted, the Retecs method learns to prioritize
error-prone test cases higher under guidance of a reward function and by
observing previous CI cycles. By applying Retecs on data extracted from three
industrial case studies, we show for the first time that reinforcement learning
enables fruitful automatic adaptive test case selection and prioritization in
CI and regression testing.
Reducing Network Agnostophobia
Authors: Akshay Raj Dhamija, Manuel Günther, Terrance E. Boult
Date: 2018-11-09 00:00:00
Proceeding: NeurIPS 2018 12

Agnostophobia, the fear of the unknown, can be experienced by deep learning
engineers while applying their networks to real-world applications.
Unfortunately, network behavior is not well defined for inputs far from a
networks training set. In an uncontrolled environment, networks face many
instances that are not of interest to them and have to be rejected in order to
avoid a false positive. This problem has previously been tackled by researchers
by either a) thresholding softmax, which by construction cannot return "none of
the known classes", or b) using an additional background or garbage class. In
this paper, we show that both of these approaches help, but are generally
insufficient when previously unseen classes are encountered. We also introduce
a new evaluation metric that focuses on comparing the performance of multiple
approaches in scenarios where such unseen classes or unknowns are encountered.
Our major contributions are simple yet effective Entropic Open-Set and
Objectosphere losses that train networks using negative samples from some
classes. These novel losses are designed to maximize entropy for unknown inputs
while increasing separation in deep feature space by modifying magnitudes of
known and unknown samples. Experiments on networks trained to classify classes
from MNIST and CIFAR-10 show that our novel loss functions are significantly
better at dealing with unknown inputs from datasets such as Devanagari,
NotMNIST, CIFAR-100, and SVHN.
Deep Learning Super-Diffusion in Multiplex Networks
Authors: Vito M. Leli, Saeed Osat, Timur Tlyachev, Dmitry V. Dylov, Jacob D. Biamonte
Date: 2018-11-09 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

Complex network theory has shown success in understanding the emergent and collective behavior of complex systems [1]. Many real-world complex systems were recently discovered to be more accurately modeled as multiplex networks [2-6]---in which each interaction type is mapped to its own network layer; e.g.~multi-layer transportation networks, coupled social networks, metabolic and regulatory networks, etc. A salient physical phenomena emerging from multiplexity is super-diffusion: exhibited by an accelerated diffusion admitted by the multi-layer structure as compared to any single layer. Theoretically super-diffusion was only known to be predicted using the spectral gap of the full Laplacian of a multiplex network and its interacting layers. Here we turn to machine learning which has developed techniques to recognize, classify, and characterize complex sets of data. We show that modern machine learning architectures, such as fully connected and convolutional neural networks, can classify and predict the presence of super-diffusion in multiplex networks with 94.12\% accuracy. Such predictions can be done {\it in situ}, without the need to determine spectral properties of a network.
Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification
Authors: Maryam Babaee, Ali Athar, Gerhard Rigoll
Date: 2018-11-09 00:00:00
Tasks: Clustering, Multiple People Tracking, Person Re-Identification

The task of multiple people tracking in monocular videos is challenging
because of the numerous difficulties involved: occlusions, varying
environments, crowded scenes, camera parameters and motion. In the
tracking-by-detection paradigm, most approaches adopt person re-identification
techniques based on computing the pairwise similarity between detections.
However, these techniques are less effective in handling long-term occlusions.
By contrast, tracklet (a sequence of detections) re-identification can improve
association accuracy since tracklets offer a richer set of visual appearance
and spatio-temporal cues. In this paper, we propose a tracking framework that
employs a hierarchical clustering mechanism for merging tracklets. To this end,
tracklet re-identification is performed by utilizing a novel multi-stage deep
network that can jointly reason about the visual appearance and spatio-temporal
properties of a pair of tracklets, thereby providing a robust measure of
affinity. Experimental results on the challenging MOT16 and MOT17 benchmarks
show that our method significantly outperforms state-of-the-arts.
Observability Properties of Colored Graphs
Authors: Mark Chilenski, George Cybenko, Isaac Dekine, Piyush Kumar, Gil Raz
Date: 2018-11-09 00:00:00

A colored graph is a directed graph in which nodes or edges have been assigned colors that are not necessarily unique. Observability problems in such graphs consider whether an agent observing the colors of edges or nodes traversed on a path in the graph can determine which node they are at currently or which nodes were visited earlier in the traversal. Previous research efforts have identified several different notions of observability as well as the associated properties of graphs for which those observability properties hold. This paper unifies the prior work into a common framework with several new results about relationships between those notions and associated graph properties. The new framework provides an intuitive way to reason about the attainable accuracy as a function of lag and time spent observing, and identifies simple modifications to improve the observability of a given graph. We show that one form of the graph modification problem is in NP-Complete. The intuition of the new framework is borne out with numerical experiments. This work has implications for problems that can be described in terms of an agent traversing a colored graph, including the reconstruction of hidden states in a hidden Markov model (HMM).
A Bayesian Perspective of Statistical Machine Learning for Big Data
Authors: Rajiv Sambasivan, Sourish Das, Sujit K Sahu
Date: 2018-11-09 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes, Learning Theory

Statistical Machine Learning (SML) refers to a body of algorithms and methods
by which computers are allowed to discover important features of input data
sets which are often very large in size. The very task of feature discovery
from data is essentially the meaning of the keyword `learning' in SML.
Theoretical justifications for the effectiveness of the SML algorithms are
underpinned by sound principles from different disciplines, such as Computer
Science and Statistics. The theoretical underpinnings particularly justified by
statistical inference methods are together termed as statistical learning
theory.
  This paper provides a review of SML from a Bayesian decision theoretic point
of view -- where we argue that many SML techniques are closely connected to
making inference by using the so called Bayesian paradigm. We discuss many
important SML techniques such as supervised and unsupervised learning, deep
learning, online learning and Gaussian processes especially in the context of
very large data sets where these are often employed. We present a dictionary
which maps the key concepts of SML from Computer Science and Statistics. We
illustrate the SML techniques with three moderately large data sets where we
also discuss many practical implementation issues. Thus the review is
especially targeted at statisticians and computer scientists who are aspiring
to understand and apply SML for moderately large to big data sets.
Mathematical Theory of Evidence Versus Evidence
Authors: Mieczysław Kłopotek
Date: 2018-11-09 00:00:00

This paper is concerned with the apparent greatest weakness of the
Mathematical Theory of Evidence (MTE) of Shafer \cite{Shafer:76}, which has
been strongly criticized by Wasserman \cite{Wasserman:92ijar}.
  Weaknesses of Shafer's proposal \cite{Shafer:90b} of probabilistic
interpretation of MTE belief functions is demonstrated. Thereafter a new
probabilistic interpretation of MTE conforming both to definition of belief
function and to Dempster's rule of combination of independent evidence. It is
shown that shaferian conditioning of belief functions on observations
\cite{Shafer:90b} may be treated as selection combined with modification of
data, that is data is not viewed as it is but it is casted into one's beliefs
in what it should be like.
PennyLane: Automatic differentiation of hybrid quantum-classical computations
Authors: Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, Shahnawaz Ahmed, Vishnu Ajith, M. Sohaib Alam, Guillermo Alonso-Linaje, B. AkashNarayanan, Ali Asadi, Juan Miguel Arrazola, Utkarsh Azad, Sam Banning, Carsten Blank, Thomas R Bromley, Benjamin A. Cordier, Jack Ceroni, Alain Delgado, Olivia Di Matteo, Amintor Dusko, Tanya Garg, Diego Guala, Anthony Hayes, Ryan Hill, Aroosa Ijaz, Theodor Isacsson, David Ittah, Soran Jahangiri, Prateek Jain, Edward Jiang, Ankit Khandelwal, Korbinian Kottmann, Robert A. Lang, Christina Lee, Thomas Loke, Angus Lowe, Keri McKiernan, Johannes Jakob Meyer, J. A. Montañez-Barrera, Romain Moyard, Zeyue Niu, Lee James O'Riordan, Steven Oud, Ashish Panigrahi, Chae-Yeun Park, Daniel Polatajko, Nicolás Quesada, Chase Roberts, Nahum Sá, Isidor Schoch, Borun Shi, Shuli Shu, Sukin Sim, Arshpreet Singh, Ingrid Strandberg, Jay Soni, Antal Száva, Slimane Thabet, Rodrigo A. Vargas-Hernández, Trevor Vincent, Nicola Vitucci, Maurice Weber, David Wierichs, Roeland Wiersema, Moritz Willmann, Vincent Wong, Shaoming Zhang, Nathan Killoran
Date: 2018-11-12 00:00:00
Tasks: BIG-bench Machine Learning, Quantum Machine Learning

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.