pwc-archive/papers-with-abstracts huggingface dataset:
Local moment matching: A unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance
Authors: Yanjun Han, Jiantao Jiao, Tsachy Weissman
Date: 2018-02-23 00:00:00

We present \emph{Local Moment Matching (LMM)}, a unified methodology for
symmetric functional estimation and distribution estimation under Wasserstein
distance. We construct an efficiently computable estimator that achieves the
minimax rates in estimating the distribution up to permutation, and show that
the plug-in approach of our unlabeled distribution estimator is "universal" in
estimating symmetric functionals of discrete distributions. Instead of doing
best polynomial approximation explicitly as in existing literature of
functional estimation, the plug-in approach conducts polynomial approximation
implicitly and attains the optimal sample complexity for the entropy, power sum
and support size functionals.
A hybrid deep learning approach for medical relation extraction
Authors: Veera Raghavendra Chikka, Kamalakar Karlapalem
Date: 2018-06-26 00:00:00
Tasks: Deep Learning, Medical Relation Extraction, Relation, Relation Extraction, Sentence

Mining relationships between treatment(s) and medical problem(s) is vital in
the biomedical domain. This helps in various applications, such as decision
support system, safety surveillance, and new treatment discovery. We propose a
deep learning approach that utilizes both word level and sentence-level
representations to extract the relationships between treatment and problem.
While deep learning techniques demand a large amount of data for training, we
make use of a rule-based system particularly for relationship classes with
fewer samples. Our final relations are derived by jointly combining the results
from deep learning and rule-based models. Our system achieved a promising
performance on the relationship classes of I2b2 2010 relation extraction task.
Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria
Authors: Keze Wang, Liang Lin, Xiaopeng Yan, Ziliang Chen, Dongyu Zhang, Lei Zhang
Date: 2018-06-30 00:00:00
Tasks: Active Learning, object-detection, Object Detection, Self-Learning

Though quite challenging, leveraging large-scale unlabeled or partially
labeled data in learning systems (e.g., model/classifier training) has
attracted increasing attentions due to its fundamental importance. To address
this problem, many active learning (AL) methods have been proposed that employ
up-to-date detectors to retrieve representative minority samples according to
predefined confidence or uncertainty thresholds. However, these AL methods
cause the detectors to ignore the remaining majority samples (i.e., those with
low uncertainty or high prediction confidence). In this work, by developing a
principled active sample mining (ASM) framework, we demonstrate that
cost-effectively mining samples from these unlabeled majority data is key to
training more powerful object detectors while minimizing user effort.
Specifically, our ASM framework involves a switchable sample selection
mechanism for determining whether an unlabeled sample should be manually
annotated via AL or automatically pseudo-labeled via a novel self-learning
process. The proposed process can be compatible with mini-batch based training
(i.e., using a batch of unlabeled or partially labeled data as a one-time
input) for object detection. In addition, a few samples with low-confidence
predictions are selected and annotated via AL. Notably, our method is suitable
for object categories that are not seen in the unlabeled data during the
learning process. Extensive experiments clearly demonstrate that our ASM
framework can achieve performance comparable to that of alternative methods but
with significantly fewer annotations.
Learning Goal-Oriented Visual Dialog via Tempered Policy Gradient
Authors: Rui Zhao, Volker Tresp
Date: 2018-07-02 00:00:00
Tasks: Deep Reinforcement Learning, Policy Gradient Methods, Reinforcement Learning, Reinforcement Learning (RL), Visual Dialog

Learning goal-oriented dialogues by means of deep reinforcement learning has recently become a popular research topic. However, commonly used policy-based dialogue agents often end up focusing on simple utterances and suboptimal policies. To mitigate this problem, we propose a class of novel temperature-based extensions for policy gradient methods, which are referred to as Tempered Policy Gradients (TPGs). On a recent AI-testbed, i.e., the GuessWhat?! game, we achieve significant improvements with two innovations. The first one is an extension of the state-of-the-art solutions with Seq2Seq and Memory Network structures that leads to an improvement of 7%. The second one is the application of our newly developed TPG methods, which improves the performance additionally by around 5% and, even more importantly, helps produce more convincing utterances.
Classifying Data with Local Hamiltonians
Authors: Johannes Bausch
Date: 2018-07-02 00:00:00
Tasks: General Classification

The goal of this work is to define a notion of a quantum neural network to
classify data, which exploits the low energy spectrum of a local Hamiltonian.
As a concrete application, we build a binary classifier, train it on some
actual data and then test its performance on a simple classification task. More
specifically, we use Microsoft's quantum simulator, Liquid, to construct local
Hamiltonians that can encode trained classifier functions in their ground
space, and which can be probed by measuring the overlap with test states
corresponding to the data to be classified. To obtain such a classifier
Hamiltonian, we further propose a training scheme based on quantum annealing
which is completely closed-off to the environment and which does not depend on
external measurements until the very end, avoiding unnecessary decoherence
during the annealing procedure. For a network of size n, the trained network
can be stored as a list of O(n) coupling strengths. We address the question of
which interactions are most suitable for a given classification task, and
develop a qubit-saving optimization for the training procedure on a simulated
annealing device. Furthermore, a small neural network to classify colors into
red vs. blue is trained and tested, and benchmarked against the annealing
parameters.
Deepcode: Feedback Codes via Deep Learning
Authors: Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
Date: 2018-07-02 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Deep Learning

The design of codes for communicating reliably over a statistically well
defined channel is an important endeavor involving deep mathematical research
and wide-ranging practical applications. In this work, we present the first
family of codes obtained via deep learning, which significantly beats
state-of-the-art codes designed over several decades of research. The
communication channel under consideration is the Gaussian noise channel with
feedback, whose study was initiated by Shannon; feedback is known theoretically
to improve reliability of communication, but no practical codes that do so have
ever been successfully constructed.
  We break this logjam by integrating information theoretic insights
harmoniously with recurrent-neural-network based encoders and decoders to
create novel codes that outperform known codes by 3 orders of magnitude in
reliability. We also demonstrate several desirable properties of the codes: (a)
generalization to larger block lengths, (b) composability with known codes, (c)
adaptation to practical constraints. This result also has broader ramifications
for coding theory: even when the channel has a clear mathematical model, deep
learning methodologies, when combined with channel-specific
information-theoretic insights, can potentially beat state-of-the-art codes
constructed over decades of mathematical research.
Pragmatic approach to structured data querying via natural language interface
Authors: Aliaksei Vertsel, Mikhail Rumiantsau
Date: 2018-07-02 00:00:00
Tasks: Information Retrieval, Natural Language Queries, Retrieval

As the use of technology increases and data analysis becomes integral in many
businesses, the ability to quickly access and interpret data has become more
important than ever. Information retrieval technologies are being utilized by
organizations and companies to manage their information systems and processes.
Despite information retrieval of a large amount of data being efficient
organized in relational databases, a user still needs to master the DB
language/schema to completely formulate the queries. This puts a burden on
organizations and companies to hire employees that are proficient in DB
languages/schemas to formulate queries. To reduce some of the burden on already
overstretched data teams, many organizations are looking for tools that allow
non-developers to query their databases. Unfortunately, writing a valid SQL
query that answers the question a user is trying to ask isn't always easy. Even
seemingly simple questions, like "Which start-up companies received more than
$200M in funding?" can actually be very hard to answer, let alone convert into
a SQL query. How do you define start-up companies? By size, location, duration
of time they have been incorporated? This may be fine if a user is working with
a database they're already familiar with, but what if users are not familiar
with the database. What is needed is a centralized system that can effectively
translate natural language queries into specific database queries for different
customer database types. There is a number of factors that can dramatically
affect the system architecture and the set of algorithms used to translate NL
queries into a structured query representation.
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices
Authors: Till Speicher, Hoda Heidari, Nina Grgic-Hlaca, Krishna P. Gummadi, Adish Singla, Adrian Weller, Muhammad Bilal Zafar
Date: 2018-07-02 00:00:00
Tasks: Decision Making, Fairness

Discrimination via algorithmic decision making has received considerable
attention. Prior work largely focuses on defining conditions for fairness, but
does not define satisfactory measures of algorithmic unfairness. In this paper,
we focus on the following question: Given two unfair algorithms, how should we
determine which of the two is more unfair? Our core idea is to use existing
inequality indices from economics to measure how unequally the outcomes of an
algorithm benefit different individuals or groups in a population. Our work
offers a justified and general framework to compare and contrast the
(un)fairness of algorithmic predictors. This unifying approach enables us to
quantify unfairness both at the individual and the group level. Further, our
work reveals overlooked tradeoffs between different fairness notions: using our
proposed measures, the overall individual-level unfairness of an algorithm can
be decomposed into a between-group and a within-group component. Earlier
methods are typically designed to tackle only between-group unfairness, which
may be justified for legal or other reasons. However, we demonstrate that
minimizing exclusively the between-group component may, in fact, increase the
within-group, and hence the overall unfairness. We characterize and illustrate
the tradeoffs between our measures of (un)fairness and the prediction accuracy.
Ambient Hidden Space of Generative Adversarial Networks
Authors: Xinhan Di, Pengqian Yu, Meng Tian
Date: 2018-07-02 00:00:00

Generative adversarial models are powerful tools to model structure in
complex distributions for a variety of tasks. Current techniques for learning
generative models require an access to samples which have high quality, and
advanced generative models are applied to generate samples from noisy training
data through ambient modules. However, the modules are only practical for the
output space of the generator, and their application in the hidden space is not
well studied. In this paper, we extend the ambient module to the hidden space
of the generator, and provide the uniqueness condition and the corresponding
strategy for the ambient hidden generator in the adversarial training process.
We report the practicality of the proposed method on the benchmark dataset.
Representation Mapping: A Novel Approach to Generate High-Quality Multi-Lingual Emotion Lexicons
Authors: Sven Buechel, Udo Hahn
Date: 2018-07-02 00:00:00
Proceeding: LREC 2018 5
Tasks: Sentiment Analysis

In the past years, sentiment analysis has increasingly shifted attention to
representational frameworks more expressive than semantic polarity (being
positive, negative or neutral). However, these richer formats (like Basic
Emotions or Valence-Arousal-Dominance, and variants therefrom), rooted in
psychological research, tend to proliferate the number of representation
schemes for emotion encoding. Thus, a large amount of representationally
incompatible emotion lexicons has been developed by various research groups
adopting one or the other emotion representation format. As a consequence, the
reusability of these resources decreases as does the comparability of systems
using them. In this paper, we propose to solve this dilemma by methods and
tools which map different representation formats onto each other for the sake
of mutual compatibility and interoperability of language resources. We present
the first large-scale investigation of such representation mappings for four
typologically diverse languages and find evidence that our approach produces
(near-)gold quality emotion lexicons, even in cross-lingual settings. Finally,
we use our models to create new lexicons for eight typologically diverse
languages.
Path Finding for the Coalition of Co-operative Agents Acting in the Environment with Destructible Obstacles
Authors: Anton Andreychuk, Konstantin Yakovlev
Date: 2018-07-02 00:00:00
Tasks: Heuristic Search

The problem of planning a set of paths for the coalition of robots (agents)
with different capabilities is considered in the paper. Some agents can modify
the environment by destructing the obstacles thus allowing the other ones to
shorten their paths to the goal. As a result the mutual solution of lower cost,
e.g. time to completion, may be acquired. We suggest an original procedure to
identify the obstacles for further removal that can be embedded into almost any
heuristic search planner (we use Theta*) and evaluate it empirically. Results
of the evaluation show that time-to-complete the mission can be decreased up to
9-12 % by utilizing the proposed technique.
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration
Authors: Gellért Weisz, András György, Csaba Szepesvári
Date: 2018-07-02 00:00:00
Proceeding: ICML 2018

We consider the problem of configuring general-purpose solvers to run
efficiently on problem instances drawn from an unknown distribution. The goal
of the configurator is to find a configuration that runs fast on average on
most instances, and do so with the least amount of total work. It can run a
chosen solver on a random instance until the solver finishes or a timeout is
reached. We propose LeapsAndBounds, an algorithm that tests configurations on
randomly selected problem instances for longer and longer time. We prove that
the capped expected runtime of the configuration returned by LeapsAndBounds is
close to the optimal expected runtime, while our algorithm's running time is
near-optimal. Our results show that LeapsAndBounds is more efficient than the
recent algorithm of Kleinberg et al. (2017), which, to our knowledge, is the
only other algorithm configuration method with non-trivial theoretical
guarantees. Experimental results on configuring a public SAT solver on a new
benchmark dataset also stand witness to the superiority of our method.
Waveform to Single Sinusoid Regression to Estimate the F0 Contour from Noisy Speech Using Recurrent Deep Neural Networks
Authors: Akihiro Kato, Tomi Kinnunen
Date: 2018-07-02 00:00:00

The fundamental frequency (F0) represents pitch in speech that determines
prosodic characteristics of speech and is needed in various tasks for speech
analysis and synthesis. Despite decades of research on this topic, F0
estimation at low signal-to-noise ratios (SNRs) in unexpected noise conditions
remains difficult. This work proposes a new approach to noise robust F0
estimation using a recurrent neural network (RNN) trained in a supervised
manner. Recent studies employ deep neural networks (DNNs) for F0 tracking as a
frame-by-frame classification task into quantised frequency states but we
propose waveform-to-sinusoid regression instead to achieve both noise
robustness and accurate estimation with increased frequency resolution.
  Experimental results with PTDB-TUG corpus contaminated by additive noise
(NOISEX-92) demonstrate that the proposed method improves gross pitch error
(GPE) rate and fine pitch error (FPE) by more than 35 % at SNRs between -10 dB
and +10 dB compared with well-known noise robust F0 tracker, PEFAC.
Furthermore, the proposed method also outperforms state-of-the-art DNN-based
approaches by more than 15 % in terms of both FPE and GPE rate over the
preceding SNR range.
Understanding the Effectiveness of Lipschitz-Continuity in Generative Adversarial Nets
Authors: Zhiming Zhou, Yuxuan Song, Lantao Yu, Hongwei Wang, Jiadong Liang, Wei-Nan Zhang, Zhihua Zhang, Yong Yu
Date: 2018-07-02 00:00:00
Tasks: valid

In this paper, we investigate the underlying factor that leads to failure and
success in the training of GANs. We study the property of the optimal
discriminative function and show that in many GANs, the gradient from the
optimal discriminative function is not reliable, which turns out to be the
fundamental cause of failure in training of GANs. We further demonstrate that a
well-defined distance metric does not necessarily guarantee the convergence of
GANs. Finally, we prove in this paper that Lipschitz-continuity condition is a
general solution to make the gradient of the optimal discriminative function
reliable, and characterized the necessary condition where Lipschitz-continuity
ensures the convergence, which leads to a broad family of valid GAN objectives
under Lipschitz-continuity condition, where Wasserstein distance is one special
case. We experiment with several new objectives, which are sound according to
our theorems, and we found that, compared with Wasserstein distance, the
outputs of the discriminator with new objectives are more stable and the final
qualities of generated samples are also consistently higher than those produced
by Wasserstein distance.
Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data
Authors: Michael A. Hedderich, Dietrich Klakow
Date: 2018-07-02 00:00:00
Proceeding: WS 2018 7
Tasks: NER

Manually labeled corpora are expensive to create and often not available for
low-resource languages or domains. Automatic labeling approaches are an
alternative way to obtain labeled data in a quicker and cheaper way. However,
these labels often contain more errors which can deteriorate a classifier's
performance when trained on this data. We propose a noise layer that is added
to a neural network architecture. This allows modeling the noise and train on a
combination of clean and noisy data. We show that in a low-resource NER task we
can improve performance by up to 35% by using additional, noisy data and
handling the noise.
Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm
Authors: Marcel Gehrke, Tanya Braun, Ralf Möller
Date: 2018-07-02 00:00:00

The lifted dynamic junction tree algorithm (LDJT) efficiently answers
filtering and prediction queries for probabilistic relational temporal models
by building and then reusing a first-order cluster representation of a
knowledge base for multiple queries and time steps. Unfortunately, a non-ideal
elimination order can lead to groundings even though a lifted run is possible
for a model. We extend LDJT (i) to identify unnecessary groundings while
proceeding in time and (ii) to prevent groundings by delaying eliminations
through changes in a temporal first-order cluster representation. The extended
version of LDJT answers multiple temporal queries orders of magnitude faster
than the original version.
Fusing First-order Knowledge Compilation and the Lifted Junction Tree Algorithm
Authors: Tanya Braun, Ralf Möller
Date: 2018-07-02 00:00:00

Standard approaches for inference in probabilistic formalisms with
first-order constructs include lifted variable elimination (LVE) for single
queries as well as first-order knowledge compilation (FOKC) based on weighted
model counting. To handle multiple queries efficiently, the lifted junction
tree algorithm (LJT) uses a first-order cluster representation of a model and
LVE as a subroutine in its computations. For certain inputs, the
implementations of LVE and, as a result, LJT ground parts of a model where FOKC
has a lifted run. The purpose of this paper is to prepare LJT as a backbone for
lifted inference and to use any exact inference algorithm as subroutine. Using
FOKC in LJT allows us to compute answers faster than LJT, LVE, and FOKC for
certain inputs.
The relativistic discriminator: a key element missing from standard GAN
Authors: Alexia Jolicoeur-Martineau
Date: 2018-07-02 00:00:00
Proceeding: ICLR 2019 5
Tasks: Generative Adversarial Network, Image Generation

In standard generative adversarial network (SGAN), the discriminator
estimates the probability that the input data is real. The generator is trained
to increase the probability that fake data is real. We argue that it should
also simultaneously decrease the probability that real data is real because 1)
this would account for a priori knowledge that half of the data in the
mini-batch is fake, 2) this would be observed with divergence minimization, and
3) in optimal settings, SGAN would be equivalent to integral probability metric
(IPM) GANs.
  We show that this property can be induced by using a relativistic
discriminator which estimate the probability that the given real data is more
realistic than a randomly sampled fake data. We also present a variant in which
the discriminator estimate the probability that the given real data is more
realistic than fake data, on average. We generalize both approaches to
non-standard GAN loss functions and we refer to them respectively as
Relativistic GANs (RGANs) and Relativistic average GANs (RaGANs). We show that
IPM-based GANs are a subset of RGANs which use the identity function.
  Empirically, we observe that 1) RGANs and RaGANs are significantly more
stable and generate higher quality data samples than their non-relativistic
counterparts, 2) Standard RaGAN with gradient penalty generate data of better
quality than WGAN-GP while only requiring a single discriminator update per
generator update (reducing the time taken for reaching the state-of-the-art by
400%), and 3) RaGANs are able to generate plausible high resolutions images
(256x256) from a very small sample (N=2011), while GAN and LSGAN cannot; these
images are of significantly better quality than the ones generated by WGAN-GP
and SGAN with spectral normalization.
Transparent, Efficient, and Robust Word Embedding Access with WOMBAT
Authors: Mark-Christoph Müller, Michael Strube
Date: 2018-07-02 00:00:00
Proceeding: COLING 2018 8
Tasks: Sentence, Sentence Similarity, Word Embeddings

We present WOMBAT, a Python tool which supports NLP practitioners in
accessing word embeddings from code. WOMBAT addresses common research problems,
including unified access, scaling, and robust and reproducible preprocessing.
Code that uses WOMBAT for accessing word embeddings is not only cleaner, more
readable, and easier to reuse, but also much more efficient than code using
standard in-memory methods: a Python script using WOMBAT for evaluating seven
large word embedding collections (8.7M embedding vectors in total) on a simple
SemEval sentence similarity task involving 250 raw sentence pairs completes in
under ten seconds end-to-end on a standard notebook computer.