pwc-archive/papers-with-abstracts huggingface dataset:
Style and Content Disentanglement in Generative Adversarial Networks
Authors: Hadi Kazemi, Seyed Mehdi Iranmanesh, Nasser M. Nasrabadi
Date: 2018-11-14 00:00:00
Tasks: Disentanglement, Generative Adversarial Network, Image Generation

Disentangling factors of variation within data has become a very challenging
problem for image generation tasks. Current frameworks for training a
Generative Adversarial Network (GAN), learn to disentangle the representations
of the data in an unsupervised fashion and capture the most significant factors
of the data variations. However, these approaches ignore the principle of
content and style disentanglement in image generation, which means their
learned latent code may alter the content and style of the generated images at
the same time. This paper describes the Style and Content Disentangled GAN
(SC-GAN), a new unsupervised algorithm for training GANs that learns
disentangled style and content representations of the data. We assume that the
representation of an image can be decomposed into a content code that
represents the geometrical information of the data, and a style code that
captures textural properties. Consequently, by fixing the style portion of the
latent representation, we can generate diverse images in a particular style.
Reversely, we can set the content code and generate a specific scene in a
variety of styles. The proposed SC-GAN has two components: a content code which
is the input to the generator, and a style code which modifies the scene style
through modification of the Adaptive Instance Normalization (AdaIN) layers'
parameters. We evaluate the proposed SC-GAN framework on a set of baseline
datasets.
Improving Distantly Supervised Relation Extraction with Neural Noise Converter and Conditional Optimal Selector
Authors: Shanchan Wu, Kai Fan, Qiong Zhang
Date: 2018-11-14 00:00:00
Tasks: Relation, Relation Extraction

Distant supervised relation extraction has been successfully applied to large
corpus with thousands of relations. However, the inevitable wrong labeling
problem by distant supervision will hurt the performance of relation
extraction. In this paper, we propose a method with neural noise converter to
alleviate the impact of noisy data, and a conditional optimal selector to make
proper prediction. Our noise converter learns the structured transition matrix
on logit level and captures the property of distant supervised relation
extraction dataset. The conditional optimal selector on the other hand helps to
make proper prediction decision of an entity pair even if the group of
sentences is overwhelmed by no-relation sentences. We conduct experiments on a
widely used dataset and the results show significant improvement over
competitive baseline methods.
SepNE: Bringing Separability to Network Embedding
Authors: Ziyao Li, Liang Zhang, Guojie Song
Date: 2018-11-14 00:00:00
Tasks: Network Embedding

Many successful methods have been proposed for learning low dimensional
representations on large-scale networks, while almost all existing methods are
designed in inseparable processes, learning embeddings for entire networks even
when only a small proportion of nodes are of interest. This leads to great
inconvenience, especially on super-large or dynamic networks, where these
methods become almost impossible to implement. In this paper, we formalize the
problem of separated matrix factorization, based on which we elaborate a novel
objective function that preserves both local and global information. We further
propose SepNE, a simple and flexible network embedding algorithm which
independently learns representations for different subsets of nodes in
separated processes. By implementing separability, our algorithm reduces the
redundant efforts to embed irrelevant nodes, yielding scalability to
super-large networks, automatic implementation in distributed learning and
further adaptations. We demonstrate the effectiveness of this approach on
several real-world networks with different scales and subjects. With comparable
accuracy, our approach significantly outperforms state-of-the-art baselines in
running times on large networks.
Bayesian Reinforcement Learning in Factored POMDPs
Authors: Sammie Katt, Frans Oliehoek, Christopher Amato
Date: 2018-11-14 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Bayesian approaches provide a principled solution to the
exploration-exploitation trade-off in Reinforcement Learning. Typical
approaches, however, either assume a fully observable environment or scale
poorly. This work introduces the Factored Bayes-Adaptive POMDP model, a
framework that is able to exploit the underlying structure while learning the
dynamics in partially observable systems. We also present a belief tracking
method to approximate the joint posterior over state and model variables, and
an adaptation of the Monte-Carlo Tree Search solution method, which together
are capable of solving the underlying problem near-optimally. Our method is
able to learn efficiently given a known factorization or also learn the
factorization and the model parameters at the same time. We demonstrate that
this approach is able to outperform current methods and tackle problems that
were previously infeasible.
Extractive Summary as Discrete Latent Variables
Authors: Aran Komatsuzaki
Date: 2018-11-14 00:00:00
Tasks: Language Modeling, Language Modelling, Text Generation

In this paper, we compare various methods to compress a text using a neural
model. We find that extracting tokens as latent variables significantly
outperforms the state-of-the-art discrete latent variable models such as
VQ-VAE. Furthermore, we compare various extractive compression schemes. There
are two best-performing methods that perform equally. One method is to simply
choose the tokens with the highest tf-idf scores. Another is to train a
bidirectional language model similar to ELMo and choose the tokens with the
highest loss. If we consider any subsequence of a text to be a text in a
broader sense, we conclude that language is a strong compression code of
itself. Our finding justifies the high quality of generation achieved with
hierarchical method, as their latent variables are nothing but natural language
summary. We also conclude that there is a hierarchy in language such that an
entire text can be predicted much more easily based on a sequence of a small
number of keywords, which can be easily found by classical methods as tf-idf.
We speculate that this extraction process may be useful for unsupervised
hierarchical text generation.
TrolleyMod v1.0: An Open-Source Simulation and Data-Collection Platform for Ethical Decision Making in Autonomous Vehicles
Authors: Vahid Behzadan, James Minton, Arslan Munir
Date: 2018-11-14 00:00:00
Tasks: Autonomous Vehicles, Decision Making, Deep Reinforcement Learning, Reinforcement Learning

This paper presents TrolleyMod v1.0, an open-source platform based on the
CARLA simulator for the collection of ethical decision-making data for
autonomous vehicles. This platform is designed to facilitate experiments aiming
to observe and record human decisions and actions in high-fidelity simulations
of ethical dilemmas that occur in the context of driving. Targeting experiments
in the class of trolley problems, TrolleyMod provides a seamless approach to
creating new experimental settings and environments with the realistic
physics-engine and the high-quality graphical capabilities of CARLA and the
Unreal Engine. Also, TrolleyMod provides a straightforward interface between
the CARLA environment and Python to enable the implementation of custom
controllers, such as deep reinforcement learning agents. The results of such
experiments can be used for sociological analyses, as well as the training and
tuning of value-aligned autonomous vehicles based on social values that are
inferred from observations.
Controllability, Multiplexing, and Transfer Learning in Networks using Evolutionary Learning
Authors: Rise Ooi, Chao-Han Huck Yang, Pin-Yu Chen, Vìctor Eguìluz, Narsis Kiani, Hector Zenil, David Gomez-Cabrero, Jesper Tegnèr
Date: 2018-11-14 00:00:00
Tasks: Transfer Learning

Networks are fundamental building blocks for representing data, and computations. Remarkable progress in learning in structurally defined (shallow or deep) networks has recently been achieved. Here we introduce evolutionary exploratory search and learning method of topologically flexible networks under the constraint of producing elementary computational steady-state input-output operations. Our results include; (1) the identification of networks, over four orders of magnitude, implementing computation of steady-state input-output functions, such as a band-pass filter, a threshold function, and an inverse band-pass function. Next, (2) the learned networks are technically controllable as only a small number of driver nodes are required to move the system to a new state. Furthermore, we find that the fraction of required driver nodes is constant during evolutionary learning, suggesting a stable system design. (3), our framework allows multiplexing of different computations using the same network. For example, using a binary representation of the inputs, the network can readily compute three different input-output functions. Finally, (4) the proposed evolutionary learning demonstrates transfer learning. If the system learns one function A, then learning B requires on average less number of steps as compared to learning B from tabula rasa. We conclude that the constrained evolutionary learning produces large robust controllable circuits, capable of multiplexing and transfer learning. Our study suggests that network-based computations of steady-state functions, representing either cellular modules of cell-to-cell communication networks or internal molecular circuits communicating within a cell, could be a powerful model for biologically inspired computing. This complements conceptualizations such as attractor based models, or reservoir computing.
Emergence of Addictive Behaviors in Reinforcement Learning Agents
Authors: Vahid Behzadan, Roman V. Yampolskiy, Arslan Munir
Date: 2018-11-14 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents a novel approach to the technical analysis of wireheading
in intelligent agents. Inspired by the natural analogues of wireheading and
their prevalent manifestations, we propose the modeling of such phenomenon in
Reinforcement Learning (RL) agents as psychological disorders. In a preliminary
step towards evaluating this proposal, we study the feasibility and dynamics of
emergent addictive policies in Q-learning agents in the tractable environment
of the game of Snake. We consider a slightly modified settings for this game,
in which the environment provides a "drug" seed alongside the original
"healthy" seed for the consumption of the snake. We adopt and extend an
RL-based model of natural addiction to Q-learning agents in this settings, and
derive sufficient parametric conditions for the emergence of addictive
behaviors in such agents. Furthermore, we evaluate our theoretical analysis
with three sets of simulation-based experiments. The results demonstrate the
feasibility of addictive wireheading in RL agents, and provide promising venues
of further research on the psychopathological modeling of complex AI safety
problems.
YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers
Authors: Jonathan Pedoeem, Rachel Huang
Date: 2018-11-14 00:00:00
Tasks: GPU, Object, object-detection, Object Detection, Real-Time Object Detection

This paper focuses on YOLO-LITE, a real-time object detection model developed
to run on portable devices such as a laptop or cellphone lacking a Graphics
Processing Unit (GPU). The model was first trained on the PASCAL VOC dataset
then on the COCO dataset, achieving a mAP of 33.81% and 12.26% respectively.
YOLO-LITE runs at about 21 FPS on a non-GPU computer and 10 FPS after
implemented onto a website with only 7 layers and 482 million FLOPS. This speed
is 3.8x faster than the fastest state of art model, SSD MobilenetvI. Based on
the original object detection algorithm YOLOV2, YOLO- LITE was designed to
create a smaller, faster, and more efficient model increasing the accessibility
of real-time object detection to a variety of devices.
Aequitas: A Bias and Fairness Audit Toolkit
Authors: Pedro Saleiro, Benedict Kuester, Loren Hinkson, Jesse London, Abby Stevens, Ari Anisfeld, Kit T. Rodolfa, Rayid Ghani
Date: 2018-11-14 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Fairness

Recent work has raised concerns on the risk of unintended bias in AI systems
being used nowadays that can affect individuals unfairly based on race, gender
or religion, among other possible characteristics. While a lot of bias metrics
and fairness definitions have been proposed in recent years, there is no
consensus on which metric/definition should be used and there are very few
available resources to operationalize them. Therefore, despite recent
awareness, auditing for bias and fairness when developing and deploying AI
systems is not yet a standard practice. We present Aequitas, an open source
bias and fairness audit toolkit that is an intuitive and easy to use addition
to the machine learning workflow, enabling users to seamlessly test models for
several bias and fairness metrics in relation to multiple population
sub-groups. Aequitas facilitates informed and equitable decisions around
developing and deploying algorithmic decision making systems for both data
scientists, machine learning researchers and policymakers.
Cross-lingual Short-text Matching with Deep Learning
Authors: Asmelash Teka Hadgu
Date: 2018-11-13 00:00:00
Tasks: Deep Learning, Feature Engineering, Question Answering, Text Matching

The problem of short text matching is formulated as follows: given a pair of
sentences or questions, a matching model determines whether the input pair mean
the same or not. Models that can automatically identify questions with the same
meaning have a wide range of applications in question answering sites and
modern chatbots. In this article, we describe the approach by team hahu to
solve this problem in the context of the "CIKM AnalytiCup 2018 - Cross-lingual
Short-text Matching of Question Pairs" that is sponsored by Alibaba. Our
solution is an end-to-end system based on current advances in deep learning
which avoids heavy feature-engineering and achieves improved performance over
traditional machine-learning approaches. The log-loss scores for the first and
second rounds of the contest are 0.35 and 0.39 respectively. The team was
ranked 7th from 1027 teams in the overall ranking scheme by the organizers that
consisted of the two contest scores as well as: innovation and system
integrity, understanding data as well as practicality of the solution for
business.
Text Assisted Insight Ranking Using Context-Aware Memory Network
Authors: Qi Zeng, Liangchen Luo, Wenhao Huang, Yang Tang
Date: 2018-11-13 00:00:00

Extracting valuable facts or informative summaries from multi-dimensional
tables, i.e. insight mining, is an important task in data analysis and business
intelligence. However, ranking the importance of insights remains a challenging
and unexplored task. The main challenge is that explicitly scoring an insight
or giving it a rank requires a thorough understanding of the tables and costs a
lot of manual efforts, which leads to the lack of available training data for
the insight ranking problem. In this paper, we propose an insight ranking model
that consists of two parts: A neural ranking model explores the data
characteristics, such as the header semantics and the data statistical
features, and a memory network model introduces table structure and context
information into the ranking process. We also build a dataset with text
assistance. Experimental results show that our approach largely improves the
ranking precision as reported in multi evaluation metrics.
Corpus Phonetics Tutorial
Authors: Eleanor Chodroff
Date: 2018-11-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Corpus phonetics has become an increasingly popular method of research in
linguistic analysis. With advances in speech technology and computational
power, large scale processing of speech data has become a viable technique.
This tutorial introduces the speech scientist and engineer to various automatic
speech processing tools. These include acoustic model creation and forced
alignment using the Kaldi Automatic Speech Recognition Toolkit (Povey et al.,
2011), forced alignment using FAVE-align (Rosenfelder et al., 2014), the
Montreal Forced Aligner (McAuliffe et al., 2017), and the Penn Phonetics Lab
Forced Aligner (Yuan & Liberman, 2008), as well as stop consonant burst
alignment using AutoVOT (Keshet et al., 2014). The tutorial provides a general
overview of each program, step-by-step instructions for running the program, as
well as several tips and tricks.
Neural Wavetable: a playable wavetable synthesizer using neural networks
Authors: Lamtharn Hantrakul, Li-Chia Yang
Date: 2018-11-13 00:00:00

We present Neural Wavetable, a proof-of-concept wavetable synthesizer that
uses neural networks to generate playable wavetables. The system can produce
new, distinct waveforms through the interpolation of traditional wavetables in
an autoencoder's latent space. It is available as a VST/AU plugin for use in a
Digital Audio Workstation.
An Analysis of the Semantic Annotation Task on the Linked Data Cloud
Authors: Gagnon Michel, Zouaq Amal, Aranha Francisco, Ensan Faezeh, Jean-Louis Ludovic
Date: 2018-11-13 00:00:00
Tasks: Information Retrieval, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Retrieval

Semantic annotation, the process of identifying key-phrases in texts and
linking them to concepts in a knowledge base, is an important basis for
semantic information retrieval and the Semantic Web uptake. Despite the
emergence of semantic annotation systems, very few comparative studies have
been published on their performance. In this paper, we provide an evaluation of
the performance of existing systems over three tasks: full semantic annotation,
named entity recognition, and keyword detection. More specifically, the
spotting capability (recognition of relevant surface forms in text) is
evaluated for all three tasks, whereas the disambiguation (correctly
associating an entity from Wikipedia or DBpedia to the spotted surface forms)
is evaluated only for the first two tasks. Our evaluation is twofold: First, we
compute standard precision and recall on the output of semantic annotators on
diverse datasets, each best suited for one of the identified tasks. Second, we
build a statistical model using logistic regression to identify significant
performance differences. Our results show that systems that provide full
annotation perform better than named entities annotators and keyword
extractors, for all three tasks. However, there is still much room for
improvement for the identification of the most relevant entities described in a
text.
Discourse in Multimedia: A Case Study in Information Extraction
Authors: Mrinmaya Sachan, Kumar Avinava Dubey, Eduard H. Hovy, Tom M. Mitchell, Dan Roth, Eric P. Xing
Date: 2018-11-13 00:00:00

To ensure readability, text is often written and presented with due
formatting. These text formatting devices help the writer to effectively convey
the narrative. At the same time, these help the readers pick up the structure
of the discourse and comprehend the conveyed information. There have been a
number of linguistic theories on discourse structure of text. However, these
theories only consider unformatted text. Multimedia text contains rich
formatting features which can be leveraged for various NLP tasks. In this
paper, we study some of these discourse features in multimedia text and what
communicative function they fulfil in the context. We examine how these
multimedia discourse features can be used to improve an information extraction
system. We show that the discourse and text layout features provide information
that is complementary to lexical semantic information commonly used for
information extraction. As a case study, we use these features to harvest
structured subject knowledge of geometry from textbooks. We show that the
harvested structured knowledge can be used to improve an existing solver for
geometry problems, making it more accurate as well as more explainable.
Data Driven Governing Equations Approximation Using Deep Neural Networks
Authors: Tong Qin, Kailiang Wu, Dongbin Xiu
Date: 2018-11-13 00:00:00

We present a numerical framework for approximating unknown governing
equations using observation data and deep neural networks (DNN). In particular,
we propose to use residual network (ResNet) as the basic building block for
equation approximation. We demonstrate that the ResNet block can be considered
as a one-step method that is exact in temporal integration. We then present two
multi-step methods, recurrent ResNet (RT-ResNet) method and recursive ReNet
(RS-ResNet) method. The RT-ResNet is a multi-step method on uniform time steps,
whereas the RS-ResNet is an adaptive multi-step method using variable time
steps. All three methods presented here are based on integral form of the
underlying dynamical system. As a result, they do not require time derivative
data for equation recovery and can cope with relatively coarsely distributed
trajectory data. Several numerical examples are presented to demonstrate the
performance of the methods.
Staging Human-computer Dialogs: An Application of the Futamura Projections
Authors: Brandon M. Williams, Saverio Perugini
Date: 2018-11-13 00:00:00

We demonstrate an application of the Futamura Projections to human-computer
interaction, and particularly to staging human-computer dialogs. Specifically,
by providing staging analogs to the classical Futamura Projections, we
demonstrate that the Futamura Projections can be applied to the staging of
human-computer dialogs in addition to the execution of programs.
Interactive dimensionality reduction using similarity projections
Authors: Dimitris Spathis, Nikolaos Passalis, Anastasios Tefas
Date: 2018-11-13 00:00:00
Tasks: Clustering, Dimensionality Reduction, Domain Adaptation, Transfer Learning

Recent advances in machine learning allow us to analyze and describe the
content of high-dimensional data like text, audio, images or other signals. In
order to visualize that data in 2D or 3D, usually Dimensionality Reduction (DR)
techniques are employed. Most of these techniques, e.g., PCA or t-SNE, produce
static projections without taking into account corrections from humans or other
data exploration scenarios. In this work, we propose the interactive Similarity
Projection (iSP), a novel interactive DR framework based on similarity
embeddings, where we form a differentiable objective based on the user
interactions and perform learning using gradient descent, with an end-to-end
trainable architecture. Two interaction scenarios are evaluated. First, a
common methodology in multidimensional projection is to project a subset of
data, arrange them in classes or clusters, and project the rest unseen dataset
based on that manipulation, in a kind of semi-supervised interpolation. We
report results that outperform competitive baselines in a wide range of metrics
and datasets. Second, we explore the scenario of manipulating some classes,
while enriching the optimization with high-dimensional neighbor information.
Apart from improving classification precision and clustering on images and text
documents, the new emerging structure of the projection unveils semantic
manifolds. For example, on the Head Pose dataset, by just dragging the faces
looking far left to the left and those looking far right to the right, all
faces are re-arranged on a continuum even on the vertical axis (face up and
down). This end-to-end framework can be used for fast, visual semi-supervised
learning, manifold exploration, interactive domain adaptation of neural
embeddings and transfer learning.