pwc-archive/papers-with-abstracts huggingface dataset:
An Interactive Machine Learning Framework
Authors: Teng Lee, James Johnson, Steve Cheng
Date: 2016-10-18 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Machine learning (ML) is believed to be an effective and efficient tool to
build reliable prediction model or extract useful structure from an avalanche
of data. However, ML is also criticized by its difficulty in interpretation and
complicated parameter tuning. In contrast, visualization is able to well
organize and visually encode the entangled information in data and guild
audiences to simpler perceptual inferences and analytic thinking. But large
scale and high dimensional data will usually lead to the failure of many
visualization methods. In this paper, we close a loop between ML and
visualization via interaction between ML algorithm and users, so machine
intelligence and human intelligence can cooperate and improve each other in a
mutually rewarding way. In particular, we propose "transparent boosting tree
(TBT)", which visualizes both the model structure and prediction statistics of
each step in the learning process of gradient boosting tree to user, and
involves user's feedback operations to trees into the learning process. In TBT,
ML is in charge of updating weights in learning model and filtering information
shown to user from the big data, while visualization is in charge of providing
a visual understanding of ML model to facilitate user exploration. It combines
the advantages of both ML in big data statistics and human in decision making
based on domain knowledge. We develop a user friendly interface for this novel
learning method, and apply it to two datasets collected from real applications.
Our study shows that making ML transparent by using interactive visualization
can significantly improve the exploration of ML algorithms, give rise to novel
insights of ML models, and integrates both machine and human intelligence.
Makespan Optimal Solving of Cooperative Path-Finding via Reductions to Propositional Satisfiability
Authors: Pavel Surynek
Date: 2016-10-18 00:00:00

The problem of makespan optimal solving of cooperative path finding (CPF) is
addressed in this paper. The task in CPF is to relocate a group of agents in a
non-colliding way so that each agent eventually reaches its goal location from
the given initial location. The abstraction adopted in this work assumes that
agents are discrete items moving in an undirected graph by traversing edges.
Makespan optimal solving of CPF means to generate solutions that are as short
as possi-ble in terms of the total number of time steps required for the
execution of the solution.
  We show that reducing CPF to propositional satisfiability (SAT) represents a
viable option for obtaining makespan optimal solutions. Several encodings of
CPF into propositional formulae are suggested and experimentally evaluated. The
evaluation indicates that SAT based CPF solving outperforms other makespan
optimal methods significantly in highly constrained situations (environments
that are densely occupied by agents).
Automatic Separation of Compound Figures in Scientific Articles
Authors: Mario Taschwer, Oge Marques
Date: 2016-06-03 00:00:00
Tasks: Articles, Retrieval

Content-based analysis and retrieval of digital images found in scientific
articles is often hindered by images consisting of multiple subfigures
(compound figures). We address this problem by proposing a method to
automatically classify and separate compound figures, which consists of two
main steps: (i) a supervised compound figure classifier (CFC) discriminates
between compound and non-compound figures using task-specific image features;
and (ii) an image processing algorithm is applied to predicted compound images
to perform compound figure separation (CFS). Our CFC approach is shown to
achieve state-of-the-art classification performance on a published dataset. Our
CFS algorithm shows superior separation accuracy on two different datasets
compared to other known automatic approaches. Finally, we propose a method to
evaluate the effectiveness of the CFC-CFS process chain and use it to optimize
the misclassification loss of CFC for maximal effectiveness in the process
chain.
Generalization error minimization: a new approach to model evaluation and selection with an application to penalized regression
Authors: Ning Xu, Jian Hong, Timothy C. G. Fisher
Date: 2016-10-18 00:00:00
Tasks: Model Selection, regression

We study model evaluation and model selection from the perspective of
generalization ability (GA): the ability of a model to predict outcomes in new
samples from the same population. We believe that GA is one way formally to
address concerns about the external validity of a model. The GA of a model
estimated on a sample can be measured by its empirical out-of-sample errors,
called the generalization errors (GE). We derive upper bounds for the GE, which
depend on sample sizes, model complexity and the distribution of the loss
function. The upper bounds can be used to evaluate the GA of a model, ex ante.
We propose using generalization error minimization (GEM) as a framework for
model selection. Using GEM, we are able to unify a big class of penalized
regression estimators, including lasso, ridge and bridge, under the same set of
assumptions. We establish finite-sample and asymptotic properties (including
$\mathcal{L}_2$-consistency) of the GEM estimator for both the $n \geqslant p$
and the $n < p$ cases. We also derive the $\mathcal{L}_2$-distance between the
penalized and corresponding unpenalized regression estimates. In practice, GEM
can be implemented by validation or cross-validation. We show that the GE
bounds can be used for selecting the optimal number of folds in $K$-fold
cross-validation. We propose a variant of $R^2$, the $GR^2$, as a measure of
GA, which considers both both in-sample and out-of-sample goodness of fit.
Simulations are used to demonstrate our key results.
Learning Structured Sparsity in Deep Neural Networks
Authors: Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li
Date: 2016-08-12 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: CPU, GPU

High demand for computation resources severely hinders deployment of
large-scale Deep Neural Networks (DNN) in resource constrained devices. In this
work, we propose a Structured Sparsity Learning (SSL) method to regularize the
structures (i.e., filters, channels, filter shapes, and layer depth) of DNNs.
SSL can: (1) learn a compact structure from a bigger DNN to reduce computation
cost; (2) obtain a hardware-friendly structured sparsity of DNN to efficiently
accelerate the DNNs evaluation. Experimental results show that SSL achieves on
average 5.1x and 3.1x speedups of convolutional layer computation of AlexNet
against CPU and GPU, respectively, with off-the-shelf libraries. These speedups
are about twice speedups of non-structured sparsity; (3) regularize the DNN
structure to improve classification accuracy. The results show that for
CIFAR-10, regularization on layer depth can reduce 20 layers of a Deep Residual
Network (ResNet) to 18 layers while improve the accuracy from 91.25% to 92.60%,
which is still slightly higher than that of original ResNet with 32 layers. For
AlexNet, structure regularization by SSL also reduces the error by around ~1%.
Open source code is in https://github.com/wenwei202/caffe/tree/scnn
Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges
Authors: Ali Khalajmehrabadi, Nikolaos Gatsis, David Akopian
Date: 2016-10-18 00:00:00

Wireless Local Area Network (WLAN) has become a promising choice for indoor
positioning as the only existing and established infrastructure, to localize
the mobile and stationary users indoors. However, since WLAN has been initially
designed for wireless networking and not positioning, the localization task
based on WLAN signals has several challenges. Amongst the WLAN positioning
methods, WLAN fingerprinting localization has recently achieved great attention
due to its promising results. WLAN fingerprinting faces several challenges and
hence, in this paper, our goal is to overview these challenges and the
state-of-the-art solutions. This paper consists of three main parts: 1)
Conventional localization schemes; 2) State-of-the-art approaches; 3) Practical
deployment challenges. Since all the proposed methods in WLAN literature have
been conducted and tested in different settings, the reported results are not
equally comparable. So, we compare some of the main localization schemes in a
single real environment and assess their localization accuracy, positioning
error statistics, and complexity. Our results depict illustrative evaluation of
WLAN localization systems and guide to future improvement opportunities.
Structured Group Sparsity: A Novel Indoor WLAN Localization, Outlier Detection, and Radio Map Interpolation Scheme
Authors: Ali Khalajmehrabadi, Nikolaos Gatsis, David Akopian
Date: 2016-10-18 00:00:00
Tasks: Indoor Localization, Outlier Detection

This paper introduces novel schemes for indoor localization, outlier
detection, and radio map interpolation using Wireless Local Area Networks
(WLANs). The localization method consists of a novel multicomponent
optimization technique that minimizes the squared $\ell_{2}$-norm of the
residuals between the radio map and the online Received Signal Strength (RSS)
measurements, the $\ell_{1}$-norm of the user's location vector, and weighted
$\ell_{2}$-norms of layered groups of Reference Points (RPs). RPs are grouped
using a new criterion based on the similarity between the so-called Access
Point (AP) coverage vectors. In addition, since AP readings are prone to
containing inordinate readings, called outliers, an augmented optimization
problem is proposed to detect the outliers and localize the user with cleaned
online measurements. Moreover, a novel scheme to record fingerprints from a
smaller number of RPs and estimate the radio map at RPs without recorded
fingerprints is developed using sparse recovery techniques. All localization
schemes are tested on RSS fingerprints collected from a real environment. The
overall scheme has comparable complexity with competing approaches, while it
performs with high accuracy under a small number of APs and finer granularity
of RPs.
A Joint Indoor WLAN Localization and Outlier Detection Scheme Using LASSO and Elastic-Net Optimization Techniques
Authors: Ali Khalajmehrabadi, Nikolaos Gatsis, Daniel Pack, David Akopian
Date: 2016-10-18 00:00:00
Tasks: Outlier Detection, Position

In this paper, we introduce two indoor Wireless Local Area Network (WLAN)
positioning methods using augmented sparse recovery algorithms. These schemes
render a sparse user's position vector, and in parallel, minimize the distance
between the online measurement and radio map. The overall localization scheme
for both methods consists of three steps: 1) coarse localization, obtained from
comparing the online measurements with clustered radio map. A novel graph-based
method is proposed to cluster the offline fingerprints. In the online phase, a
Region Of Interest (ROI) is selected within which we search for the user's
location; 2) Access Point (AP) selection; and 3) fine localization through the
novel sparse recovery algorithms. Since the online measurements are subject to
inordinate measurement readings, called outliers, the sparse recovery methods
are modified in order to jointly estimate the outliers and user's position
vector. The outlier detection procedure identifies the APs whose readings are
either not available or erroneous. The proposed localization methods have been
tested with Received Signal Strength (RSS) measurements in a typical office
environment and the results show that they can localize the user with
significantly high accuracy and resolution which is superior to the results
from competing WLAN fingerprinting localization methods.
VRPBench: A Vehicle Routing Benchmark Tool
Authors: Guilherme A. Zeni, Mauro Menzori, P. S. Martins, Luis A. A. Meira
Date: 2016-10-18 00:00:00

The number of optimization techniques in the combinatorial domain is large
and diversified. Nevertheless, there is still a lack of real benchmarks to
validate optimization algorithms. In this work we introduce VRPBench, a tool to
create instances and visualize solutions to the Vehicle Routing Problem (VRP)
in a planar graph embedded in the Euclidean 2D space. We use VRPBench to model
a real-world mail delivery case of the city of Artur Nogueira. Such scenarios
were characterized as a multi-objective optimization of the VRP. We extracted a
weighted graph from a digital map of the city to create a challenging benchmark
for the VRP. Each instance models one generic day of mail delivery with
hundreds to thousands of delivery points, thus allowing both the comparison and
validation of optimization algorithms for routing problems.
Sequential Learning without Feedback
Authors: Manjesh Hanawal, Csaba Szepesvari, Venkatesh Saligrama
Date: 2016-10-18 00:00:00

In many security and healthcare systems a sequence of features/sensors/tests
are used for detection and diagnosis. Each test outputs a prediction of the
latent state, and carries with it inherent costs. Our objective is to {\it
learn} strategies for selecting tests to optimize accuracy \& costs.
Unfortunately it is often impossible to acquire in-situ ground truth
annotations and we are left with the problem of unsupervised sensor selection
(USS). We pose USS as a version of stochastic partial monitoring problem with
an {\it unusual} reward structure (even noisy annotations are unavailable).
Unsurprisingly no learner can achieve sublinear regret without further
assumptions. To this end we propose the notion of weak-dominance. This is a
condition on the joint probability distribution of test outputs and latent
state and says that whenever a test is accurate on an example, a later test in
the sequence is likely to be accurate as well. We empirically verify that weak
dominance holds on real datasets and prove that it is a maximal condition for
achieving sublinear regret. We reduce USS to a special case of multi-armed
bandit problem with side information and develop polynomial time algorithms
that achieve sublinear regret.
Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images
Authors: Noel Codella, Quoc-Bao Nguyen, Sharath Pankanti, David Gutman, Brian Helba, Allan Halpern, John R. Smith
Date: 2016-10-14 00:00:00
Tasks: Deep Learning, Sensitivity, Specificity

Melanoma is the deadliest form of skin cancer. While curable with early
detection, only highly trained specialists are capable of accurately
recognizing the disease. As expertise is in limited supply, automated systems
capable of identifying disease could save lives, reduce unnecessary biopsies,
and reduce costs. Toward this goal, we propose a system that combines recent
developments in deep learning with established machine learning approaches,
creating ensembles of methods that are capable of segmenting skin lesions, as
well as analyzing the detected area and surrounding tissue for melanoma
detection. The system is evaluated using the largest publicly available
benchmark dataset of dermoscopic images, containing 900 training and 379
testing images. New state-of-the-art performance levels are demonstrated,
leading to an improvement in the area under receiver operating characteristic
curve of 7.5% (0.843 vs. 0.783), in average precision of 4% (0.649 vs. 0.624),
and in specificity measured at the clinically relevant 95% sensitivity
operating point 2.9 times higher than the previous state-of-the-art (36.8%
specificity compared to 12.5%). Compared to the average of 8 expert
dermatologists on a subset of 100 test images, the proposed system produces a
higher accuracy (76% vs. 70.5%), and specificity (62% vs. 59%) evaluated at an
equivalent sensitivity (82%).
Neural networks for the prediction organic chemistry reactions
Authors: Jennifer N. Wei, David Duvenaud, Alán Aspuru-Guzik
Date: 2016-08-22 00:00:00
Tasks: Prediction

Reaction prediction remains one of the major challenges for organic
chemistry, and is a pre-requisite for efficient synthetic planning. It is
desirable to develop algorithms that, like humans, "learn" from being exposed
to examples of the application of the rules of organic chemistry. We explore
the use of neural networks for predicting reaction types, using a new reaction
fingerprinting method. We combine this predictor with SMARTS transformations to
build a system which, given a set of reagents and re- actants, predicts the
likely products. We test this method on problems from a popular organic
chemistry textbook.
End-to-end attention-based distant speech recognition with Highway LSTM
Authors: Hassan Taherian
Date: 2016-10-17 00:00:00
Tasks: Distant Speech Recognition, speech-recognition, Speech Recognition

End-to-end attention-based models have been shown to be competitive
alternatives to conventional DNN-HMM models in the Speech Recognition Systems.
In this paper, we extend existing end-to-end attention-based models that can be
applied for Distant Speech Recognition (DSR) task. Specifically, we propose an
end-to-end attention-based speech recognizer with multichannel input that
performs sequence prediction directly at the character level. To gain a better
performance, we also incorporate Highway long short-term memory (HLSTM) which
outperforms previous models on AMI distant speech recognition task.
Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observation
Authors: Yiyi Liao, Lichao Huang, Yue Wang, Sarath Kodagoda, Yinan Yu, Yong liu
Date: 2016-10-17 00:00:00
Tasks: Depth Completion, Depth Estimation

Many standard robotic platforms are equipped with at least a fixed 2D laser
range finder and a monocular camera. Although those platforms do not have
sensors for 3D depth sensing capability, knowledge of depth is an essential
part in many robotics activities. Therefore, recently, there is an increasing
interest in depth estimation using monocular images. As this task is inherently
ambiguous, the data-driven estimated depth might be unreliable in robotics
applications. In this paper, we have attempted to improve the precision of
monocular depth estimation by introducing 2D planar observation from the
remaining laser range finder without extra cost. Specifically, we construct a
dense reference map from the sparse laser range data, redefining the depth
estimation task as estimating the distance between the real and the reference
depth. To solve the problem, we construct a novel residual of residual neural
network, and tightly combine the classification and regression losses for
continuous depth estimation. Experimental results suggest that our method
achieves considerable promotion compared to the state-of-the-art methods on
both NYUD2 and KITTI, validating the effectiveness of our method on leveraging
the additional sensory information. We further demonstrate the potential usage
of our method in obstacle avoidance where our methodology provides
comprehensive depth information compared to the solution using monocular camera
or 2D laser range finder alone.
How Well Do Local Algorithms Solve Semidefinite Programs?
Authors: Zhou Fan, Andrea Montanari
Date: 2016-10-17 00:00:00

Several probabilistic models from high-dimensional statistics and machine
learning reveal an intriguing --and yet poorly understood-- dichotomy. Either
simple local algorithms succeed in estimating the object of interest, or even
sophisticated semi-definite programming (SDP) relaxations fail.
  In order to explore this phenomenon, we study a classical SDP relaxation of
the minimum graph bisection problem, when applied to Erd\H{o}s-Renyi random
graphs with bounded average degree $d>1$, and obtain several types of results.
First, we use a dual witness construction (using the so-called non-backtracking
matrix of the graph) to upper bound the SDP value. Second, we prove that a
simple local algorithm approximately solves the SDP to within a factor
$2d^2/(2d^2+d-1)$ of the upper bound. In particular, the local algorithm is at
most $8/9$ suboptimal, and $1+O(1/d)$ suboptimal for large degree.
  We then analyze a more sophisticated local algorithm, which aggregates
information according to the harmonic measure on the limiting Galton-Watson
(GW) tree. The resulting lower bound is expressed in terms of the conductance
of the GW tree and matches surprisingly well the empirically determined SDP
values on large-scale Erd\H{o}s-Renyi graphs.
  We finally consider the planted partition model. In this case, purely local
algorithms are known to fail, but they do succeed if a small amount of side
information is available. Our results imply quantitative bounds on the
threshold for partial recovery using SDP in this model.
Unsupervised Learning for Physical Interaction through Video Prediction
Authors: Chelsea Finn, Ian Goodfellow, Sergey Levine
Date: 2016-05-23 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Object, Prediction, Video Generation, Video Prediction

A core challenge for an agent learning to interact with the world is to
predict how its actions affect objects in its environment. Many existing
methods for learning the dynamics of physical interactions require labeled
object information. However, to scale real-world interaction learning to a
variety of scenes and objects, acquiring labeled data becomes increasingly
impractical. To learn about physical object motion without labels, we develop
an action-conditioned video prediction model that explicitly models pixel
motion, by predicting a distribution over pixel motion from previous frames.
Because our model explicitly predicts motion, it is partially invariant to
object appearance, enabling it to generalize to previously unseen objects. To
explore video prediction for real-world interactive agents, we also introduce a
dataset of 59,000 robot interactions involving pushing motions, including a
test set with novel objects. In this dataset, accurate prediction of videos
conditioned on the robot's future actions amounts to learning a "visual
imagination" of different futures based on different courses of action. Our
experiments show that our proposed method produces more accurate video
predictions both quantitatively and qualitatively, when compared to prior
methods.
End-to-end optimization of nonlinear transform codes for perceptual quality
Authors: Johannes Ballé, Valero Laparra, Eero P. Simoncelli
Date: 2016-07-18 00:00:00
Tasks: Quantization

We introduce a general framework for end-to-end optimization of the
rate--distortion performance of nonlinear transform codes assuming scalar
quantization. The framework can be used to optimize any differentiable pair of
analysis and synthesis transforms in combination with any differentiable
perceptual metric. As an example, we consider a code built from a linear
transform followed by a form of multi-dimensional local gain control.
Distortion is measured with a state-of-the-art perceptual metric. When
optimized over a large database of images, this representation offers
substantial improvements in bitrate and perceptual appearance over fixed (DCT)
codes, and over linear transform codes optimized for mean squared error.