pwc-archive/papers-with-abstracts huggingface dataset:
Embedded Bandits for Large-Scale Black-Box Optimization
Authors: Abdullah Al-Dujaili, S. Suresh
Date: 2016-11-27 00:00:00

Random embedding has been applied with empirical success to large-scale
black-box optimization problems with low effective dimensions. This paper
proposes the EmbeddedHunter algorithm, which incorporates the technique in a
hierarchical stochastic bandit setting, following the optimism in the face of
uncertainty principle and breaking away from the multiple-run framework in
which random embedding has been conventionally applied similar to stochastic
black-box optimization solvers. Our proposition is motivated by the bounded
mean variation in the objective value for a low-dimensional point projected
randomly into the decision space of Lipschitz-continuous problems. In essence,
the EmbeddedHunter algorithm expands optimistically a partitioning tree over a
low-dimensional---equal to the effective dimension of the problem---search
space based on a bounded number of random embeddings of sampled points from the
low-dimensional space. In contrast to the probabilistic theoretical guarantees
of multiple-run random-embedding algorithms, the finite-time analysis of the
proposed algorithm presents a theoretical upper bound on the regret as a
function of the algorithm's number of iterations. Furthermore, numerical
experiments were conducted to validate its performance. The results show a
clear performance gain over recently proposed random embedding methods for
large-scale problems, provided the intrinsic dimensionality is low.
FusionNet: 3D Object Classification Using Multiple Data Representations
Authors: Vishakh Hegde, Reza Zadeh
Date: 2016-07-19 00:00:00
Tasks: 3D Object Classification, 3D Object Recognition, Classification, General Classification, Object, Object Recognition

High-quality 3D object recognition is an important component of many vision
and robotics systems. We tackle the object recognition problem using two data
representations, to achieve leading results on the Princeton ModelNet
challenge. The two representations: 1. Volumetric representation: the 3D object
is discretized spatially as binary voxels - $1$ if the voxel is occupied and
$0$ otherwise. 2. Pixel representation: the 3D object is represented as a set
of projected 2D pixel images. Current leading submissions to the ModelNet
Challenge use Convolutional Neural Networks (CNNs) on pixel representations.
However, we diverge from this trend and additionally, use Volumetric CNNs to
bridge the gap between the efficiency of the above two representations. We
combine both representations and exploit them to learn new features, which
yield a significantly better classifier than using either of the
representations in isolation. To do this, we introduce new Volumetric CNN
(V-CNN) architectures.
Fill it up: Exploiting partial dependency annotations in a minimum spanning tree parser
Authors: Liang Sun, Jason Mielens, Jason Baldridge
Date: 2016-11-26 00:00:00
Tasks: Dependency Parsing

Unsupervised models of dependency parsing typically require large amounts of
clean, unlabeled data plus gold-standard part-of-speech tags. Adding indirect
supervision (e.g. language universals and rules) can help, but we show that
obtaining small amounts of direct supervision - here, partial dependency
annotations - provides a strong balance between zero and full supervision. We
adapt the unsupervised ConvexMST dependency parser to learn from partial
dependencies expressed in the Graph Fragment Language. With less than 24 hours
of total annotation, we obtain 7% and 17% absolute improvement in unlabeled
dependency scores for English and Spanish, respectively, compared to the same
parser using only universal grammar constraints.
What Can Be Predicted from Six Seconds of Driver Glances?
Authors: Lex Fridman, Heishiro Toyoda, Sean Seaman, Bobbie Seppelt, Linda Angell, Joonbum Lee, Bruce Mehler, Bryan Reimer
Date: 2016-11-26 00:00:00
Tasks: Binary Classification, Gaze Estimation

We consider a large dataset of real-world, on-road driving from a 100-car
naturalistic study to explore the predictive power of driver glances and,
specifically, to answer the following question: what can be predicted about the
state of the driver and the state of the driving environment from a 6-second
sequence of macro-glances? The context-based nature of such glances allows for
application of supervised learning to the problem of vision-based gaze
estimation, making it robust, accurate, and reliable in messy, real-world
conditions. So, it's valuable to ask whether such macro-glances can be used to
infer behavioral, environmental, and demographic variables? We analyze 27
binary classification problems based on these variables. The takeaway is that
glance can be used as part of a multi-sensor real-time system to predict
radio-tuning, fatigue state, failure to signal, talking, and several
environment variables.
Deep Learning for the Classification of Lung Nodules
Authors: He Yang, Hengyong Yu, Ge Wang
Date: 2016-11-21 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Deep Learning, General Classification, Object Recognition

Deep learning, as a promising new area of machine learning, has attracted a
rapidly increasing attention in the field of medical imaging. Compared to the
conventional machine learning methods, deep learning requires no hand-tuned
feature extractor, and has shown a superior performance in many visual object
recognition applications. In this study, we develop a deep convolutional neural
network (CNN) and apply it to thoracic CT images for the classification of lung
nodules. We present the CNN architecture and classification accuracy for the
original images of lung nodules. In order to understand the features of lung
nodules, we further construct new datasets, based on the combination of
artificial geometric nodules and some transformations of the original images,
as well as a stochastic nodule shape model. It is found that simplistic
geometric nodules cannot capture the important features of lung nodules.
Representation Learning with Deconvolution for Multivariate Time Series Classification and Visualization
Authors: Zhiguang Wang, Wei Song, Lu Liu, Fan Zhang, Junxiao Xue, Yangdong Ye, Ming Fan, Mingliang Xu
Date: 2016-10-24 00:00:00
Tasks: Dimensionality Reduction, General Classification, Representation Learning, Time Series, Time Series Analysis, Time Series Classification

We propose a new model based on the deconvolutional networks and SAX
discretization to learn the representation for multivariate time series.
Deconvolutional networks fully exploit the advantage the powerful
expressiveness of deep neural networks in the manner of unsupervised learning.
We design a network structure specifically to capture the cross-channel
correlation with deconvolution, forcing the pooling operation to perform the
dimension reduction along each position in the individual channel.
Discretization based on Symbolic Aggregate Approximation is applied on the
feature vectors to further extract the bag of features. We show how this
representation and bag of features helps on classification. A full comparison
with the sequence distance based approach is provided to demonstrate the
effectiveness of our approach on the standard datasets. We further build the
Markov matrix from the discretized representation from the deconvolution to
visualize the time series as complex networks, which show more class-specific
statistical properties and clear structures with respect to different labels.
Structural Correspondence Learning for Cross-lingual Sentiment Classification with One-to-many Mappings
Authors: Nana Li, Shuangfei Zhai, Zhongfei Zhang, Boying Liu
Date: 2016-11-26 00:00:00
Tasks: Cross-Lingual Sentiment Classification, General Classification, Sentiment Analysis, Sentiment Classification, Translation, Word Translation

Structural correspondence learning (SCL) is an effective method for
cross-lingual sentiment classification. This approach uses unlabeled documents
along with a word translation oracle to automatically induce task specific,
cross-lingual correspondences. It transfers knowledge through identifying
important features, i.e., pivot features. For simplicity, however, it assumes
that the word translation oracle maps each pivot feature in source language to
exactly only one word in target language. This one-to-one mapping between words
in different languages is too strict. Also the context is not considered at
all. In this paper, we propose a cross-lingual SCL based on distributed
representation of words; it can learn meaningful one-to-many mappings for pivot
words using large amounts of monolingual data and a small dictionary. We
conduct experiments on NLP\&CC 2013 cross-lingual sentiment analysis dataset,
employing English as source language, and Chinese as target language. Our
method does not rely on the parallel corpora and the experimental results show
that our approach is more competitive than the state-of-the-art methods in
cross-lingual sentiment classification.
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks
Authors: Nicolas Usunier, Gabriel Synnaeve, Zeming Lin, Soumith Chintala
Date: 2016-09-10 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft

We consider scenarios from the real-time strategy game StarCraft as new
benchmarks for reinforcement learning algorithms. We propose micromanagement
tasks, which present the problem of the short-term, low-level control of army
members during a battle. From a reinforcement learning point of view, these
scenarios are challenging because the state-action space is very large, and
because there is no obvious feature representation for the state-action
evaluation function. We describe our approach to tackle the micromanagement
scenarios with deep neural network controllers from raw state features given by
the game engine. In addition, we present a heuristic reinforcement learning
algorithm which combines direct exploration in the policy space and
backpropagation. This algorithm allows for the collection of traces for
learning using deterministic policies, which appears much more efficient than,
for example, {\epsilon}-greedy exploration. Experiments show that with this
algorithm, we successfully learn non-trivial strategies for scenarios with
armies of up to 15 agents, where both Q-learning and REINFORCE struggle.
BliStrTune: Hierarchical Invention of Theorem Proving Strategies
Authors: Jan Jakubuv, Josef Urban
Date: 2016-11-26 00:00:00
Tasks: Automated Theorem Proving

Inventing targeted proof search strategies for specific problem sets is a
difficult task. State-of-the-art automated theorem provers (ATPs) such as E
allow a large number of user-specified proof search strategies described in a
rich domain specific language. Several machine learning methods that invent
strategies automatically for ATPs were proposed previously. One of them is the
Blind Strategymaker (BliStr), a system for automated invention of ATP
strategies.
  In this paper we introduce BliStrTune -- a hierarchical extension of BliStr.
BliStrTune allows exploring much larger space of E strategies by interleaving
search for high-level parameters with their fine-tuning. We use BliStrTune to
invent new strategies based also on new clause weight functions targeted at
problems from large ITP libraries. We show that the new strategies
significantly improve E's performance in solving problems from the Mizar
Mathematical Library.
Structured adaptive and random spinners for fast machine learning computations
Authors: Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski, Francois Fagan, Cedric Gouy-Pailler, Anne Morvan, Nourhan Sakr, Tamas Sarlos, Jamal Atif
Date: 2016-10-19 00:00:00
Tasks: BIG-bench Machine Learning, Dimensionality Reduction, Quantization

We consider an efficient computational framework for speeding up several
machine learning algorithms with almost no loss of accuracy. The proposed
framework relies on projections via structured matrices that we call Structured
Spinners, which are formed as products of three structured matrix-blocks that
incorporate rotations. The approach is highly generic, i.e. i) structured
matrices under consideration can either be fully-randomized or learned, ii) our
structured family contains as special cases all previously considered
structured schemes, iii) the setting extends to the non-linear case where the
projections are followed by non-linear functions, and iv) the method finds
numerous applications including kernel approximations via random feature maps,
dimensionality reduction algorithms, new fast cross-polytope LSH techniques,
deep learning, convex optimization algorithms via Newton sketches, quantization
with random projection trees, and more. The proposed framework comes with
theoretical guarantees characterizing the capacity of the structured model in
reference to its unstructured counterpart and is based on a general theoretical
principle that we describe in the paper. As a consequence of our theoretical
analysis, we provide the first theoretical guarantees for one of the most
efficient existing LSH algorithms based on the HD3HD2HD1 structured matrix
[Andoni et al., 2015]. The exhaustive experimental evaluation confirms the
accuracy and efficiency of structured spinners for a variety of different
applications.
Machine Learning on Human Connectome Data from MRI
Authors: Colin J Brown, Ghassan Hamarneh
Date: 2016-11-26 00:00:00
Tasks: BIG-bench Machine Learning, Diffusion  MRI, feature selection

Functional MRI (fMRI) and diffusion MRI (dMRI) are non-invasive imaging
modalities that allow in-vivo analysis of a patient's brain network (known as a
connectome). Use of these technologies has enabled faster and better diagnoses
and treatments of neurological disorders and a deeper understanding of the
human brain. Recently, researchers have been exploring the application of
machine learning models to connectome data in order to predict clinical
outcomes and analyze the importance of subnetworks in the brain. Connectome
data has unique properties, which present both special challenges and
opportunities when used for machine learning. The purpose of this work is to
review the literature on the topic of applying machine learning models to
MRI-based connectome data. This field is growing rapidly and now encompasses a
large body of research. To summarize the research done to date, we provide a
comparative, structured summary of 77 relevant works, tabulated according to
different criteria, that represent the majority of the literature on this
topic. (We also published a living version of this table online at
http://connectomelearning.cs.sfu.ca that the community can continue to
contribute to.) After giving an overview of how connectomes are constructed
from dMRI and fMRI data, we discuss the variety of machine learning tasks that
have been explored with connectome data. We then compare the advantages and
drawbacks of different machine learning approaches that have been employed,
discussing different feature selection and feature extraction schemes, as well
as the learning models and regularization penalties themselves. Throughout this
discussion, we focus particularly on how the methods are adapted to the unique
nature of graphical connectome data. Finally, we conclude by summarizing the
current state of the art and by outlining what we believe are strategic
directions for future research.
Understanding data augmentation for classification: when to warp?
Authors: Sebastien C. Wong, Adam Gatt, Victor Stamatescu, Mark D. McDonnell
Date: 2016-09-28 00:00:00
Tasks: Classification, Data Augmentation, General Classification

In this paper we investigate the benefit of augmenting data with
synthetically created samples when training a machine learning classifier. Two
approaches for creating additional training samples are data warping, which
generates additional samples through transformations applied in the data-space,
and synthetic over-sampling, which creates additional samples in feature-space.
We experimentally evaluate the benefits of data augmentation for a
convolutional backpropagation-trained neural network, a convolutional support
vector machine and a convolutional extreme learning machine classifier, using
the standard MNIST handwritten digit dataset. We found that while it is
possible to perform generic augmentation in feature-space, if plausible
transforms for the data are known then augmentation in data-space provides a
greater benefit for improving performance and reducing overfitting.
Multiwinner Approval Rules as Apportionment Methods
Authors: Markus Brill, Jean-François Laslier, Piotr Skowron
Date: 2016-11-26 00:00:00

We establish a link between multiwinner elections and apportionment problems by showing how approval-based multiwinner election rules can be interpreted as methods of apportionment. We consider several multiwinner rules and observe that they induce apportionment methods that are well-established in the literature on proportional representation. For instance, we show that Proportional Approval Voting induces the D'Hondt method and that Monroe's rule induces the largest reminder method. We also consider properties of apportionment methods and exhibit multiwinner rules that induce apportionment methods satisfying these properties.
A General Retraining Framework for Scalable Adversarial Classification
Authors: Bo Li, Yevgeniy Vorobeychik, Xinyun Chen
Date: 2016-04-09 00:00:00
Tasks: Classification, General Classification

Traditional classification algorithms assume that training and test data come
from similar distributions. This assumption is violated in adversarial
settings, where malicious actors modify instances to evade detection. A number
of custom methods have been developed for both adversarial evasion attacks and
robust learning. We propose the first systematic and general-purpose retraining
framework which can: a) boost robustness of an \emph{arbitrary} learning
algorithm, in the face of b) a broader class of adversarial models than any
prior methods. We show that, under natural conditions, the retraining framework
minimizes an upper bound on optimal adversarial risk, and show how to extend
this result to account for approximations of evasion attacks. Extensive
experimental evaluation demonstrates that our retraining methods are nearly
indistinguishable from state-of-the-art algorithms for optimizing adversarial
risk, but are more general and far more scalable. The experiments also confirm
that without retraining, our adversarial framework dramatically reduces the
effectiveness of learning. In contrast, retraining significantly boosts
robustness to evasion attacks without significantly compromising overall
accuracy.
Sparse Proteomics Analysis - A compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data
Authors: Tim Conrad, Martin Genzel, Nada Cvetkovic, Niklas Wulkow, Alexander Leichtle, Jan Vybiral, Gitta Kutyniok, Christof Schütte
Date: 2015-06-11 00:00:00
Tasks: compressed sensing, feature selection, General Classification, Single Particle Analysis

Background: High-throughput proteomics techniques, such as mass spectrometry
(MS)-based approaches, produce very high-dimensional data-sets. In a clinical
setting one is often interested in how mass spectra differ between patients of
different classes, for example spectra from healthy patients vs. spectra from
patients having a particular disease. Machine learning algorithms are needed to
(a) identify these discriminating features and (b) classify unknown spectra
based on this feature set. Since the acquired data is usually noisy, the
algorithms should be robust against noise and outliers, while the identified
feature set should be as small as possible.
  Results: We present a new algorithm, Sparse Proteomics Analysis (SPA), based
on the theory of compressed sensing that allows us to identify a minimal
discriminating set of features from mass spectrometry data-sets. We show (1)
how our method performs on artificial and real-world data-sets, (2) that its
performance is competitive with standard (and widely used) algorithms for
analyzing proteomics data, and (3) that it is robust against random and
systematic noise. We further demonstrate the applicability of our algorithm to
two previously published clinical data-sets.
Deep Reinforcement Learning for Multi-Domain Dialogue Systems
Authors: Heriberto Cuayáhuitl, Seunghak Yu, Ashley Williamson, Jacob Carse
Date: 2016-11-26 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Standard deep reinforcement learning methods such as Deep Q-Networks (DQN)
for multiple tasks (domains) face scalability problems. We propose a method for
multi-domain dialogue policy learning---termed NDQN, and apply it to an
information-seeking spoken dialogue system in the domains of restaurants and
hotels. Experimental results comparing DQN (baseline) versus NDQN (proposed)
using simulations report that our proposed method exhibits better scalability
and is promising for optimising the behaviour of multi-domain dialogue systems.
Training an Interactive Humanoid Robot Using Multimodal Deep Reinforcement Learning
Authors: Heriberto Cuayáhuitl, Guillaume Couly, Clément Olalainty
Date: 2016-11-26 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Training robots to perceive, act and communicate using multiple modalities
still represents a challenging problem, particularly if robots are expected to
learn efficiently from small sets of example interactions. We describe a
learning approach as a step in this direction, where we teach a humanoid robot
how to play the game of noughts and crosses. Given that multiple multimodal
skills can be trained to play this game, we focus our attention to training the
robot to perceive the game, and to interact in this game. Our multimodal deep
reinforcement learning agent perceives multimodal features and exhibits verbal
and non-verbal actions while playing. Experimental results using simulations
show that the robot can learn to win or draw up to 98% of the games. A pilot
test of the proposed multimodal system for the targeted game---integrating
speech, vision and gestures---reports that reasonable and fluent interactions
can be achieved using the proposed approach.
Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation
Authors: Xun Xu, Timothy M. Hospedales, Shaogang Gong
Date: 2016-11-26 00:00:00
Tasks: Action Recognition, Data Augmentation, Temporal Action Localization, Zero-Shot Action Recognition, Zero-Shot Learning

Zero-Shot Learning (ZSL) promises to scale visual recognition by bypassing
the conventional model training requirement of annotated examples for every
category. This is achieved by establishing a mapping connecting low-level
features and a semantic description of the label space, referred as
visual-semantic mapping, on auxiliary data. Reusing the learned mapping to
project target videos into an embedding space thus allows novel-classes to be
recognised by nearest neighbour inference. However, existing ZSL methods suffer
from auxiliary-target domain shift intrinsically induced by assuming the same
mapping for the disjoint auxiliary and target classes. This compromises the
generalisation accuracy of ZSL recognition on the target data. In this work, we
improve the ability of ZSL to generalise across this domain shift in both
model- and data-centric ways by formulating a visual-semantic mapping with
better generalisation properties and a dynamic data re-weighting method to
prioritise auxiliary data that are relevant to the target classes.
Specifically: (1) We introduce a multi-task visual-semantic mapping to improve
generalisation by constraining the semantic mapping parameters to lie on a
low-dimensional manifold, (2) We explore prioritised data augmentation by
expanding the pool of auxiliary data with additional instances weighted by
relevance to the target domain. The proposed new model is applied to the
challenging zero-shot action recognition problem to demonstrate its advantages
over existing ZSL models.