pwc-archive/papers-with-abstracts huggingface dataset:
From Audio to Semantics: Approaches to end-to-end spoken language understanding
Authors: Parisa Haghani, Arun Narayanan, Michiel Bacchiani, Galen Chuang, Neeraj Gaur, Pedro Moreno, Rohit Prabhavalkar, Zhongdi Qu, Austin Waters
Date: 2018-09-24 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Natural Language Understanding, speech-recognition, Speech Recognition, Spoken Language Understanding

Conventional spoken language understanding systems consist of two main
components: an automatic speech recognition module that converts audio to a
transcript, and a natural language understanding module that transforms the
resulting text (or top N hypotheses) into a set of domains, intents, and
arguments. These modules are typically optimized independently. In this paper,
we formulate audio to semantic understanding as a sequence-to-sequence problem
[1]. We propose and compare various encoder-decoder based approaches that
optimize both modules jointly, in an end-to-end manner. Evaluations on a
real-world task show that 1) having an intermediate text representation is
crucial for the quality of the predicted semantics, especially the intent
arguments and 2) jointly optimizing the full system improves overall accuracy
of prediction. Compared to independently trained models, our best jointly
trained model achieves similar domain and intent prediction F1 scores, but
improves argument word error rate by 18% relative.
Zoom-RNN: A Novel Method for Person Recognition Using Recurrent Neural Networks
Authors: Sina Mokhtarzadeh Azar, Sajjad Azami, Mina Ghadimi Atigh, Mohammad Javadi, Ahmad Nickabadi
Date: 2018-09-24 00:00:00
Tasks: Person Recognition

The overwhelming popularity of social media has resulted in bulk amounts of
personal photos being uploaded to the internet every day. Since these photos
are taken in unconstrained settings, recognizing the identities of people among
the photos remains a challenge. Studies have indicated that utilizing evidence
other than face appearance improves the performance of person recognition
systems. In this work, we aim to take advantage of additional cues obtained
from different body regions in a zooming in fashion for person recognition.
Hence, we present Zoom-RNN, a novel method based on recurrent neural networks
for combining evidence extracted from the whole body, upper body, and head
regions. Our model is evaluated on a challenging dataset, namely People In
Photo Albums (PIPA), and we demonstrate that employing our system improves the
performance of conventional fusion methods by a noticeable margin.
Numerical Aspects for Approximating Governing Equations Using Data
Authors: Kailiang Wu, Dongbin Xiu
Date: 2018-09-24 00:00:00

We present effective numerical algorithms for locally recovering unknown
governing differential equations from measurement data. We employ a set of
standard basis functions, e.g., polynomials, to approximate the governing
equation with high accuracy. Upon recasting the problem into a function
approximation problem, we discuss several important aspects for accurate
approximation. Most notably, we discuss the importance of using a large number
of short bursts of trajectory data, rather than using data from a single long
trajectory. Several options for the numerical algorithms to perform accurate
approximation are then presented, along with an error estimate of the final
equation approximation. We then present an extensive set of numerical examples
of both linear and nonlinear systems to demonstrate the properties and
effectiveness of our equation recovery algorithms.
Locally Private Learning without Interaction Requires Separation
Authors: Amit Daniely, Vitaly Feldman
Date: 2018-09-24 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: PAC learning

We consider learning under the constraint of local differential privacy (LDP). For many learning problems known efficient algorithms in this model require many rounds of communication between the server and the clients holding the data points. Yet multi-round protocols are prohibitively slow in practice due to network latency and, as a result, currently deployed large-scale systems are limited to a single round. Despite significant research interest, very little is known about which learning problems can be solved by such non-interactive systems. The only lower bound we are aware of is for PAC learning an artificial class of functions with respect to a uniform distribution (Kasiviswanathan et al. 2011). We show that the margin complexity of a class of Boolean functions is a lower bound on the complexity of any non-interactive LDP algorithm for distribution-independent PAC learning of the class. In particular, the classes of linear separators and decision lists require exponential number of samples to learn non-interactively even though they can be learned in polynomial time by an interactive LDP algorithm. This gives the first example of a natural problem that is significantly harder to solve without interaction and also resolves an open problem of Kasiviswanathan et al. (2011). We complement this lower bound with a new efficient learning algorithm whose complexity is polynomial in the margin complexity of the class. Our algorithm is non-interactive on labeled samples but still needs interactive access to unlabeled samples. All of our results also apply to the statistical query model and any model in which the number of bits communicated about each data point is constrained.
Real-Time Monocular Object-Model Aware Sparse SLAM
Authors: Mehdi Hosseinzadeh, Kejie Li, Yasir Latif, Ian Reid
Date: 2018-09-24 00:00:00
Tasks: Camera Localization, model, Object, object-detection, Object Detection, Semantic SLAM, Simultaneous Localization and Mapping

Simultaneous Localization And Mapping (SLAM) is a fundamental problem in
mobile robotics. While sparse point-based SLAM methods provide accurate camera
localization, the generated maps lack semantic information. On the other hand,
state of the art object detection methods provide rich information about
entities present in the scene from a single image. This work incorporates a
real-time deep-learned object detector to the monocular SLAM framework for
representing generic objects as quadrics that permit detections to be
seamlessly integrated while allowing the real-time performance. Finer
reconstruction of an object, learned by a CNN network, is also incorporated and
provides a shape prior for the quadric leading further refinement. To capture
the dominant structure of the scene, additional planar landmarks are detected
by a CNN-based plane detector and modeled as independent landmarks in the map.
Extensive experiments support our proposed inclusion of semantic objects and
planar structures directly in the bundle-adjustment of SLAM - Semantic SLAM -
that enriches the reconstructed map semantically, while significantly improving
the camera localization. The performance of our SLAM system is demonstrated in
https://youtu.be/UMWXd4sHONw and https://youtu.be/QPQqVrvP0dE .
Better Safe than Sorry: Evidence Accumulation Allows for Safe Reinforcement Learning
Authors: Akshat Agarwal, Abhinau Kumar V, Kyle Dunovan, Erik Peterson, Timothy Verstynen, Katia Sycara
Date: 2018-09-24 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

In the real world, agents often have to operate in situations with incomplete
information, limited sensing capabilities, and inherently stochastic
environments, making individual observations incomplete and unreliable.
Moreover, in many situations it is preferable to delay a decision rather than
run the risk of making a bad decision. In such situations it is necessary to
aggregate information before taking an action; however, most state of the art
reinforcement learning (RL) algorithms are biased towards taking actions
\textit{at every time step}, even if the agent is not particularly confident in
its chosen action. This lack of caution can lead the agent to make critical
mistakes, regardless of prior experience and acclimation to the environment.
Motivated by theories of dynamic resolution of uncertainty during decision
making in biological brains, we propose a simple accumulator module which
accumulates evidence in favor of each possible decision, encodes uncertainty as
a dynamic competition between actions, and acts on the environment only when it
is sufficiently confident in the chosen action. The agent makes no decision by
default, and the burden of proof to make a decision falls on the policy to
accrue evidence strongly in favor of a single decision. Our results show that
this accumulator module achieves near-optimal performance on a simple guessing
game, far outperforming deep recurrent networks using traditional, forced
action selection policies.
EpiRL: A Reinforcement Learning Agent to Facilitate Epistasis Detection
Authors: Kexin Huang, Rodrigo Nogueira
Date: 2018-09-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Epistasis (gene-gene interaction) is crucial to predicting genetic disease.
Our work tackles the computational challenges faced by previous works in
epistasis detection by modeling it as a one-step Markov Decision Process where
the state is genome data, the actions are the interacted genes, and the reward
is an interaction measurement for the selected actions. A reinforcement
learning agent using policy gradient method then learns to discover a set of
highly interacted genes.
Text Summarization as Tree Transduction by Top-Down TreeLSTM
Authors: Davide Bacciu, Antonio Bruno
Date: 2018-09-24 00:00:00
Tasks: Sentence, Sentence Compression, Text Summarization

Extractive compression is a challenging natural language processing problem.
This work contributes by formulating neural extractive compression as a parse
tree transduction problem, rather than a sequence transduction task. Motivated
by this, we introduce a deep neural model for learning
structure-to-substructure tree transductions by extending the standard Long
Short-Term Memory, considering the parent-child relationships in the structural
recursion. The proposed model can achieve state of the art performance on
sentence compression benchmarks, both in terms of accuracy and compression
rate.
A family of neighborhood contingency logics
Authors: Jie Fan
Date: 2018-09-24 00:00:00

This article proposes the axiomatizations of contingency logics of various
natural classes of neighborhood frames. In particular, by defining a suitable
canonical neighborhood function, we give sound and complete axiomatizations of
monotone contingency logic and regular contingency logic, thereby answering two
open questions raised by Bakhtiari, van Ditmarsch, and Hansen. The canonical
function is inspired by a function proposed by Kuhn in~1995. We show that
Kuhn's function is actually equal to a related function originally given by
Humberstone.
Central Bank Communication and the Yield Curve: A Semi-Automatic Approach using Non-Negative Matrix Factorization
Authors: Ancil Crayton
Date: 2018-09-24 00:00:00

Communication is now a standard tool in the central bank's monetary policy
toolkit. Theoretically, communication provides the central bank an opportunity
to guide public expectations, and it has been shown empirically that central
bank communication can lead to financial market fluctuations. However, there
has been little research into which dimensions or topics of information are
most important in causing these fluctuations. We develop a semi-automatic
methodology that summarizes the FOMC statements into its main themes,
automatically selects the best model based on coherency, and assesses whether
there is a significant impact of these themes on the shape of the U.S Treasury
yield curve using topic modeling methods from the machine learning literature.
Our findings suggest that the FOMC statements can be decomposed into three
topics: (i) information related to the economic conditions and the mandates,
(ii) information related to monetary policy tools and intermediate targets, and
(iii) information related to financial markets and the financial crisis. We
find that statements are most influential during the financial crisis and the
effects are mostly present in the curvature of the yield curve through
information related to the financial theme.
Recognizing Film Entities in Podcasts
Authors: Ahmet Salih Gundogdu, Arjun Sanghvi, Keith Harrigian
Date: 2018-09-24 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

In this paper, we propose a Named Entity Recognition (NER) system to identify
film titles in podcast audio. Taking inspiration from NER systems for noisy
text in social media, we implement a two-stage approach that is robust to
computer transcription errors and does not require significant computational
expense to accommodate new film titles/releases. Evaluating on a diverse set of
podcasts, we demonstrate more than a 20% increase in F1 score across three
baseline approaches when combining fuzzy-matching with a linear model aware of
film-specific metadata.
On Reinforcement Learning for Full-length Game of StarCraft
Authors: Zhen-Jia Pang, Ruo-Ze Liu, Zhou-Yu Meng, Yi Zhang, Yang Yu, Tong Lu
Date: 2018-09-23 00:00:00
Tasks: CPU, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft, Starcraft II, Transfer Learning

StarCraft II poses a grand challenge for reinforcement learning. The main
difficulties of it include huge state and action space and a long-time horizon.
In this paper, we investigate a hierarchical reinforcement learning approach
for StarCraft II. The hierarchy involves two levels of abstraction. One is the
macro-action automatically extracted from expert's trajectories, which reduces
the action space in an order of magnitude yet remains effective. The other is a
two-layer hierarchical architecture which is modular and easy to scale,
enabling a curriculum transferring from simpler tasks to more complex tasks.
The reinforcement training algorithm for this architecture is also
investigated. On a 64x64 map and using restrictive units, we achieve a winning
rate of more than 99\% against the difficulty level-1 built-in AI. Through the
curriculum transfer learning algorithm and a mixture of combat model, we can
achieve over 93\% winning rate of Protoss against the most difficult
non-cheating built-in AI (level-7) of Terran, training within two days using a
single machine with only 48 CPU cores and 8 K40 GPUs. It also shows strong
generalization performance, when tested against never seen opponents including
cheating levels built-in AI and all levels of Zerg and Protoss built-in AI. We
hope this study could shed some light on the future research of large-scale
reinforcement learning.
Night-to-Day Image Translation for Retrieval-based Localization
Authors: Asha Anoosheh, Torsten Sattler, Radu Timofte, Marc Pollefeys, Luc van Gool
Date: 2018-09-26 00:00:00
Tasks: Image Retrieval, Position, Retrieval, Style Generalization, Translation, Visual Localization

Visual localization is a key step in many robotics pipelines, allowing the
robot to (approximately) determine its position and orientation in the world.
An efficient and scalable approach to visual localization is to use image
retrieval techniques. These approaches identify the image most similar to a
query photo in a database of geo-tagged images and approximate the query's pose
via the pose of the retrieved database image. However, image retrieval across
drastically different illumination conditions, e.g. day and night, is still a
problem with unsatisfactory results, even in this age of powerful neural
models. This is due to a lack of a suitably diverse dataset with true
correspondences to perform end-to-end learning. A recent class of neural models
allows for realistic translation of images among visual domains with relatively
little training data and, most importantly, without ground-truth pairings. In
this paper, we explore the task of accurately localizing images captured from
two traversals of the same area in both day and night. We propose ToDayGAN - a
modified image-translation model to alter nighttime driving images to a more
useful daytime representation. We then compare the daytime and translated night
images to obtain a pose estimate for the night image using the known 6-DOF
position of the closest day image. Our approach improves localization
performance by over 250% compared the current state-of-the-art, in the context
of standard metrics in multiple categories.
RF-Based Direction Finding of UAVs Using DNN
Authors: Samith Abeywickrama, Lahiru Jayasinghe, Hua Fu, Subashini Nissanka, Chau Yuen
Date: 2017-12-01 00:00:00
Tasks: Denoising

This paper presents a sparse denoising autoencoder (SDAE)-based deep neural
network (DNN) for the direction finding (DF) of small unmanned aerial vehicles
(UAVs). It is motivated by the practical challenges associated with classical
DF algorithms such as MUSIC and ESPRIT. The proposed DF scheme is practical and
low-complex in the sense that a phase synchronization mechanism, an antenna
calibration mechanism, and the analytical model of the antenna radiation
pattern are not essential. Also, the proposed DF method can be implemented
using a single-channel RF receiver. The paper validates the proposed method
experimentally as well.
Learning Navigation Behaviors End-to-End with AutoRL
Authors: Hao-Tien Lewis Chiang, Aleksandra Faust, Marek Fiser, Anthony Francis
Date: 2018-09-26 00:00:00
Tasks: Deep Reinforcement Learning, Motion Planning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We learn end-to-end point-to-point and path-following navigation behaviors
that avoid moving obstacles. These policies receive noisy lidar observations
and output robot linear and angular velocities. The policies are trained in
small, static environments with AutoRL, an evolutionary automation layer around
Reinforcement Learning (RL) that searches for a deep RL reward and neural
network architecture with large-scale hyper-parameter optimization. AutoRL
first finds a reward that maximizes task completion, and then finds a neural
network architecture that maximizes the cumulative of the found reward.
Empirical evaluations, both in simulation and on-robot, show that AutoRL
policies do not suffer from the catastrophic forgetfulness that plagues many
other deep reinforcement learning algorithms, generalize to new environments
and moving obstacles, are robust to sensor, actuator, and localization noise,
and can serve as robust building blocks for larger navigation tasks. Our
path-following and point-to-point policies are respectively 23% and 26% more
successful than comparison methods across new environments. Video at:
https://youtu.be/0UwkjpUEcbI
Safely Learning to Control the Constrained Linear Quadratic Regulator
Authors: Sarah Dean, Stephen Tu, Nikolai Matni, Benjamin Recht
Date: 2018-09-26 00:00:00

We study the constrained linear quadratic regulator with unknown dynamics, addressing the tension between safety and exploration in data-driven control techniques. We present a framework which allows for system identification through persistent excitation, while maintaining safety by guaranteeing the satisfaction of state and input constraints. This framework involves a novel method for synthesizing robust constraint-satisfying feedback controllers, leveraging newly developed tools from system level synthesis. We connect statistical results with cost sub-optimality bounds to give non-asymptotic guarantees on both estimation and controller performance.
From Classical to Generalized Zero-Shot Learning: a Simple Adaptation Process
Authors: Yannick Le Cacheux, Hervé Le Borgne, Michel Crucianu
Date: 2018-09-26 00:00:00
Tasks: Generalized Zero-Shot Learning, Zero-Shot Learning

Zero-shot learning (ZSL) is concerned with the recognition of previously
unseen classes. It relies on additional semantic knowledge for which a mapping
can be learned with training examples of seen classes. While classical ZSL
considers the recognition performance on unseen classes only, generalized
zero-shot learning (GZSL) aims at maximizing performance on both seen and
unseen classes. In this paper, we propose a new process for training and
evaluation in the GZSL setting; this process addresses the gap in performance
between samples from unseen and seen classes by penalizing the latter, and
enables to select hyper-parameters well-suited to the GZSL task. It can be
applied to any existing ZSL approach and leads to a significant performance
boost: the experimental evaluation shows that GZSL performance, averaged over
eight state-of-the-art methods, is improved from 28.5 to 42.2 on CUB and from
28.2 to 57.1 on AwA2.
Convolutional Neural Networks for Video Quality Assessment
Authors: Michalis Giannopoulos, Grigorios Tsagkatakis, Saverio Blasi, Farzad Toutounchi, Athanasios Mouchtaris, Panagiotis Tsakalides, Marta Mrak, Ebroul Izquierdo
Date: 2018-09-26 00:00:00
Tasks: Video Quality Assessment, Visual Question Answering (VQA)

Video Quality Assessment (VQA) is a very challenging task due to its highly
subjective nature. Moreover, many factors influence VQA. Compression of video
content, while necessary for minimising transmission and storage requirements,
introduces distortions which can have detrimental effects on the perceived
quality. Especially when dealing with modern video coding standards, it is
extremely difficult to model the effects of compression due to the
unpredictability of encoding on different content types. Moreover, transmission
also introduces delays and other distortion types which affect the perceived
quality. Therefore, it would be highly beneficial to accurately predict the
perceived quality of video to be distributed over modern content distribution
platforms, so that specific actions could be undertaken to maximise the Quality
of Experience (QoE) of the users. Traditional VQA techniques based on feature
extraction and modelling may not be sufficiently accurate. In this paper, a
novel Deep Learning (DL) framework is introduced for effectively predicting VQA
of video content delivery mechanisms based on end-to-end feature learning. The
proposed framework is based on Convolutional Neural Networks, taking into
account compression distortion as well as transmission delays. Training and
evaluation of the proposed framework are performed on a user annotated VQA
dataset specifically created to undertake this work. The experiments show that
the proposed methods can lead to high accuracy of the quality estimation,
showcasing the potential of using DL in complex VQA scenarios.
Redundant Perception and State Estimation for Reliable Autonomous Racing
Authors: Nikhil Bharadwaj Gosala, Andreas Bühler, Manish Prajapat, Claas Ehmke, Mehak Gupta, Ramya Sivanesan, Abel Gawel, Mark Pfeiffer, Mathias Bürki, Inkyu Sa, Renaud Dubé, Roland Siegwart
Date: 2018-09-26 00:00:00
Tasks: Autonomous Racing, State Estimation

In autonomous racing, vehicles operate close to the limits of handling and a
sensor failure can have critical consequences. To limit the impact of such
failures, this paper presents the redundant perception and state estimation
approaches developed for an autonomous race car. Redundancy in perception is
achieved by estimating the color and position of the track delimiting objects
using two sensor modalities independently. Specifically, learning-based
approaches are used to generate color and pose estimates, from LiDAR and camera
data respectively. The redundant perception inputs are fused by a particle
filter based SLAM algorithm that operates in real-time. Velocity is estimated
using slip dynamics, with reliability being ensured through a probabilistic
failure detection algorithm. The sub-modules are extensively evaluated in
real-world racing conditions using the autonomous race car "gotthard
driverless", achieving lateral accelerations up to 1.7G and a top speed of
90km/h.