pwc-archive/papers-with-abstracts huggingface dataset:
End-to-End Deep Reinforcement Learning for Lane Keeping Assist
Authors: Ahmad El Sallab, Mohammed Abdou, Etienne Perot, Senthil Yogamani
Date: 2016-12-13 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning is considered to be a strong AI paradigm which can be
used to teach machines through interaction with the environment and learning
from their mistakes, but it has not yet been successfully used for automotive
applications. There has recently been a revival of interest in the topic,
however, driven by the ability of deep learning algorithms to learn good
representations of the environment. Motivated by Google DeepMind's successful
demonstrations of learning for games from Breakout to Go, we will propose
different methods for autonomous driving using deep reinforcement learning.
This is of particular interest as it is difficult to pose autonomous driving as
a supervised learning problem as it has a strong interaction with the
environment including other vehicles, pedestrians and roadworks. As this is a
relatively new area of research for autonomous driving, we will formulate two
main categories of algorithms: 1) Discrete actions category, and 2) Continuous
actions category. For the discrete actions category, we will deal with Deep
Q-Network Algorithm (DQN) while for the continuous actions category, we will
deal with Deep Deterministic Actor Critic Algorithm (DDAC). In addition to
that, We will also discover the performance of these two categories on an open
source car simulator for Racing called (TORCS) which stands for The Open Racing
car Simulator. Our simulation results demonstrate learning of autonomous
maneuvering in a scenario of complex road curvatures and simple interaction
with other vehicles. Finally, we explain the effect of some restricted
conditions, put on the car during the learning phase, on the convergence time
for finishing its learning phase.
Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks
Authors: Qinglong Wang, Wenbo Guo, Alexander G. Ororbia II, Xinyu Xing, Lin Lin, C. Lee Giles, Xue Liu, Peng Liu, Gang Xiong
Date: 2016-10-06 00:00:00
Tasks: Autonomous Vehicles, Dimensionality Reduction, speech-recognition, Speech Recognition

Deep neural networks have proven to be quite effective in a wide variety of
machine learning tasks, ranging from improved speech recognition systems to
advancing the development of autonomous vehicles. However, despite their
superior performance in many applications, these models have been recently
shown to be susceptible to a particular type of attack possible through the
generation of particular synthetic examples referred to as adversarial samples.
These samples are constructed by manipulating real examples from the training
data distribution in order to "fool" the original neural model, resulting in
misclassification (with high confidence) of previously correctly classified
samples. Addressing this weakness is of utmost importance if deep neural
architectures are to be applied to critical applications, such as those in the
domain of cybersecurity. In this paper, we present an analysis of this
fundamental flaw lurking in all neural architectures to uncover limitations of
previously proposed defense mechanisms. More importantly, we present a unifying
framework for protecting deep neural models using a non-invertible data
transformation--developing two adversary-resilient architectures utilizing both
linear and nonlinear dimensionality reduction. Empirical results indicate that
our framework provides better robustness compared to state-of-art solutions
while having negligible degradation in accuracy.
Fast Patch-based Style Transfer of Arbitrary Style
Authors: Tian Qi Chen, Mark Schmidt
Date: 2016-12-13 00:00:00
Tasks: Image Generation, Style Transfer

Artistic style transfer is an image synthesis problem where the content of an
image is reproduced with the style of another. Recent works show that a
visually appealing style transfer can be achieved by using the hidden
activations of a pretrained convolutional neural network. However, existing
methods either apply (i) an optimization procedure that works for any style
image but is very expensive, or (ii) an efficient feedforward network that only
allows a limited number of trained styles. In this work we propose a simpler
optimization objective based on local matching that combines the content
structure and style textures in a single layer of the pretrained network. We
show that our objective has desirable properties such as a simpler optimization
landscape, intuitive parameter tuning, and consistent frame-by-frame
performance on video. Furthermore, we use 80,000 natural images and 80,000
paintings to train an inverse network that approximates the result of the
optimization. This results in a procedure for artistic style transfer that is
efficient but also allows arbitrary content and style images.
Incorporating Human Domain Knowledge into Large Scale Cost Function Learning
Authors: Markus Wulfmeier, Dushyant Rao, Ingmar Posner
Date: 2016-12-13 00:00:00
Tasks: Motion Planning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent advances have shown the capability of Fully Convolutional Neural
Networks (FCN) to model cost functions for motion planning in the context of
learning driving preferences purely based on demonstration data from human
drivers. While pure learning from demonstrations in the framework of Inverse
Reinforcement Learning (IRL) is a promising approach, we can benefit from well
informed human priors and incorporate them into the learning process. Our work
achieves this by pretraining a model to regress to a manual cost function and
refining it based on Maximum Entropy Deep Inverse Reinforcement Learning. When
injecting prior knowledge as pretraining for the network, we achieve higher
robustness, more visually distinct obstacle boundaries, and the ability to
capture instances of obstacles that elude models that purely learn from
demonstration data. Furthermore, by exploiting these human priors, the
resulting model can more accurately handle corner cases that are scarcely seen
in the demonstration data, such as stairs, slopes, and underpasses.
Combining Maps and Street Level Images for Building Height and Facade Estimation
Authors: Jiangye Yuan, Anil M. Cheriyadat
Date: 2016-01-28 00:00:00
Tasks: Position

We propose a method that integrates two widely available data sources,
building footprints from 2D maps and street level images, to derive valuable
information that is generally difficult to acquire -- building heights and
building facade masks in images. Building footprints are elevated in world
coordinates and projected onto images. Building heights are estimated by
scoring projected footprints based on their alignment with building features in
images. Building footprints with estimated heights can be converted to simple
3D building models, which are projected back to images to identify buildings.
In this procedure, accurate camera projections are critical. However, camera
position errors inherited from external sensors commonly exist, which adversely
affect results. We derive a solution to precisely locate cameras on maps using
correspondence between image features and building footprints. Experiments on
real-world datasets show the promise of our method.
Towards Adaptive Training of Agent-based Sparring Partners for Fighter Pilots
Authors: Brett W. Israelsen, Nisar Ahmed, Kenneth Center, Roderick Green, Winston Bennett Jr
Date: 2016-12-13 00:00:00
Tasks: Bayesian Optimization

A key requirement for the current generation of artificial decision-makers is
that they should adapt well to changes in unexpected situations. This paper
addresses the situation in which an AI for aerial dog fighting, with tunable
parameters that govern its behavior, must optimize behavior with respect to an
objective function that is evaluated and learned through simulations. Bayesian
optimization with a Gaussian Process surrogate is used as the method for
investigating the objective function. One key benefit is that during
optimization, the Gaussian Process learns a global estimate of the true
objective function, with predicted outcomes and a statistical measure of
confidence in areas that haven't been investigated yet. Having a model of the
objective function is important for being able to understand possible outcomes
in the decision space; for example this is crucial for training and providing
feedback to human pilots. However, standard Bayesian optimization does not
perform consistently or provide an accurate Gaussian Process surrogate function
for highly volatile objective functions. We treat these problems by introducing
a novel sampling technique called Hybrid Repeat/Multi-point Sampling. This
technique gives the AI ability to learn optimum behaviors in a highly uncertain
environment. More importantly, it not only improves the reliability of the
optimization, but also creates a better model of the entire objective surface.
With this improved model the agent is equipped to more accurately/efficiently
predict performance in unexplored scenarios.
Algorithms for Graph-Constrained Coalition Formation in the Real World
Authors: Filippo Bistaffa, Alessandro Farinelli, Jesús Cerquides, Juan A. Rodríguez-Aguilar, Sarvapali D. Ramchurn
Date: 2016-12-13 00:00:00

Coalition formation typically involves the coming together of multiple,
heterogeneous, agents to achieve both their individual and collective goals. In
this paper, we focus on a special case of coalition formation known as
Graph-Constrained Coalition Formation (GCCF) whereby a network connecting the
agents constrains the formation of coalitions. We focus on this type of problem
given that in many real-world applications, agents may be connected by a
communication network or only trust certain peers in their social network. We
propose a novel representation of this problem based on the concept of edge
contraction, which allows us to model the search space induced by the GCCF
problem as a rooted tree. Then, we propose an anytime solution algorithm
(CFSS), which is particularly efficient when applied to a general class of
characteristic functions called $m+a$ functions. Moreover, we show how CFSS can
be efficiently parallelised to solve GCCF using a non-redundant partition of
the search space. We benchmark CFSS on both synthetic and realistic scenarios,
using a real-world dataset consisting of the energy consumption of a large
number of households in the UK. Our results show that, in the best case, the
serial version of CFSS is 4 orders of magnitude faster than the state of the
art, while the parallel version is 9.44 times faster than the serial version on
a 12-core machine. Moreover, CFSS is the first approach to provide anytime
approximate solutions with quality guarantees for very large systems of agents
(i.e., with more than 2700 agents).
Application of Advanced Record Linkage Techniques for Complex Population Reconstruction
Authors: Peter Christen
Date: 2016-12-13 00:00:00

Record linkage is the process of identifying records that refer to the same
entities from several databases. This process is challenging because commonly
no unique entity identifiers are available. Linkage therefore has to rely on
partially identifying attributes, such as names and addresses of people. Recent
years have seen the development of novel techniques for linking data from
diverse application areas, where a major focus has been on linking complex data
that contain records about different types of entities. Advanced approaches
that exploit both the similarities between record attributes as well as the
relationships between entities to identify clusters of matching records have
been developed.
  In this application paper we study the novel problem where rather than
different types of entities we have databases where the same entity can have
different roles, and where these roles change over time. We specifically
develop novel techniques for linking historical birth, death, marriage and
census records with the aim to reconstruct the population covered by these
records over a period of several decades. Our experimental evaluation on real
Scottish data shows that even with advanced linkage techniques that consider
group, relationship, and temporal aspects it is challenging to achieve high
quality linkage from such complex data.
DeepCancer: Detecting Cancer through Gene Expressions via Deep Generative Learning
Authors: Rajendra Rana Bhat, Vivek Viswanath, Xiaolin Li
Date: 2016-12-09 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Transcriptional profiling on microarrays to obtain gene expressions has been
used to facilitate cancer diagnosis. We propose a deep generative machine
learning architecture (called DeepCancer) that learn features from unlabeled
microarray data. These models have been used in conjunction with conventional
classifiers that perform classification of the tissue samples as either being
cancerous or non-cancerous. The proposed model has been tested on two different
clinical datasets. The evaluation demonstrates that DeepCancer model achieves a
very high precision score, while significantly controlling the false positive
and false negative scores.
3D Simulation for Robot Arm Control with Deep Q-Learning
Authors: Stephen James, Edward Johns
Date: 2016-09-13 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, Reinforcement Learning

Recent trends in robot arm control have seen a shift towards end-to-end
solutions, using deep reinforcement learning to learn a controller directly
from raw sensor data, rather than relying on a hand-crafted, modular pipeline.
However, the high dimensionality of the state space often means that it is
impractical to generate sufficient training data with real-world experiments.
As an alternative solution, we propose to learn a robot controller in
simulation, with the potential of then transferring this to a real robot.
Building upon the recent success of deep Q-networks, we present an approach
which uses 3D simulations to train a 7-DOF robotic arm in a control task
without any prior knowledge. The controller accepts images of the environment
as its only input, and outputs motor actions for the task of locating and
grasping a cube, over a range of initial configurations. To encourage efficient
learning, a structured reward function is designed with intermediate rewards.
We also present preliminary results in direct transfer of policies over to a
real robot, without any further training.
Neuro-symbolic representation learning on biological knowledge graphs
Authors: Mona Alshahrani, Mohammed Asif Khan, Omar Maddouri, Akira R Kinjo, Núria Queralt-Rosinach, Robert Hoehndorf
Date: 2016-12-13 00:00:00
Tasks: Data Integration, Knowledge Graphs, Representation Learning, Retrieval

Motivation: Biological data and knowledge bases increasingly rely on Semantic
Web technologies and the use of knowledge graphs for data integration,
retrieval and federated queries. In the past years, feature learning methods
that are applicable to graph-structured data are becoming available, but have
not yet widely been applied and evaluated on structured biological knowledge.
Results: We develop a novel method for feature learning on biological knowledge
graphs. Our method combines symbolic methods, in particular knowledge
representation using symbolic logic and automated reasoning, with neural
networks to generate embeddings of nodes that encode for related information
within knowledge graphs. Through the use of symbolic logic, these embeddings
contain both explicit and implicit information. We apply these embeddings to
the prediction of edges in the knowledge graph representing problems of
function prediction, finding candidate genes of diseases, protein-protein
interactions, or drug target relations, and demonstrate performance that
matches and sometimes outperforms traditional approaches based on manually
crafted features. Our method can be applied to any biological knowledge graph,
and will thereby open up the increasing amount of Semantic Web based knowledge
bases in biology to use in machine learning and data analytics. Availability
and Implementation:
https://github.com/bio-ontology-research-group/walking-rdf-and-owl Contact:
robert.hoehndorf@kaust.edu.sa
TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning
Authors: Yuan Tang
Date: 2016-12-13 00:00:00
Tasks: BIG-bench Machine Learning, Vocal Bursts Intensity Prediction

TF.Learn is a high-level Python module for distributed machine learning
inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to
simplify the process of creating, configuring, training, evaluating, and
experimenting a machine learning model. TF.Learn integrates a wide range of
state-of-art machine learning algorithms built on top of TensorFlow's low level
APIs for small to large-scale supervised and unsupervised problems. This module
focuses on bringing machine learning to non-specialists using a general-purpose
high-level language as well as researchers who want to implement, benchmark,
and compare their new methods in a structured environment. Emphasis is put on
ease of use, performance, documentation, and API consistency.
Multi-Perspective Context Matching for Machine Comprehension
Authors: Zhiguo Wang, Haitao Mi, Wael Hamza, Radu Florian
Date: 2016-12-13 00:00:00
Tasks: Question Answering, Reading Comprehension

Previous machine comprehension (MC) datasets are either too small to train
end-to-end deep learning models, or not difficult enough to evaluate the
ability of current MC techniques. The newly released SQuAD dataset alleviates
these limitations, and gives us a chance to develop more realistic MC models.
Based on this dataset, we propose a Multi-Perspective Context Matching (MPCM)
model, which is an end-to-end system that directly predicts the answer
beginning and ending points in a passage. Our model first adjusts each
word-embedding vector in the passage by multiplying a relevancy weight computed
against the question. Then, we encode the question and weighted passage by
using bi-directional LSTMs. For each point in the passage, our model matches
the context of this point against the encoded question from multiple
perspectives and produces a matching vector. Given those matched vectors, we
employ another bi-directional LSTM to aggregate all the information and predict
the beginning and ending points. Experimental result on the test set of SQuAD
shows that our model achieves a competitive result on the leaderboard.
DeepMind Lab
Authors: Charles Beattie, Joel Z. Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, Julian Schrittwieser, Keith Anderson, Sarah York, Max Cant, Adam Cain, Adrian Bolton, Stephen Gaffney, Helen King, Demis Hassabis, Shane Legg, Stig Petersen
Date: 2016-12-12 00:00:00
Tasks: BIG-bench Machine Learning

DeepMind Lab is a first-person 3D game platform designed for research and
development of general artificial intelligence and machine learning systems.
DeepMind Lab can be used to study how autonomous artificial agents may learn
complex tasks in large, partially observed, and visually diverse worlds.
DeepMind Lab has a simple and flexible API enabling creative task-designs and
novel AI-designs to be explored and quickly iterated upon. It is powered by a
fast and widely recognised game engine, and tailored for effective use by the
research community.
Vicinity-Driven Paragraph and Sentence Alignment for Comparable Corpora
Authors: Gustavo Henrique Paetzold, Lucia Specia
Date: 2016-12-13 00:00:00
Tasks: Sentence, Text Simplification

Parallel corpora have driven great progress in the field of Text
Simplification. However, most sentence alignment algorithms either offer a
limited range of alignment types supported, or simply ignore valuable clues
present in comparable documents. We address this problem by introducing a new
set of flexible vicinity-driven paragraph and sentence alignment algorithms
that 1-N, N-1, N-N and long distance null alignments without the need for
hard-to-replicate supervised models.
Parsimonious Online Learning with Kernels via Sparse Projections in Function Space
Authors: Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro
Date: 2016-12-13 00:00:00
Tasks: General Classification

Despite their attractiveness, popular perception is that techniques for
nonparametric function approximation do not scale to streaming data due to an
intractable growth in the amount of storage they require. To solve this problem
in a memory-affordable way, we propose an online technique based on functional
stochastic gradient descent in tandem with supervised sparsification based on
greedy function subspace projections. The method, called parsimonious online
learning with kernels (POLK), provides a controllable tradeoff? between its
solution accuracy and the amount of memory it requires. We derive conditions
under which the generated function sequence converges almost surely to the
optimal function, and we establish that the memory requirement remains finite.
We evaluate POLK for kernel multi-class logistic regression and kernel
hinge-loss classification on three canonical data sets: a synthetic Gaussian
mixture model, the MNIST hand-written digits, and the Brodatz texture database.
On all three tasks, we observe a favorable tradeoff of objective function
evaluation, classification performance, and complexity of the nonparametric
regressor extracted the proposed method.
Observation of dynamics inside an unlabeled live cell using bright-field photon microscopy: Evaluation of organelles' trajectories
Authors: Renata Rychtarikova, Dalibor Stys
Date: 2016-12-13 00:00:00

This article presents an algorithm for the evaluation of organelles'
movements inside of an unmodified live cell. We used a time-lapse image series
obtained using wide-field bright-field photon transmission microscopy as an
algorithm input. The benefit of the algorithm is the application of the R\'enyi
information entropy, namely a variable called a point information gain, which
enables to highlight the borders of the intracellular organelles and to
localize the organelles' centers of mass with the precision of one pixel.
Corporate Disruption in the Science of Machine Learning
Authors: Sam Work
Date: 2016-12-13 00:00:00
Tasks: BIG-bench Machine Learning

This MSc dissertation considers the effects of the current corporate interest
on researchers in the field of machine learning. Situated within the field's
cyclical history of academic, public and corporate interest, this dissertation
investigates how current researchers view recent developments and negotiate
their own research practices within an environment of increased commercial
interest and funding. The original research consists of in-depth interviews
with 12 machine learning researchers working in both academia and industry.
Building on theory from science, technology and society studies, this
dissertation problematizes the traditional narratives of the neoliberalization
of academic research by allowing the researchers themselves to discuss how
their career choices, working environments and interactions with others in the
field have been affected by the reinvigorated corporate interest of recent
years.