pwc-archive/papers-with-abstracts huggingface dataset:
A comprehensive study of sparse codes on abnormality detection
Authors: Huamin Ren, Hong Pan, Søren Ingvor Olsen, Thomas B. Moeslund
Date: 2016-03-13 00:00:00
Tasks: Anomaly Detection, Event Detection

Sparse representation has been applied successfully in abnormal event
detection, in which the baseline is to learn a dictionary accompanied by sparse
codes. While much emphasis is put on discriminative dictionary construction,
there are no comparative studies of sparse codes regarding abnormality
detection. We comprehensively study two types of sparse codes solutions -
greedy algorithms and convex L1-norm solutions - and their impact on
abnormality detection performance. We also propose our framework of combining
sparse codes with different detection methods. Our comparative experiments are
carried out from various angles to better understand the applicability of
sparse codes, including computation time, reconstruction error, sparsity,
detection accuracy, and their performance combining various detection methods.
Experiments show that combining OMP codes with maximum coordinate detection
could achieve state-of-the-art performance on the UCSD dataset [14].
Conditional Risk Minimization for Stochastic Processes
Authors: Alexander Zimin, Christoph H. Lampert
Date: 2015-10-09 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Prediction

We study the task of learning from non-i.i.d. data. In particular, we aim at
learning predictors that minimize the conditional risk for a stochastic
process, i.e. the expected loss of the predictor on the next point conditioned
on the set of training samples observed so far. For non-i.i.d. data, the
training set contains information about the upcoming samples, so learning with
respect to the conditional distribution can be expected to yield better
predictors than one obtains from the classical setting of minimizing the
marginal risk. Our main contribution is a practical estimator for the
conditional risk based on the theory of non-parametric time-series prediction,
and a finite sample concentration bound that establishes uniform convergence of
the estimator to the true conditional risk under certain regularity assumptions
on the process.
Complexity Theoretic Limitations on Learning Halfspaces
Authors: Amit Daniely
Date: 2015-05-21 00:00:00

We study the problem of agnostically learning halfspaces which is defined by
a fixed but unknown distribution $\mathcal{D}$ on $\mathbb{Q}^n\times \{\pm
1\}$. We define $\mathrm{Err}_{\mathrm{HALF}}(\mathcal{D})$ as the least error
of a halfspace classifier for $\mathcal{D}$. A learner who can access
$\mathcal{D}$ has to return a hypothesis whose error is small compared to
$\mathrm{Err}_{\mathrm{HALF}}(\mathcal{D})$.
  Using the recently developed method of the author, Linial and Shalev-Shwartz
we prove hardness of learning results under a natural assumption on the
complexity of refuting random $K$-$\mathrm{XOR}$ formulas. We show that no
efficient learning algorithm has non-trivial worst-case performance even under
the guarantees that $\mathrm{Err}_{\mathrm{HALF}}(\mathcal{D}) \le \eta$ for
arbitrarily small constant $\eta>0$, and that $\mathcal{D}$ is supported in
$\{\pm 1\}^n\times \{\pm 1\}$. Namely, even under these favorable conditions
its error must be $\ge \frac{1}{2}-\frac{1}{n^c}$ for every $c>0$. In
particular, no efficient algorithm can achieve a constant approximation ratio.
Under a stronger version of the assumption (where $K$ can be poly-logarithmic
in $n$), we can take $\eta = 2^{-\log^{1-\nu}(n)}$ for arbitrarily small
$\nu>0$. Interestingly, this is even stronger than the best known lower bounds
(Arora et. al. 1993, Feldamn et. al. 2006, Guruswami and Raghavendra 2006) for
the case that the learner is restricted to return a halfspace classifier (i.e.
proper learning).
Fast Learning from Distributed Datasets without Entity Matching
Authors: Giorgio Patrini, Richard Nock, Stephen Hardy, Tiberio Caetano
Date: 2016-03-13 00:00:00
Tasks: Entity Resolution

Consider the following data fusion scenario: two datasets/peers contain the
same real-world entities described using partially shared features, e.g.
banking and insurance company records of the same customer base. Our goal is to
learn a classifier in the cross product space of the two domains, in the hard
case in which no shared ID is available -- e.g. due to anonymization.
Traditionally, the problem is approached by first addressing entity matching
and subsequently learning the classifier in a standard manner. We present an
end-to-end solution which bypasses matching entities, based on the recently
introduced concept of Rademacher observations (rados). Informally, we replace
the minimisation of a loss over examples, which requires to solve entity
resolution, by the equivalent minimisation of a (different) loss over rados.
Among others, key properties we show are (i) a potentially huge subset of these
rados does not require to perform entity matching, and (ii) the algorithm that
provably minimizes the rado loss over these rados has time and space
complexities smaller than the algorithm minimizing the equivalent example loss.
Last, we relax a key assumption of the model, that the data is vertically
partitioned among peers --- in this case, we would not even know the existence
of a solution to entity resolution. In this more general setting, experiments
validate the possibility of significantly beating even the optimal peer in
hindsight.
Learning Typographic Style
Authors: Shumeet Baluja
Date: 2016-03-13 00:00:00
Tasks: Font Generation, Font Recognition

Typography is a ubiquitous art form that affects our understanding,
perception, and trust in what we read. Thousands of different font-faces have
been created with enormous variations in the characters. In this paper, we
learn the style of a font by analyzing a small subset of only four letters.
From these four letters, we learn two tasks. The first is a discrimination
task: given the four letters and a new candidate letter, does the new letter
belong to the same font? Second, given the four basis letters, can we generate
all of the other letters with the same characteristics as those in the basis
set? We use deep neural networks to address both tasks, quantitatively and
qualitatively measure the results in a variety of novel manners, and present a
thorough investigation of the weaknesses and strengths of the approach.
An efficient Exact-PGA algorithm for constant curvature manifolds
Authors: Rudrasis Chakraborty, Dohyung Seo, Baba C. Vemuri
Date: 2016-03-13 00:00:00
Proceeding: CVPR 2016 6

Manifold-valued datasets are widely encountered in many computer vision
tasks. A non-linear analog of the PCA, called the Principal Geodesic Analysis
(PGA) suited for data lying on Riemannian manifolds was reported in literature
a decade ago. Since the objective function in PGA is highly non-linear and hard
to solve efficiently in general, researchers have proposed a linear
approximation. Though this linear approximation is easy to compute, it lacks
accuracy especially when the data exhibits a large variance. Recently, an
alternative called exact PGA was proposed which tries to solve the optimization
without any linearization. For general Riemannian manifolds, though it gives
better accuracy than the original (linearized) PGA, for data that exhibit large
variance, the optimization is not computationally efficient. In this paper, we
propose an efficient exact PGA for constant curvature Riemannian manifolds
(CCM-EPGA). CCM-EPGA differs significantly from existing PGA algorithms in two
aspects, (i) the distance between a given manifold-valued data point and the
principal submanifold is computed analytically and thus no optimization is
required as in existing methods. (ii) Unlike the existing PGA algorithms, the
descent into codimension-1 submanifolds does not require any optimization but
is accomplished through the use of the Rimeannian inverse Exponential map and
the parallel transport operations. We present theoretical and experimental
results for constant curvature Riemannian manifolds depicting favorable
performance of CCM-EPGA compared to existing PGA algorithms. We also present
data reconstruction from principal components and directions which has not been
presented in literature in this setting.
Temporally Robust Global Motion Compensation by Keypoint-based Congealing
Authors: S. Morteza Safdarnejad, Yousef Atoum, Xiaoming Liu
Date: 2016-03-12 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Motion Compensation, Motion Estimation, Multi-Object Tracking, Object Tracking

Global motion compensation (GMC) removes the impact of camera motion and
creates a video in which the background appears static over the progression of
time. Various vision problems, such as human activity recognition, background
reconstruction, and multi-object tracking can benefit from GMC. Existing GMC
algorithms rely on sequentially processing consecutive frames, by estimating
the transformation mapping the two frames, and obtaining a composite
transformation to a global motion compensated coordinate. Sequential GMC
suffers from temporal drift of frames from the accurate global coordinate, due
to either error accumulation or sporadic failures of motion estimation at a few
frames. We propose a temporally robust global motion compensation (TRGMC)
algorithm which performs accurate and stable GMC, despite complicated and
long-term camera motion. TRGMC densely connects pairs of frames, by matching
local keypoints of each frame. A joint alignment of these frames is formulated
as a novel keypoint-based congealing problem, where the transformation of each
frame is updated iteratively, such that the spatial coordinates for the start
and end points of matched keypoints are identical. Experimental results
demonstrate that TRGMC has superior performance in a wide range of scenarios.
Image Captioning with Semantic Attention
Authors: Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, Jiebo Luo
Date: 2016-03-12 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Captioning

Automatically generating a natural language description of an image has
attracted interests recently both because of its importance in practical
applications and because it connects two major artificial intelligence fields:
computer vision and natural language processing. Existing approaches are either
top-down, which start from a gist of an image and convert it into words, or
bottom-up, which come up with words describing various aspects of an image and
then combine them. In this paper, we propose a new algorithm that combines both
approaches through a model of semantic attention. Our algorithm learns to
selectively attend to semantic concept proposals and fuse them into hidden
states and outputs of recurrent neural networks. The selection and fusion form
a feedback connecting the top-down and bottom-up computation. We evaluate our
algorithm on two public benchmarks: Microsoft COCO and Flickr30K. Experimental
results show that our algorithm significantly outperforms the state-of-the-art
approaches consistently across different evaluation metrics.
Characterization of neighborhood behaviours in a multi-neighborhood local search algorithm
Authors: Nguyen Thi Thanh Dang, Patrick De Causmaecker
Date: 2016-03-12 00:00:00
Tasks: SMAC, SMAC+

We consider a multi-neighborhood local search algorithm with a large number
of possible neighborhoods. Each neighborhood is accompanied by a weight value
which represents the probability of being chosen at each iteration. These
weights are fixed before the algorithm runs, and are considered as parameters
of the algorithm. Given a set of instances, off-line tuning of the algorithm's
parameters can be done by automated algorithm configuration tools (e.g., SMAC).
However, the large number of neighborhoods can make the tuning expensive and
difficult even when the number of parameters has been reduced by some
intuition. In this work, we propose a systematic method to characterize each
neighborhood's behaviours, representing them as a feature vector, and using
cluster analysis to form similar groups of neighborhoods. The novelty of our
characterization method is the ability of reflecting changes of behaviours
according to hardness of different solution quality regions. We show that using
neighborhood clusters instead of individual neighborhoods helps to reduce the
parameter configuration space without misleading the search of the tuning
procedure. Moreover, this method is problem-independent and potentially can be
applied in similar contexts.
Grounding Recursive Aggregates: Preliminary Report
Authors: Martin Gebser, Roland Kaminski, Torsten Schaub
Date: 2016-03-12 00:00:00

Problem solving in Answer Set Programming consists of two steps, a first
grounding phase, systematically replacing all variables by terms, and a second
solving phase computing the stable models of the obtained ground program. An
intricate part of both phases is the treatment of aggregates, which are popular
language constructs that allow for expressing properties over sets. In this
paper, we elaborate upon the treatment of aggregates during grounding in Gringo
series 4. Consequently, our approach is applicable to grounding based on
semi-naive database evaluation techniques. In particular, we provide a series
of algorithms detailing the treatment of recursive aggregates and illustrate
this by a running example.
Towards Building an RGBD-M Scanner
Authors: Zhe Wu, Sai-Kit Yeung, Ping Tan
Date: 2016-03-12 00:00:00
Tasks: Material Segmentation

We present a portable device to capture both shape and reflectance of an
indoor scene. Consisting of a Kinect, an IR camera and several IR LEDs, our
device allows the user to acquire data in a similar way as he/she scans with a
single Kinect. Scene geometry is reconstructed by KinectFusion. To estimate
reflectance from incomplete and noisy observations, 3D vertices of the same
material are identified by our material segmentation propagation algorithm.
Then BRDF observations at these vertices are merged into a more complete and
accurate BRDF for the material. Effectiveness of our device is demonstrated by
quality results on real-world scenes.
Neural Discourse Relation Recognition with Semantic Memory
Authors: Biao Zhang, Deyi Xiong, Jinsong Su
Date: 2016-03-12 00:00:00
Tasks: General Knowledge, Relation, Word Embeddings

Humans comprehend the meanings and relations of discourses heavily relying on
their semantic memory that encodes general knowledge about concepts and facts.
Inspired by this, we propose a neural recognizer for implicit discourse
relation analysis, which builds upon a semantic memory that stores knowledge in
a distributed fashion. We refer to this recognizer as SeMDER. Starting from
word embeddings of discourse arguments, SeMDER employs a shallow encoder to
generate a distributed surface representation for a discourse. A semantic
encoder with attention to the semantic memory matrix is further established
over surface representations. It is able to retrieve a deep semantic meaning
representation for the discourse from the memory. Using the surface and
semantic representations as input, SeMDER finally predicts implicit discourse
relations via a neural recognizer. Experiments on the benchmark data set show
that SeMDER benefits from the semantic memory and achieves substantial
improvements of 2.56\% on average over current state-of-the-art baselines in
terms of F1-score.
Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation
Authors: Hyo-Eun Kim, Sangheum Hwang
Date: 2016-02-16 00:00:00
Tasks: Lesion Segmentation, Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

A weakly-supervised semantic segmentation framework with a tied
deconvolutional neural network is presented. Each deconvolution layer in the
framework consists of unpooling and deconvolution operations. 'Unpooling'
upsamples the input feature map based on unpooling switches defined by
corresponding convolution layer's pooling operation. 'Deconvolution' convolves
the input unpooled features by using convolutional weights tied with the
corresponding convolution layer's convolution operation. The
unpooling-deconvolution combination helps to eliminate less discriminative
features in a feature extraction stage, since output features of the
deconvolution layer are reconstructed from the most discriminative unpooled
features instead of the raw one. This results in reduction of false positives
in a pixel-level inference stage. All the feature maps restored from the entire
deconvolution layers can constitute a rich discriminative feature set according
to different abstraction levels. Those features are stacked to be selectively
used for generating class-specific activation maps. Under the weak supervision
(image-level labels), the proposed framework shows promising results on lesion
segmentation in medical images (chest X-rays) and achieves state-of-the-art
performance on the PASCAL VOC segmentation dataset in the same experimental
condition.
A Statistical Decision-Theoretic Framework for Social Choice
Authors: Hossein Azari Soufiani, David C. Parkes, Lirong Xia
Date: 2014-10-29 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Decision Making

In this paper, we take a statistical decision-theoretic viewpoint on social
choice, putting a focus on the decision to be made on behalf of a system of
agents. In our framework, we are given a statistical ranking model, a decision
space, and a loss function defined on (parameter, decision) pairs, and
formulate social choice mechanisms as decision rules that minimize expected
loss. This suggests a general framework for the design and analysis of new
social choice mechanisms. We compare Bayesian estimators, which minimize
Bayesian expected loss, for the Mallows model and the Condorcet model
respectively, and the Kemeny rule. We consider various normative properties, in
addition to computational complexity and asymptotic behavior. In particular, we
show that the Bayesian estimator for the Condorcet model satisfies some desired
properties such as anonymity, neutrality, and monotonicity, can be computed in
polynomial time, and is asymptotically different from the other two rules when
the data are generated from the Condorcet model for some ground truth
parameter.
Real-time 3D scene description using Spheres, Cones and Cylinders
Authors: Kristiyan Georgiev, Motaz Al-Hami, Rolf Lakaemper
Date: 2016-03-12 00:00:00

The paper describes a novel real-time algorithm for finding 3D geometric
primitives (cylinders, cones and spheres) from 3D range data. In its core, it
performs a fast model fitting with a model update in constant time (O(1)) for
each new data point added to the model. We use a three stage approach.The first
step inspects 1.5D sub spaces, to find ellipses. The next stage uses these
ellipses as input by examining their neighborhood structure to form sets of
candidates for the 3D geometric primitives. Finally, candidate ellipses are
fitted to the geometric primitives. The complexity for point processing is
O(n); additional time of lower order is needed for working on significantly
smaller amount of mid-level objects. This allows the approach to process 30
frames per second on Kinect depth data, which suggests this approach as a
pre-processing step for 3D real-time higher level tasks in robotics, like
tracking or feature based mapping.
Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks
Authors: Ji Young Lee, Franck Dernoncourt
Date: 2016-03-12 00:00:00
Proceeding: NAACL 2016 6
Tasks: Classification, General Classification, text-classification, Text Classification

Recent approaches based on artificial neural networks (ANNs) have shown
promising results for short-text classification. However, many short texts
occur in sequences (e.g., sentences in a document or utterances in a dialog),
and most existing ANN-based systems do not leverage the preceding short texts
when classifying a subsequent one. In this work, we present a model based on
recurrent neural networks and convolutional neural networks that incorporates
the preceding short texts. Our model achieves state-of-the-art results on three
different datasets for dialog act prediction.
Solving MaxSAT by Successive Calls to a SAT Solver
Authors: Mohamed El Halaby
Date: 2016-03-11 00:00:00

The Maximum Satisfiability (MaxSAT) problem is the problem of finding a truth
assignment that maximizes the number of satisfied clauses of a given Boolean
formula in Conjunctive Normal Form (CNF). Many exact solvers for MaxSAT have
been developed during recent years, and many of them were presented in the
well-known SAT conference. Algorithms for MaxSAT generally fall into two
categories: (1) branch and bound algorithms and (2) algorithms that use
successive calls to a SAT solver (SAT- based), which this paper in on. In
practical problems, SAT-based algorithms have been shown to be more efficient.
This paper provides an experimental investigation to compare the performance of
recent SAT-based and branch and bound algorithms on the benchmarks of the
MaxSAT Evaluations.
Personalized Speech recognition on mobile devices
Authors: Ian McGraw, Rohit Prabhavalkar, Raziel Alvarez, Montse Gonzalez Arenas, Kanishka Rao, David Rybach, Ouais Alsharif, Hasim Sak, Alexander Gruenstein, Francoise Beaufays, Carolina Parada
Date: 2016-03-10 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

We describe a large vocabulary speech recognition system that is accurate,
has low latency, and yet has a small enough memory and computational footprint
to run faster than real-time on a Nexus 5 Android smartphone. We employ a
quantized Long Short-Term Memory (LSTM) acoustic model trained with
connectionist temporal classification (CTC) to directly predict phoneme
targets, and further reduce its memory footprint using an SVD-based compression
scheme. Additionally, we minimize our memory footprint by using a single
language model for both dictation and voice command domains, constructed using
Bayesian interpolation. Finally, in order to properly handle device-specific
information, such as proper names and other context-dependent information, we
inject vocabulary items into the decoder graph and bias the language model
on-the-fly. Our system achieves 13.5% word error rate on an open-ended
dictation task, running with a median speed that is seven times faster than
real-time.
Demonstrating the Feasibility of Automatic Game Balancing
Authors: Vanessa Volz, Günter Rudolph, Boris Naujoks
Date: 2016-03-11 00:00:00
Tasks: Fairness, Game Design, Real-Time Strategy Games

Game balancing is an important part of the (computer) game design process, in
which designers adapt a game prototype so that the resulting gameplay is as
entertaining as possible. In industry, the evaluation of a game is often based
on costly playtests with human players. It suggests itself to automate this
process using surrogate models for the prediction of gameplay and outcome. In
this paper, the feasibility of automatic balancing using simulation- and
deck-based objectives is investigated for the card game top trumps.
Additionally, the necessity of a multi-objective approach is asserted by a
comparison with the only known (single-objective) method. We apply a
multi-objective evolutionary algorithm to obtain decks that optimise
objectives, e.g. win rate and average number of tricks, developed to express
the fairness and the excitement of a game of top trumps. The results are
compared with decks from published top trumps decks using simulation-based
objectives. The possibility to generate decks better or at least as good as
decks from published top trumps decks in terms of these objectives is
demonstrated. Our results indicate that automatic balancing with the presented
approach is feasible even for more complex games such as real-time strategy
games.