pwc-archive/papers-with-abstracts huggingface dataset:
Going Deeper in Facial Expression Recognition using Deep Neural Networks
Authors: Ali Mollahosseini, David Chan, Mohammad H. Mahoor
Date: 2015-11-12 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Automated Facial Expression Recognition (FER) has remained a challenging and
interesting problem. Despite efforts made in developing various methods for
FER, existing approaches traditionally lack generalizability when applied to
unseen images or those that are captured in wild setting. Most of the existing
approaches are based on engineered features (e.g. HOG, LBPH, and Gabor) where
the classifier's hyperparameters are tuned to give best recognition accuracies
across a single database, or a small collection of similar databases.
Nevertheless, the results are not significant when they are applied to novel
data. This paper proposes a deep neural network architecture to address the FER
problem across multiple well-known standard face datasets. Specifically, our
network consists of two convolutional layers each followed by max pooling and
then four Inception layers. The network is a single component architecture that
takes registered facial images as the input and classifies them into either of
the six basic or the neutral expressions. We conducted comprehensive
experiments on seven publically available facial expression databases, viz.
MultiPIE, MMI, CK+, DISFA, FERA, SFEW, and FER2013. The results of proposed
architecture are comparable to or better than the state-of-the-art methods and
better than traditional convolutional neural networks and in both accuracy and
training time.
Deep Gaussian Conditional Random Field Network: A Model-based Deep Network for Discriminative Denoising
Authors: Raviteja Vemulapalli, Oncel Tuzel, Ming-Yu Liu
Date: 2015-11-12 00:00:00
Proceeding: CVPR 2016 6
Tasks: Denoising, Image Denoising

We propose a novel deep network architecture for image\\ denoising based on a
Gaussian Conditional Random Field (GCRF) model. In contrast to the existing
discriminative denoising methods that train a separate model for each noise
level, the proposed deep network explicitly models the input noise variance and
hence is capable of handling a range of noise levels. Our deep network, which
we refer to as deep GCRF network, consists of two sub-networks: (i) a parameter
generation network that generates the pairwise potential parameters based on
the noisy input image, and (ii) an inference network whose layers perform the
computations involved in an iterative GCRF inference procedure.\ We train the
entire deep GCRF network (both parameter generation and inference networks)
discriminatively in an end-to-end fashion by maximizing the peak
signal-to-noise ratio measure. Experiments on Berkeley segmentation and
PASCALVOC datasets show that the proposed deep GCRF network outperforms
state-of-the-art image denoising approaches for several noise levels.
Properly Learning Poisson Binomial Distributions in Almost Polynomial Time
Authors: Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
Date: 2015-11-12 00:00:00

We give an algorithm for properly learning Poisson binomial distributions. A
Poisson binomial distribution (PBD) of order $n$ is the discrete probability
distribution of the sum of $n$ mutually independent Bernoulli random variables.
Given $\widetilde{O}(1/\epsilon^2)$ samples from an unknown PBD $\mathbf{p}$,
our algorithm runs in time $(1/\epsilon)^{O(\log \log (1/\epsilon))}$, and
outputs a hypothesis PBD that is $\epsilon$-close to $\mathbf{p}$ in total
variation distance. The previously best known running time for properly
learning PBDs was $(1/\epsilon)^{O(\log(1/\epsilon))}$.
  As one of our main contributions, we provide a novel structural
characterization of PBDs. We prove that, for all $\epsilon >0,$ there exists an
explicit collection $\cal{M}$ of $(1/\epsilon)^{O(\log \log (1/\epsilon))}$
vectors of multiplicities, such that for any PBD $\mathbf{p}$ there exists a
PBD $\mathbf{q}$ with $O(\log(1/\epsilon))$ distinct parameters whose
multiplicities are given by some element of ${\cal M}$, such that $\mathbf{q}$
is $\epsilon$-close to $\mathbf{p}$. Our proof combines tools from Fourier
analysis and algebraic geometry.
  Our approach to the proper learning problem is as follows: Starting with an
accurate non-proper hypothesis, we fit a PBD to this hypothesis. More
specifically, we essentially start with the hypothesis computed by the
computationally efficient non-proper learning algorithm in our recent
work~\cite{DKS15}. Our aforementioned structural characterization allows us to
reduce the corresponding fitting problem to a collection of
$(1/\epsilon)^{O(\log \log(1/\epsilon))}$ systems of low-degree polynomial
inequalities. We show that each such system can be solved in time
$(1/\epsilon)^{O(\log \log(1/\epsilon))}$, which yields the overall running
time of our algorithm.
Efficient non-greedy optimization of decision trees
Authors: Mohammad Norouzi, Maxwell D. Collins, Matthew Johnson, David J. Fleet, Pushmeet Kohli
Date: 2015-11-12 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Structured Prediction

Decision trees and randomized forests are widely used in computer vision and
machine learning. Standard algorithms for decision tree induction optimize the
split functions one node at a time according to some splitting criteria. This
greedy procedure often leads to suboptimal trees. In this paper, we present an
algorithm for optimizing the split functions at all levels of the tree jointly
with the leaf parameters, based on a global objective. We show that the problem
of finding optimal linear-combination (oblique) splits for decision trees is
related to structured prediction with latent variables, and we formulate a
convex-concave upper bound on the tree's empirical loss. The run-time of
computing the gradient of the proposed surrogate objective with respect to each
training exemplar is quadratic in the the tree depth, and thus training deep
trees is feasible. The use of stochastic gradient descent for optimization
enables effective training with large datasets. Experiments on several
classification benchmarks demonstrate that the resulting non-greedy decision
trees outperform greedy decision tree baselines.
Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images
Authors: Roozbeh Mottaghi, Hessam Bagherinezhad, Mohammad Rastegari, Ali Farhadi
Date: 2015-11-12 00:00:00
Tasks: Object

In this paper, we study the challenging problem of predicting the dynamics of
objects in static images. Given a query object in an image, our goal is to
provide a physical understanding of the object in terms of the forces acting
upon it and its long term motion as response to those forces. Direct and
explicit estimation of the forces and the motion of objects from a single image
is extremely challenging. We define intermediate physical abstractions called
Newtonian scenarios and introduce Newtonian Neural Network ($N^3$) that learns
to map a single image to a state in a Newtonian scenario. Our experimental
evaluations show that our method can reliably predict dynamics of a query
object from a single image. In addition, our approach can provide physical
reasoning that supports the predicted dynamics in terms of velocity and force
vectors. To spur research in this direction we compiled Visual Newtonian
Dynamics (VIND) dataset that includes 6806 videos aligned with Newtonian
scenarios represented using game engines, and 4516 still images with their
ground truth dynamics.
ICON Challenge on Algorithm Selection
Authors: Lars Kotthoff
Date: 2015-11-12 00:00:00

We present the results of the ICON Challenge on Algorithm Selection.
Human Curation and Convnets: Powering Item-to-Item Recommendations on Pinterest
Authors: Dmitry Kislyuk, Yuchen Liu, David Liu, Eric Tzeng, Yushi Jing
Date: 2015-11-12 00:00:00
Tasks: Collaborative Filtering

This paper presents Pinterest Related Pins, an item-to-item recommendation
system that combines collaborative filtering with content-based ranking. We
demonstrate that signals derived from user curation, the activity of users
organizing content, are highly effective when used in conjunction with
content-based ranking. This paper also demonstrates the effectiveness of visual
features, such as image or object representations learned from convnets, in
improving the user engagement rate of our item-to-item recommendation system.
Automatic Inference of the Quantile Parameter
Authors: Karthikeyan Natesan Ramamurthy, Aleksandr Y. Aravkin, Jayaraman J. Thiagarajan
Date: 2015-11-12 00:00:00

Supervised learning is an active research area, with numerous applications in
diverse fields such as data analytics, computer vision, speech and audio
processing, and image understanding. In most cases, the loss functions used in
machine learning assume symmetric noise models, and seek to estimate the
unknown function parameters. However, loss functions such as quantile and
quantile Huber generalize the symmetric $\ell_1$ and Huber losses to the
asymmetric setting, for a fixed quantile parameter. In this paper, we propose
to jointly infer the quantile parameter and the unknown function parameters,
for the asymmetric quantile Huber and quantile losses. We explore various
properties of the quantile Huber loss and implement a convexity certificate
that can be used to check convexity in the quantile parameter. When the loss if
convex with respect to the parameter of the function, we prove that it is
biconvex in both the function and the quantile parameters, and propose an
algorithm to jointly estimate these. Results with synthetic and real data
demonstrate that the proposed approach can automatically recover the quantile
parameter corresponding to the noise and also provide an improved recovery of
function parameters. To illustrate the potential of the framework, we extend
the gradient boosting machines with quantile losses to automatically estimate
the quantile parameter at each iteration.
Rank Centrality: Ranking from Pair-wise Comparisons
Authors: Sahand Negahban, Sewoong Oh, Devavrat Shah
Date: 2012-09-08 00:00:00

The question of aggregating pair-wise comparisons to obtain a global ranking
over a collection of objects has been of interest for a very long time: be it
ranking of online gamers (e.g. MSR's TrueSkill system) and chess players,
aggregating social opinions, or deciding which product to sell based on
transactions. In most settings, in addition to obtaining a ranking, finding
`scores' for each object (e.g. player's rating) is of interest for
understanding the intensity of the preferences.
  In this paper, we propose Rank Centrality, an iterative rank aggregation
algorithm for discovering scores for objects (or items) from pair-wise
comparisons. The algorithm has a natural random walk interpretation over the
graph of objects with an edge present between a pair of objects if they are
compared; the score, which we call Rank Centrality, of an object turns out to
be its stationary probability under this random walk. To study the efficacy of
the algorithm, we consider the popular Bradley-Terry-Luce (BTL) model
(equivalent to the Multinomial Logit (MNL) for pair-wise comparisons) in which
each object has an associated score which determines the probabilistic outcomes
of pair-wise comparisons between objects. In terms of the pair-wise marginal
probabilities, which is the main subject of this paper, the MNL model and the
BTL model are identical. We bound the finite sample error rates between the
scores assumed by the BTL model and those estimated by our algorithm. In
particular, the number of samples required to learn the score well with high
probability depends on the structure of the comparison graph. When the
Laplacian of the comparison graph has a strictly positive spectral gap, e.g.
each item is compared to a subset of randomly chosen items, this leads to
dependence on the number of samples that is nearly order-optimal.
Prediction of the Yield of Enzymatic Synthesis of Betulinic Acid Ester Using Artificial Neural Networks and Support Vector Machine
Authors: Run Wang, Qiaoli Mo, Qian Zhang, Fudi Chen, Dazuo Yang
Date: 2015-11-12 00:00:00
Tasks: Prediction

3\b{eta}-O-phthalic ester of betulinic acid is of great importance in
anticancer studies. However, the optimization of its reaction conditions
requires a large number of experimental works. To simplify the number of times
of optimization in experimental works, here, we use artificial neural network
(ANN) and support vector machine (SVM) models for the prediction of yields of
3\b{eta}-O-phthalic ester of betulinic acid synthesized by betulinic acid and
phthalic anhydride using lipase as biocatalyst. General regression neural
network (GRNN), multilayer feed-forward neural network (MLFN) and the SVM
models were trained based on experimental data. Four indicators were set as
independent variables, including time (h), temperature (C), amount of enzyme
(mg) and molar ratio, while the yield of the 3\b{eta}-O-phthalic ester of
betulinic acid was set as the dependent variable. Results show that the GRNN
and SVM models have the best prediction results during the testing process,
with comparatively low RMS errors (4.01 and 4.23respectively) and short
training times (both 1s). The prediction accuracy of the GRNN and SVM are both
100% in testing process, under the tolerance of 30%.
Software Agents with Concerns of their Own
Authors: Luis Botelho, Luis Nunes, Ricardo Ribeiro, Rui J. Lopes
Date: 2015-11-12 00:00:00

We claim that it is possible to have artificial software agents for which
their actions and the world they inhabit have first-person or intrinsic
meanings. The first-person or intrinsic meaning of an entity to a system is
defined as its relation with the system's goals and capabilities, given the
properties of the environment in which it operates. Therefore, for a system to
develop first-person meanings, it must see itself as a goal-directed actor,
facing limitations and opportunities dictated by its own capabilities, and by
the properties of the environment. The first part of the paper discusses this
claim in the context of arguments against and proposals addressing the
development of computer programs with first-person meanings. A set of
definitions is also presented, most importantly the concepts of cold and
phenomenal first-person meanings. The second part of the paper presents
preliminary proposals and achievements, resulting of actual software
implementations, within a research approach that aims to develop software
agents that intrinsically understand their actions and what happens to them. As
a result, an agent with no a priori notion of its goals and capabilities, and
of the properties of its environment acquires all these notions by observing
itself in action. The cold first-person meanings of the agent's actions and of
what happens to it are defined using these acquired notions. Although not
solving the full problem of first-person meanings, the proposed approach and
preliminary results allow us some confidence to address the problems yet to be
considered, in particular the phenomenal aspect of first-person meanings.
Bayesian Analysis of Dynamic Linear Topic Models
Authors: Chris Glynn, Surya T. Tokdar, David L. Banks, Brian Howard
Date: 2015-11-12 00:00:00
Tasks: Data Augmentation, Dynamic Topic Modeling, Topic Models

In dynamic topic modeling, the proportional contribution of a topic to a
document depends on the temporal dynamics of that topic's overall prevalence in
the corpus. We extend the Dynamic Topic Model of Blei and Lafferty (2006) by
explicitly modeling document level topic proportions with covariates and
dynamic structure that includes polynomial trends and periodicity. A Markov
Chain Monte Carlo (MCMC) algorithm that utilizes Polya-Gamma data augmentation
is developed for posterior inference. Conditional independencies in the model
and sampling are made explicit, and our MCMC algorithm is parallelized where
possible to allow for inference in large corpora. To address computational
bottlenecks associated with Polya-Gamma sampling, we appeal to the Central
Limit Theorem to develop a Gaussian approximation to the Polya-Gamma random
variable. This approximation is fast and reliable for parameter values relevant
in the text mining domain. Our model and inference algorithm are validated with
multiple simulation examples, and we consider the application of modeling
trends in PubMed abstracts. We demonstrate that sharing information across
documents is critical for accurately estimating document-specific topic
proportions. We also show that explicitly modeling polynomial and periodic
behavior improves our ability to predict topic prevalence at future time
points.
A Multilingual FrameNet-based Grammar and Lexicon for Controlled Natural Language
Authors: Normunds Gruzitis, Dana Dannélls
Date: 2015-11-12 00:00:00

Berkeley FrameNet is a lexico-semantic resource for English based on the
theory of frame semantics. It has been exploited in a range of natural language
processing applications and has inspired the development of framenets for many
languages. We present a methodological approach to the extraction and
generation of a computational multilingual FrameNet-based grammar and lexicon.
The approach leverages FrameNet-annotated corpora to automatically extract a
set of cross-lingual semantico-syntactic valence patterns. Based on data from
Berkeley FrameNet and Swedish FrameNet, the proposed approach has been
implemented in Grammatical Framework (GF), a categorial grammar formalism
specialized for multilingual grammars. The implementation of the grammar and
lexicon is supported by the design of FrameNet, providing a frame semantic
abstraction layer, an interlingual semantic API (application programming
interface), over the interlingual syntactic API already provided by GF Resource
Grammar Library. The evaluation of the acquired grammar and lexicon shows the
feasibility of the approach. Additionally, we illustrate how the FrameNet-based
grammar and lexicon are exploited in two distinct multilingual controlled
natural language applications. The produced resources are available under an
open source license.
IBMMS Decision Support Tool For Management of Bank Telemarketing Campaigns
Authors: Ali Keles, Ayturk Keles
Date: 2015-11-11 00:00:00
Tasks: Management, Marketing

Although direct marketing is a good method for banks to utilize in the face
of global competition and the financial crisis, it has been shown to exhibit
poor performance. However, there are some drawbacks to direct campaigns, such
as those related to improving the negative attributes that customers ascribe to
banks. To overcome these problems, attractive long-term deposit campaigns
should be organized and managed more effectively. The aim of this study is to
develop an Intelligent Bank Market Management System (IBMMS) for bank managers
who want to manage efficient marketing campaigns. IBMMS is the first system
developed by combining the power of data mining with the capabilities of expert
systems in this area. Moreover, IBMMS includes important features that enable
it to be intelligent: a knowledge base, an inference engine and an advisor.
Using this system, a manager can successfully direct marketing campaigns and
follow the decision schemas of customers both as individuals and as a group;
moreover, a manager can make decisions that lead to the desired response by
customers.
IfcWoD, Semantically Adapting IFC Model Relations into OWL Properties
Authors: Tarcisio Mendes de Farias, Ana Roxin, Christophe Nicolle
Date: 2015-11-12 00:00:00
Tasks: Management

In the context of Building Information Modelling, ontologies have been
identified as interesting in achieving information interoperability. Regarding
the construction and facility management domains, several IFC (Industry
Foundation Classes) based ontologies have been developed, such as IfcOWL. In
the context of ontology modelling, the constraint of optimizing the size of IFC
STEP-based files can be leveraged. In this paper, we propose an adaptation of
the IFC model into OWL which leverages from all modelling constraints required
by the object-oriented structure of IFC schema. Therefore, we do not only
present a syntactic but also a semantic adaptation of the IFC model. Our model
takes into consideration the meaning of entities, relationships, properties and
attributes defined by the IFC standard. Our approach presents several
advantages compared to other initiatives such as the optimization of query
execution time. Every advantage is defended by means of practical examples and
benchmarks.
Private False Discovery Rate Control
Authors: Cynthia Dwork, Weijie Su, Li Zhang
Date: 2015-11-12 00:00:00
Tasks: Privacy Preserving, Two-sample testing

We provide the first differentially private algorithms for controlling the
false discovery rate (FDR) in multiple hypothesis testing, with essentially no
loss in power under certain conditions. Our general approach is to adapt a
well-known variant of the Benjamini-Hochberg procedure (BHq), making each step
differentially private. This destroys the classical proof of FDR control. To
prove FDR control of our method, (a) we develop a new proof of the original
(non-private) BHq algorithm and its robust variants -- a proof requiring only
the assumption that the true null test statistics are independent, allowing for
arbitrary correlations between the true nulls and false nulls. This assumption
is fairly weak compared to those previously shown in the vast literature on
this topic, and explains in part the empirical robustness of BHq. Then (b) we
relate the FDR control properties of the differentially private version to the
control properties of the non-private version. \end{enumerate} We also present
a low-distortion "one-shot" differentially private primitive for "top $k$"
problems, e.g., "Which are the $k$ most popular hobbies?" (which we apply to:
"Which hypotheses have the $k$ most significant $p$-values?"), and use it to
get a faster privacy-preserving instantiation of our general approach at little
cost in accuracy. The proof of privacy for the one-shot top~$k$ algorithm
introduces a new technique of independent interest.
Dynamic Belief Fusion for Object Detection
Authors: Ryan Robinson
Date: 2015-02-26 00:00:00
Tasks: Object, object-detection, Object Detection

A novel approach for the fusion of detection scores from disparate object
detection methods is proposed. In order to effectively integrate the outputs of
multiple detectors, the level of ambiguity in each individual detection score
(called "uncertainty") is estimated using the precision/recall relationship of
the corresponding detector. The proposed fusion method, called Dynamic Belief
Fusion (DBF), dynamically assigns basic probabilities to propositions (target,
non-target, uncertain) based on confidence levels in the detection results of
individual approaches. A joint basic probability assignment, containing
information from all detectors, is determined using Dempster's combination
rule, and is easily reduced to a single fused detection score. Experiments on
ARL and PASCAL VOC 07 datasets demonstrate that the detection accuracy of DBF
is considerably greater than conventional fusion approaches as well as
state-of-the-art individual detectors.