pwc-archive/papers-with-abstracts huggingface dataset:
Fine-grained Recognition in the Wild: A Multi-Task Domain Adaptation Approach
Authors: Timnit Gebru, Judy Hoffman, Li Fei-Fei
Date: 2017-09-07 00:00:00
Proceeding: ICCV 2017 10
Tasks: Attribute, Domain Adaptation, Object Recognition

While fine-grained object recognition is an important problem in computer
vision, current models are unlikely to accurately classify objects in the wild.
These fully supervised models need additional annotated images to classify
objects in every new scenario, a task that is infeasible. However, sources such
as e-commerce websites and field guides provide annotated images for many
classes. In this work, we study fine-grained domain adaptation as a step
towards overcoming the dataset shift between easily acquired annotated images
and the real world. Adaptation has not been studied in the fine-grained setting
where annotations such as attributes could be used to increase performance. Our
work uses an attribute based multi-task adaptation loss to increase accuracy
from a baseline of 4.1% to 19.1% in the semi-supervised adaptation case. Prior
do- main adaptation works have been benchmarked on small datasets such as [46]
with a total of 795 images for some domains, or simplistic datasets such as
[41] consisting of digits. We perform experiments on a subset of a new
challenging fine-grained dataset consisting of 1,095,021 images of 2, 657 car
categories drawn from e-commerce web- sites and Google Street View.
Local Neighborhood Intensity Pattern: A new texture feature descriptor for image retrieval
Authors: Prithaj Banerjee, Ayan Kumar Bhunia, Avirup Bhattacharyya, Partha Pratim Roy, Subrahmanyam Murala
Date: 2017-09-07 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

In this paper, a new texture descriptor based on the local neighborhood
intensity difference is proposed for content based image retrieval (CBIR). For
computation of texture features like Local Binary Pattern (LBP), the center
pixel in a 3*3 window of an image is compared with all the remaining neighbors,
one pixel at a time to generate a binary bit pattern. It ignores the effect of
the adjacent neighbors of a particular pixel for its binary encoding and also
for texture description. The proposed method is based on the concept that
neighbors of a particular pixel hold a significant amount of texture
information that can be considered for efficient texture representation for
CBIR. Taking this into account, we develop a new texture descriptor, named as
Local Neighborhood Intensity Pattern (LNIP) which considers the relative
intensity difference between a particular pixel and the center pixel by
considering its adjacent neighbors and generate a sign and a magnitude pattern.
Since sign and magnitude patterns hold complementary information to each other,
these two patterns are concatenated into a single feature descriptor to
generate a more concrete and useful feature descriptor. The proposed descriptor
has been tested for image retrieval on four databases, including three texture
image databases - Brodatz texture image database, MIT VisTex database and
Salzburg texture database and one face database AT&T face database. The
precision and recall values observed on these databases are compared with some
state-of-art local patterns. The proposed method showed a significant
improvement over many other existing methods.
Balancing Lexicographic Fairness and a Utilitarian Objective with Application to Kidney Exchange
Authors: Duncan C. McElfresh, John P. Dickerson
Date: 2017-02-27 00:00:00
Tasks: Fairness

Balancing fairness and efficiency in resource allocation is a classical
economic and computational problem. The price of fairness measures the
worst-case loss of economic efficiency when using an inefficient but fair
allocation rule; for indivisible goods in many settings, this price is
unacceptably high. One such setting is kidney exchange, where needy patients
swap willing but incompatible kidney donors. In this work, we close an open
problem regarding the theoretical price of fairness in modern kidney exchanges.
We then propose a general hybrid fairness rule that balances a strict
lexicographic preference ordering over classes of agents, and a utilitarian
objective that maximizes economic efficiency. We develop a utility function for
this rule that favors disadvantaged groups lexicographically; but if cost to
overall efficiency becomes too high, it switches to a utilitarian objective.
This rule has only one parameter which is proportional to a bound on the price
of fairness, and can be adjusted by policymakers. We apply this rule to real
data from a large kidney exchange and show that our hybrid rule produces more
reliable outcomes than other fairness rules.
End-to-end Face Detection and Cast Grouping in Movies Using Erdős-Rényi Clustering
Authors: SouYoung Jin, Hang Su, Chris Stauffer, Erik Learned-Miller
Date: 2017-09-07 00:00:00
Tasks: Clustering, Face Detection

We present an end-to-end system for detecting and clustering faces by
identity in full-length movies. Unlike works that start with a predefined set
of detected faces, we consider the end-to-end problem of detection and
clustering together. We make three separate contributions. First, we combine a
state-of-the-art face detector with a generic tracker to extract high quality
face tracklets. We then introduce a novel clustering method, motivated by the
classic graph theory results of Erd\H{o}s and R\'enyi. It is based on the
observations that large clusters can be fully connected by joining just a small
fraction of their point pairs, while just a single connection between two
different people can lead to poor clustering results. This suggests clustering
using a verification system with very few false positives but perhaps moderate
recall. We introduce a novel verification method, rank-1 counts verification,
that has this property, and use it in a link-based clustering scheme. Finally,
we define a novel end-to-end detection and clustering evaluation metric
allowing us to assess the accuracy of the entire end-to-end system. We present
state-of-the-art results on multiple video data sets and also on standard face
databases.
Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams
Authors: Ali Pesaranghader, Herna Viktor, Eric Paquet
Date: 2017-09-07 00:00:00
Tasks: Computer Security, Drift Detection

The last decade has seen a surge of interest in adaptive learning algorithms
for data stream classification, with applications ranging from predicting ozone
level peaks, learning stock market indicators, to detecting computer security
violations. In addition, a number of methods have been developed to detect
concept drifts in these streams. Consider a scenario where we have a number of
classifiers with diverse learning styles and different drift detectors.
Intuitively, the current 'best' (classifier, detector) pair is application
dependent and may change as a result of the stream evolution. Our research
builds on this observation. We introduce the $\mbox{Tornado}$ framework that
implements a reservoir of diverse classifiers, together with a variety of drift
detection algorithms. In our framework, all (classifier, detector) pairs
proceed, in parallel, to construct models against the evolving data streams. At
any point in time, we select the pair which currently yields the best
performance. We further incorporate two novel stacking-based drift detection
methods, namely the $\mbox{FHDDMS}$ and $\mbox{FHDDMS}_{add}$ approaches. The
experimental evaluation confirms that the current 'best' (classifier, detector)
pair is not only heavily dependent on the characteristics of the stream, but
also that this selection evolves as the stream flows. Further, our
$\mbox{FHDDMS}$ variants detect concept drifts accurately in a timely fashion
while outperforming the state-of-the-art.
Network Vector: Distributed Representations of Networks with Global Context
Authors: Hao Wu, Kristina Lerman
Date: 2017-09-07 00:00:00
Tasks: General Classification, Node Classification

We propose a neural embedding algorithm called Network Vector, which learns
distributed representations of nodes and the entire networks simultaneously. By
embedding networks in a low-dimensional space, the algorithm allows us to
compare networks in terms of structural similarity and to solve outstanding
predictive problems. Unlike alternative approaches that focus on node level
features, we learn a continuous global vector that captures each node's global
context by maximizing the predictive likelihood of random walk paths in the
network. Our algorithm is scalable to real world graphs with many nodes. We
evaluate our algorithm on datasets from diverse domains, and compare it with
state-of-the-art techniques in node classification, role discovery and concept
analogy tasks. The empirical results show the effectiveness and the efficiency
of our algorithm.
An Analysis of ISO 26262: Using Machine Learning Safely in Automotive Software
Authors: Rick Salay, Rodrigo Queiroz, Krzysztof Czarnecki
Date: 2017-09-07 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning (ML) plays an ever-increasing role in advanced automotive
functionality for driver assistance and autonomous operation; however, its
adequacy from the perspective of safety certification remains controversial. In
this paper, we analyze the impacts that the use of ML as an implementation
approach has on ISO 26262 safety lifecycle and ask what could be done to
address them. We then provide a set of recommendations on how to adapt the
standard to accommodate ML.
Deep Learning the Physics of Transport Phenomena
Authors: Amir Barati Farimani, Joseph Gomes, Vijay S. Pande
Date: 2017-09-07 00:00:00
Tasks: Deep Learning

We have developed a new data-driven paradigm for the rapid inference,
modeling and simulation of the physics of transport phenomena by deep learning.
Using conditional generative adversarial networks (cGAN), we train models for
the direct generation of solutions to steady state heat conduction and
incompressible fluid flow purely on observation without knowledge of the
underlying governing equations. Rather than using iterative numerical methods
to approximate the solution of the constitutive equations, cGANs learn to
directly generate the solutions to these phenomena, given arbitrary boundary
conditions and domain, with high test accuracy (MAE$<$1\%) and state-of-the-art
computational performance. The cGAN framework can be used to learn causal
models directly from experimental observations where the underlying physical
model is complex or unknown.
Learning the Enigma with Recurrent Neural Networks
Authors: Sam Greydanus
Date: 2017-08-24 00:00:00
Tasks: Cryptanalysis, speech-recognition, Translation

Recurrent neural networks (RNNs) represent the state of the art in
translation, image captioning, and speech recognition. They are also capable of
learning algorithmic tasks such as long addition, copying, and sorting from a
set of training examples. We demonstrate that RNNs can learn decryption
algorithms -- the mappings from plaintext to ciphertext -- for three
polyalphabetic ciphers (Vigen\`ere, Autokey, and Enigma). Most notably, we
demonstrate that an RNN with a 3000-unit Long Short-Term Memory (LSTM) cell can
learn the decryption function of the Enigma machine. We argue that our model
learns efficient internal representations of these ciphers 1) by exploring
activations of individual memory neurons and 2) by comparing memory usage
across the three ciphers. To be clear, our work is not aimed at 'cracking' the
Enigma cipher. However, we do show that our model can perform elementary
cryptanalysis by running known-plaintext attacks on the Vigen\`ere and Autokey
ciphers. Our results indicate that RNNs can learn algorithmic representations
of black box polyalphabetic ciphers and that these representations are useful
for cryptanalysis.
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
Authors: Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh
Date: 2015-12-31 00:00:00
Tasks: Variational Inference

This paper makes two contributions to Bayesian machine learning algorithms.
Firstly, we propose stochastic natural gradient expectation propagation (SNEP),
a novel alternative to expectation propagation (EP), a popular variational
inference algorithm. SNEP is a black box variational algorithm, in that it does
not require any simplifying assumptions on the distribution of interest, beyond
the existence of some Monte Carlo sampler for estimating the moments of the EP
tilted distributions. Further, as opposed to EP which has no guarantee of
convergence, SNEP can be shown to be convergent, even when using Monte Carlo
moment estimates. Secondly, we propose a novel architecture for distributed
Bayesian learning which we call the posterior server. The posterior server
allows scalable and robust Bayesian learning in cases where a data set is
stored in a distributed manner across a cluster, with each compute node
containing a disjoint subset of data. An independent Monte Carlo sampler is run
on each compute node, with direct access only to the local data subset, but
which targets an approximation to the global posterior distribution given all
data across the whole cluster. This is achieved by using a distributed
asynchronous implementation of SNEP to pass messages across the cluster. We
demonstrate SNEP and the posterior server on distributed Bayesian learning of
logistic regression and neural networks.
  Keywords: Distributed Learning, Large Scale Learning, Deep Learning, Bayesian
Learn- ing, Variational Inference, Expectation Propagation, Stochastic
Approximation, Natural Gradient, Markov chain Monte Carlo, Parameter Server,
Posterior Server.
Argument Labeling of Explicit Discourse Relations using LSTM Neural Networks
Authors: Sohail Hooda, Leila Kosseim
Date: 2017-08-11 00:00:00
Proceeding: RANLP 2017 9
Tasks: Feature Engineering

Argument labeling of explicit discourse relations is a challenging task. The
state of the art systems achieve slightly above 55% F-measure but require
hand-crafted features. In this paper, we propose a Long Short Term Memory
(LSTM) based model for argument labeling. We experimented with multiple
configurations of our model. Using the PDTB dataset, our best model achieved an
F1 measure of 23.05% without any feature engineering. This is significantly
higher than the 20.52% achieved by the state of the art RNN approach, but
significantly lower than the feature based state of the art systems. On the
other hand, because our approach learns only from the raw dataset, it is more
widely applicable to multiple textual genres and languages.
Learning from lions: inferring the utility of agents from their trajectories
Authors: Adam D. Cobb, Andrew Markham, Stephen J. Roberts
Date: 2017-09-07 00:00:00
Tasks: Decision Making, Gaussian Processes

We build a model using Gaussian processes to infer a spatio-temporal vector
field from observed agent trajectories. Significant landmarks or influence
points in agent surroundings are jointly derived through vector calculus
operations that indicate presence of sources and sinks. We evaluate these
influence points by using the Kullback-Leibler divergence between the posterior
and prior Laplacian of the inferred spatio-temporal vector field. Through
locating significant features that influence trajectories, our model aims to
give greater insight into underlying causal utility functions that determine
agent decision-making. A key feature of our model is that it infers a joint
Gaussian process over the observed trajectories, the time-varying vector field
of utility and canonical vector calculus operators. We apply our model to both
synthetic data and lion GPS data collected at the Bubye Valley Conservancy in
southern Zimbabwe.
A Video-Based Method for Objectively Rating Ataxia
Authors: Ronnachai Jaroensri, Amy Zhao, Guha Balakrishnan, Derek Lo, Jeremy Schmahmann, John Guttag, Fredo Durand
Date: 2016-12-13 00:00:00
Tasks: Optical Flow Estimation, Pose Estimation

For many movement disorders, such as Parkinson's disease and ataxia, disease
progression is visually assessed by a clinician using a numerical disease
rating scale. These tests are subjective, time-consuming, and must be
administered by a professional. This can be problematic where specialists are
not available, or when a patient is not consistently evaluated by the same
clinician. We present an automated method for quantifying the severity of
motion impairment in patients with ataxia, using only video recordings. We
consider videos of the finger-to-nose test, a common movement task used as part
of the assessment of ataxia progression during the course of routine clinical
checkups.
  Our method uses neural network-based pose estimation and optical flow
techniques to track the motion of the patient's hand in a video recording. We
extract features that describe qualities of the motion such as speed and
variation in performance. Using labels provided by an expert clinician, we
train a supervised learning model that predicts severity according to the Brief
Ataxia Rating Scale (BARS). The performance of our system is comparable to that
of a group of ataxia specialists in terms of mean error and correlation, and
our system's predictions were consistently within the range of inter-rater
variability. This work demonstrates the feasibility of using computer vision
and machine learning to produce consistent and clinically useful measures of
motor impairment.
How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
Authors: Adrian Bulat, Georgios Tzimiropoulos
Date: 2017-03-21 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Face Alignment, Face Alignment, Head Pose Estimation

This paper investigates how far a very deep neural network is from attaining
close to saturating performance on existing 2D and 3D face alignment datasets.
To this end, we make the following 5 contributions: (a) we construct, for the
first time, a very strong baseline by combining a state-of-the-art architecture
for landmark localization with a state-of-the-art residual block, train it on a
very large yet synthetically expanded 2D facial landmark dataset and finally
evaluate it on all other 2D facial landmark datasets. (b) We create a guided by
2D landmarks network which converts 2D landmark annotations to 3D and unifies
all existing datasets, leading to the creation of LS3D-W, the largest and most
challenging 3D facial landmark dataset to date ~230,000 images. (c) Following
that, we train a neural network for 3D face alignment and evaluate it on the
newly introduced LS3D-W. (d) We further look into the effect of all
"traditional" factors affecting face alignment performance like large pose,
initialization and resolution, and introduce a "new" one, namely the size of
the network. (e) We show that both 2D and 3D face alignment networks achieve
performance of remarkable accuracy which is probably close to saturating the
datasets used. Training and testing code as well as the dataset can be
downloaded from https://www.adrianbulat.com/face-alignment/
Feature selection in high-dimensional dataset using MapReduce
Authors: Claudio Reggiani, Yann-Aël Le Borgne, Gianluca Bontempi
Date: 2017-09-07 00:00:00
Tasks: feature selection, Vocal Bursts Intensity Prediction

This paper describes a distributed MapReduce implementation of the minimum
Redundancy Maximum Relevance algorithm, a popular feature selection method in
bioinformatics and network inference problems. The proposed approach handles
both tall/narrow and wide/short datasets. We further provide an open source
implementation based on Hadoop/Spark, and illustrate its scalability on
datasets involving millions of observations or features.
Monocular Navigation in Large Scale Dynamic Environments
Authors: Darius Burschka
Date: 2017-09-07 00:00:00
Tasks: Sensitivity

We present a processing technique for a robust reconstruction of motion
properties for single points in large scale, dynamic environments. We assume
that the acquisition camera is moving and that there are other independently
moving agents in a large environment, like road scenarios. The separation of
direction and magnitude of the reconstructed motion allows for robust
reconstruction of the dynamic state of the objects in situations, where
conventional binocular systems fail due to a small signal (disparity) from the
images due to a constant detection error, and where structure from motion
approaches fail due to unobserved motion of other agents between the camera
frames.
  We present the mathematical framework and the sensitivity analysis for the
resulting system.
Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis
Authors: Pooyan Jamshidi, Norbert Siegmund, Miguel Velez, Christian Kästner, Akshay Patel, Yuvraj Agarwal
Date: 2017-09-07 00:00:00
Tasks: Transfer Learning

Modern software systems provide many configuration options which
significantly influence their non-functional properties. To understand and
predict the effect of configuration options, several sampling and learning
strategies have been proposed, albeit often with significant cost to cover the
highly dimensional configuration space. Recently, transfer learning has been
applied to reduce the effort of constructing performance models by transferring
knowledge about performance behavior across environments. While this line of
research is promising to learn more accurate models at a lower cost, it is
unclear why and when transfer learning works for performance modeling. To shed
light on when it is beneficial to apply transfer learning, we conducted an
empirical study on four popular software systems, varying software
configurations and environmental conditions, such as hardware, workload, and
software versions, to identify the key knowledge pieces that can be exploited
for transfer learning. Our results show that in small environmental changes
(e.g., homogeneous workload change), by applying a linear transformation to the
performance model, we can understand the performance behavior of the target
environment, while for severe environmental changes (e.g., drastic workload
change) we can transfer only knowledge that makes sampling more efficient,
e.g., by reducing the dimensionality of the configuration space.
Cynical Selection of Language Model Training Data
Authors: Amittai Axelrod
Date: 2017-09-07 00:00:00
Tasks: Language Modeling, Language Modelling, model

The Moore-Lewis method of "intelligent selection of language model training
data" is very effective, cheap, efficient... and also has structural problems.
(1) The method defines relevance by playing language models trained on the
in-domain and the out-of-domain (or data pool) corpora against each other. This
powerful idea-- which we set out to preserve-- treats the two corpora as the
opposing ends of a single spectrum. This lack of nuance does not allow for the
two corpora to be very similar. In the extreme case where the come from the
same distribution, all of the sentences have a Moore-Lewis score of zero, so
there is no resulting ranking. (2) The selected sentences are not guaranteed to
be able to model the in-domain data, nor to even cover the in-domain data. They
are simply well-liked by the in-domain model; this is necessary, but not
sufficient. (3) There is no way to tell what is the optimal number of sentences
to select, short of picking various thresholds and building the systems.
  We present a greedy, lazy, approximate, and generally efficient
information-theoretic method of accomplishing the same goal using only
vocabulary counts. The method has the following properties: (1) Is responsive
to the extent to which two corpora differ. (2) Quickly reaches near-optimal
vocabulary coverage. (3) Takes into account what has already been selected. (4)
Does not involve defining any kind of domain, nor any kind of classifier. (6)
Knows approximately when to stop. This method can be used as an
inherently-meaningful measure of similarity, as it measures the bits of
information to be gained by adding one text to another.