pwc-archive/papers-with-abstracts huggingface dataset:
The Mean Partition Theorem of Consensus Clustering
Authors: Brijnesh J. Jain
Date: 2016-04-22 00:00:00
Tasks: Clustering

To devise efficient solutions for approximating a mean partition in consensus
clustering, Dimitriadou et al. [3] presented a necessary condition of
optimality for a consensus function based on least square distances. We show
that their result is pivotal for deriving interesting properties of consensus
clustering beyond optimization. For this, we present the necessary condition of
optimality in a slightly stronger form in terms of the Mean Partition Theorem
and extend it to the Expected Partition Theorem. To underpin its versatility,
we show three examples that apply the Mean Partition Theorem: (i) equivalence
of the mean partition and optimal multiple alignment, (ii) construction of
profiles and motifs, and (iii) relationship between consensus clustering and
cluster stability.
Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification
Authors: Tong Xiao, Hongsheng Li, Wanli Ouyang, Xiaogang Wang
Date: 2016-04-26 00:00:00
Proceeding: CVPR 2016 6
Tasks: Person Re-Identification

Learning generic and robust feature representations with data from multiple
domains for the same problem is of great value, especially for the problems
that have multiple datasets but none of them are large enough to provide
abundant data variations. In this work, we present a pipeline for learning deep
feature representations from multiple domains with Convolutional Neural
Networks (CNNs). When training a CNN with data from all the domains, some
neurons learn representations shared across several domains, while some others
are effective only for a specific one. Based on this important observation, we
propose a Domain Guided Dropout algorithm to improve the feature learning
procedure. Experiments show the effectiveness of our pipeline and the proposed
algorithm. Our methods on the person re-identification problem outperform
state-of-the-art methods on multiple datasets by large margins.
Regularizing RNNs by Stabilizing Activations
Authors: David Krueger, Roland Memisevic
Date: 2015-11-26 00:00:00
Tasks: Language Modeling, Language Modelling, Phoneme Recognition

We stabilize the activations of Recurrent Neural Networks (RNNs) by
penalizing the squared distance between successive hidden states' norms.
  This penalty term is an effective regularizer for RNNs including LSTMs and
IRNNs, improving performance on character-level language modeling and phoneme
recognition, and outperforming weight noise and dropout.
  We achieve competitive performance (18.6\% PER) on the TIMIT phoneme
recognition task for RNNs evaluated without beam search or an RNN transducer.
  With this penalty term, IRNN can achieve similar performance to LSTM on
language modeling, although adding the penalty term to the LSTM results in
superior performance.
  Our penalty term also prevents the exponential growth of IRNN's activations
outside of their training horizon, allowing them to generalize to much longer
sequences.
DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
Authors: Leonid Pishchulin, Eldar Insafutdinov, Siyu Tang, Bjoern Andres, Mykhaylo Andriluka, Peter Gehler, Bernt Schiele
Date: 2015-11-20 00:00:00
Proceeding: CVPR 2016 6
Tasks: Multi-Person Pose Estimation, Pose Estimation

This paper considers the task of articulated human pose estimation of
multiple people in real world images. We propose an approach that jointly
solves the tasks of detection and pose estimation: it infers the number of
persons in a scene, identifies occluded body parts, and disambiguates body
parts between people in close proximity of each other. This joint formulation
is in contrast to previous strategies, that address the problem by first
detecting people and subsequently estimating their body pose. We propose a
partitioning and labeling formulation of a set of body-part hypotheses
generated with CNN-based part detectors. Our formulation, an instance of an
integer linear program, implicitly performs non-maximum suppression on the set
of part candidates and groups them to form configurations of body parts
respecting geometric and appearance constraints. Experiments on four different
datasets demonstrate state-of-the-art results for both single person and multi
person pose estimation. Models and code available at
http://pose.mpi-inf.mpg.de.
Once for All: a Two-flow Convolutional Neural Network for Visual Tracking
Authors: Kai Chen, Wenbing Tao
Date: 2016-04-26 00:00:00
Tasks: All, Object, Object Tracking, Visual Object Tracking, Visual Tracking

One of the main challenges of visual object tracking comes from the arbitrary
appearance of objects. Most existing algorithms try to resolve this problem as
an object-specific task, i.e., the model is trained to regenerate or classify a
specific object. As a result, the model need to be initialized and retrained
for different objects. In this paper, we propose a more generic approach
utilizing a novel two-flow convolutional neural network (named YCNN). The YCNN
takes two inputs (one is object image patch, the other is search image patch),
then outputs a response map which predicts how likely the object appears in a
specific location. Unlike those object-specific approach, the YCNN is trained
to measure the similarity between two image patches. Thus it will not be
confined to any specific object. Furthermore the network can be end-to-end
trained to extract both shallow and deep convolutional features which are
dedicated for visual tracking. And once properly trained, the YCNN can be
applied to track all kinds of objects without further training and updating.
Benefiting from the once-for-all model, our algorithm is able to run at a very
high speed of 45 frames-per-second. The experiments on 51 sequences also show
that our algorithm achieves an outstanding performance.
Modern Physiognomy: An Investigation on Predicting Personality Traits and Intelligence from the Human Face
Authors: Rizhen Qin, Wei Gao, Huarong Xu, Zhanyi Hu
Date: 2016-04-26 00:00:00
Tasks: General Classification, regression

The human behavior of evaluating other individuals with respect to their
personality traits and intelligence by evaluating their faces plays a crucial
role in human relations. These trait judgments might influence important social
outcomes in our lives such as elections and court sentences. Previous studies
have reported that human can make valid inferences for at least four
personality traits. In addition, some studies have demonstrated that facial
trait evaluation can be learned using machine learning methods accurately. In
this work, we experimentally explore whether self-reported personality traits
and intelligence can be predicted reliably from a facial image. More
specifically, the prediction problem is separately cast in two parts: a
classification task and a regression task. A facial structural feature is
constructed from the relations among facial salient points, and an appearance
feature is built by five texture descriptors. In addition, a minutia-based
fingerprint feature from a fingerprint image is also explored. The
classification results show that the personality traits "Rule-consciousness"
and "Vigilance" can be predicted reliably, and that the traits of females can
be predicted more accurately than those of male. However, the regression
experiments show that it is difficult to predict scores for individual
personality traits and intelligence. The residual plots and the correlation
results indicate no evident linear correlation between the measured scores and
the predicted scores. Both the classification and the regression results reveal
that "Rule-consciousness" and "Tension" can be reliably predicted from the
facial features, while "Social boldness" gets the worst prediction results. The
experiments results show that it is difficult to predict intelligence from
either the facial features or the fingerprint feature, a finding that is in
agreement with previous studies.
Neurohex: A Deep Q-learning Hex Agent
Authors: Kenny Young, Ryan Hayward, Gautham Vasan
Date: 2016-04-24 00:00:00
Tasks: Atari Games, Game of Go, Q-Learning, Reinforcement Learning

DeepMind's recent spectacular success in using deep convolutional neural nets
and machine learning to build superhuman level agents --- e.g. for Atari games
via deep Q-learning and for the game of Go via Reinforcement Learning ---
raises many questions, including to what extent these methods will succeed in
other domains. In this paper we consider DQL for the game of Hex: after
supervised initialization, we use selfplay to train NeuroHex, an 11-layer CNN
that plays Hex on the 13x13 board. Hex is the classic two-player alternate-turn
stone placement game played on a rhombus of hexagonal cells in which the winner
is whomever connects their two opposing sides. Despite the large action and
state space, our system trains a Q-network capable of strong play with no
search. After two weeks of Q-learning, NeuroHex achieves win-rates of 20.4% as
first player and 2.1% as second player against a 1-second/move version of
MoHex, the current ICGA Olympiad Hex champion. Our data suggests further
improvement might be possible with more training time.
Deep Multi-fidelity Gaussian Processes
Authors: Maziar Raissi, George Karniadakis
Date: 2016-04-26 00:00:00
Tasks: Gaussian Processes

We develop a novel multi-fidelity framework that goes far beyond the
classical AR(1) Co-kriging scheme of Kennedy and O'Hagan (2000). Our method can
handle general discontinuous cross-correlations among systems with different
levels of fidelity. A combination of multi-fidelity Gaussian Processes (AR(1)
Co-kriging) and deep neural networks enables us to construct a method that is
immune to discontinuities. We demonstrate the effectiveness of the new
technology using standard benchmark problems designed to resemble the outputs
of complicated high- and low-fidelity codes.
Sparse PCA via Covariance Thresholding
Authors: Yash Deshpande, Andrea Montanari
Date: 2013-11-20 00:00:00
Proceeding: NeurIPS 2014 12

In sparse principal component analysis we are given noisy observations of a
low-rank matrix of dimension $n\times p$ and seek to reconstruct it under
additional sparsity assumptions. In particular, we assume here each of the
principal components $\mathbf{v}_1,\dots,\mathbf{v}_r$ has at most $s_0$
non-zero entries. We are particularly interested in the high dimensional regime
wherein $p$ is comparable to, or even much larger than $n$. In an influential
paper, \cite{johnstone2004sparse} introduced a simple algorithm that estimates
the support of the principal vectors $\mathbf{v}_1,\dots,\mathbf{v}_r$ by the
largest entries in the diagonal of the empirical covariance. This method can be
shown to identify the correct support with high probability if $s_0\le
K_1\sqrt{n/\log p}$, and to fail with high probability if $s_0\ge K_2
\sqrt{n/\log p}$ for two constants $0<K_1,K_2<\infty$. Despite a considerable
amount of work over the last ten years, no practical algorithm exists with
provably better support recovery guarantees.
  Here we analyze a covariance thresholding algorithm that was recently
proposed by \cite{KrauthgamerSPCA}. On the basis of numerical simulations (for
the rank-one case), these authors conjectured that covariance thresholding
correctly recover the support with high probability for $s_0\le K\sqrt{n}$
(assuming $n$ of the same order as $p$). We prove this conjecture, and in fact
establish a more general guarantee including higher-rank as well as $n$ much
smaller than $p$. Recent lower bounds \cite{berthet2013computational,
ma2015sum} suggest that no polynomial time algorithm can do significantly
better. The key technical component of our analysis develops new bounds on the
norm of kernel random matrices, in regimes that were not considered before.
Dynamic Pricing with Demand Covariates
Authors: Sheng Qiang, Mohsen Bayati
Date: 2016-04-25 00:00:00

We consider a firm that sells products over $T$ periods without knowing the
demand function. The firm sequentially sets prices to earn revenue and to learn
the underlying demand function simultaneously. A natural heuristic for this
problem, commonly used in practice, is greedy iterative least squares (GILS).
At each time period, GILS estimates the demand as a linear function of the
price by applying least squares to the set of prior prices and realized
demands. Then a price that maximizes the revenue, given the estimated demand
function, is used for the next time period. The performance is measured by the
regret, which is the expected revenue loss from the optimal (oracle) pricing
policy when the demand function is known. Recently, den Boer and Zwart (2014)
and Keskin and Zeevi (2014) demonstrated that GILS is sub-optimal. They
introduced algorithms which integrate forced price dispersion with GILS and
achieve asymptotically optimal performance.
  In this paper, we consider this dynamic pricing problem in a data-rich
environment. In particular, we assume that the firm knows the expected demand
under a particular price from historical data, and in each period, before
setting the price, the firm has access to extra information (demand covariates)
which may be predictive of the demand. We prove that in this setting GILS
achieves asymptotically optimal regret of order $\log(T)$. We also show the
following surprising result: in the original dynamic pricing problem of den
Boer and Zwart (2014) and Keskin and Zeevi (2014), inclusion of any set of
covariates in GILS as potential demand covariates (even though they could carry
no information) would make GILS asymptotically optimal. We validate our results
via extensive numerical simulations on synthetic and real data sets.
Modeling the Contribution of Central Versus Peripheral Vision in Scene, Object, and Face Recognition
Authors: Panqu Wang, Garrison Cottrell
Date: 2016-04-25 00:00:00
Tasks: Face Recognition, Object Recognition, Scene Recognition

It is commonly believed that the central visual field is important for
recognizing objects and faces, and the peripheral region is useful for scene
recognition. However, the relative importance of central versus peripheral
information for object, scene, and face recognition is unclear. In a behavioral
study, Larson and Loschky (2009) investigated this question by measuring the
scene recognition accuracy as a function of visual angle, and demonstrated that
peripheral vision was indeed more useful in recognizing scenes than central
vision. In this work, we modeled and replicated the result of Larson and
Loschky (2009), using deep convolutional neural networks. Having fit the data
for scenes, we used the model to predict future data for large-scale scene
recognition as well as for objects and faces. Our results suggest that the
relative order of importance of using central visual field information is face
recognition>object recognition>scene recognition, and vice-versa for peripheral
information.
G-CNN: an Iterative Grid Based Object Detector
Authors: Mahyar Najibi, Mohammad Rastegari, Larry S. Davis
Date: 2015-12-24 00:00:00
Proceeding: CVPR 2016 6
Tasks: 2k, Object, object-detection, Object Detection

We introduce G-CNN, an object detection technique based on CNNs which works
without proposal algorithms. G-CNN starts with a multi-scale grid of fixed
bounding boxes. We train a regressor to move and scale elements of the grid
towards objects iteratively. G-CNN models the problem of object detection as
finding a path from a fixed grid to boxes tightly surrounding the objects.
G-CNN with around 180 boxes in a multi-scale grid performs comparably to Fast
R-CNN which uses around 2K bounding boxes generated with a proposal technique.
This strategy makes detection faster by removing the object proposal stage as
well as reducing the number of boxes to be processed.
Balancing Appearance and Context in Sketch Interpretation
Authors: Yale Song, Randall Davis, Kaichen Ma, Dana L. Penny
Date: 2016-04-25 00:00:00

We describe a sketch interpretation system that detects and classifies clock
numerals created by subjects taking the Clock Drawing Test, a clinical tool
widely used to screen for cognitive impairments (e.g., dementia). We describe
how it balances appearance and context, and document its performance on some
2,000 drawings (about 24K clock numerals) produced by a wide spectrum of
patients. We calibrate the utility of different forms of context, describing
experiments with Conditional Random Fields trained and tested using a variety
of features. We identify context that contributes to interpreting otherwise
ambiguous or incomprehensible strokes. We describe ST-slices, a novel
representation that enables "unpeeling" the layers of ink that result when
people overwrite, which often produces ink impossible to analyze if only the
final drawing is examined. We characterize when ST-slices work, calibrate their
impact on performance, and consider their breadth of applicability.
Conversational Markers of Constructive Discussions
Authors: Vlad Niculae, Cristian Danescu-Niculescu-Mizil
Date: 2016-04-25 00:00:00
Proceeding: NAACL 2016 6

Group discussions are essential for organizing every aspect of modern life,
from faculty meetings to senate debates, from grant review panels to papal
conclaves. While costly in terms of time and organization effort, group
discussions are commonly seen as a way of reaching better decisions compared to
solutions that do not require coordination between the individuals (e.g.
voting)---through discussion, the sum becomes greater than the parts. However,
this assumption is not irrefutable: anecdotal evidence of wasteful discussions
abounds, and in our own experiments we find that over 30% of discussions are
unproductive.
  We propose a framework for analyzing conversational dynamics in order to
determine whether a given task-oriented discussion is worth having or not. We
exploit conversational patterns reflecting the flow of ideas and the balance
between the participants, as well as their linguistic choices. We apply this
framework to conversations naturally occurring in an online collaborative world
exploration game developed and deployed to support this research. Using this
setting, we show that linguistic cues and conversational patterns extracted
from the first 20 seconds of a team discussion are predictive of whether it
will be a wasteful or a productive one.
Total variation on a tree
Authors: Vladimir Kolmogorov, Thomas Pock, Michal Rolinek
Date: 2015-02-26 00:00:00

We consider the problem of minimizing the continuous valued total variation
subject to different unary terms on trees and propose fast direct algorithms
based on dynamic programming to solve these problems. We treat both the convex
and the non-convex case and derive worst case complexities that are equal or
better than existing methods. We show applications to total variation based 2D
image processing and computer vision problems based on a Lagrangian
decomposition approach. The resulting algorithms are very efficient, offer a
high degree of parallelism and come along with memory requirements which are
only in the order of the number of image pixels.
Attributes for Improved Attributes: A Multi-Task Network for Attribute Classification
Authors: Emily M. Hand, Rama Chellappa
Date: 2016-04-25 00:00:00
Tasks: Activity Recognition, Attribute, Face Verification, Facial Attribute Classification, General Classification

Attributes, or semantic features, have gained popularity in the past few
years in domains ranging from activity recognition in video to face
verification. Improving the accuracy of attribute classifiers is an important
first step in any application which uses these attributes. In most works to
date, attributes have been considered to be independent. However, we know this
not to be the case. Many attributes are very strongly related, such as heavy
makeup and wearing lipstick. We propose to take advantage of attribute
relationships in three ways: by using a multi-task deep convolutional neural
network (MCNN) sharing the lowest layers amongst all attributes, sharing the
higher layers for related attributes, and by building an auxiliary network on
top of the MCNN which utilizes the scores from all attributes to improve the
final classification of each attribute. We demonstrate the effectiveness of our
method by producing results on two challenging publicly available datasets.
Fast nonlinear embeddings via structured matrices
Authors: Krzysztof Choromanski, Francois Fagan
Date: 2016-04-25 00:00:00

We present a new paradigm for speeding up randomized computations of several
frequently used functions in machine learning. In particular, our paradigm can
be applied for improving computations of kernels based on random embeddings.
Above that, the presented framework covers multivariate randomized functions.
As a byproduct, we propose an algorithmic approach that also leads to a
significant reduction of space complexity. Our method is based on careful
recycling of Gaussian vectors into structured matrices that share properties of
fully random matrices. The quality of the proposed structured approach follows
from combinatorial properties of the graphs encoding correlations between rows
of these structured matrices. Our framework covers as special cases already
known structured approaches such as the Fast Johnson-Lindenstrauss Transform,
but is much more general since it can be applied also to highly nonlinear
embeddings. We provide strong concentration results showing the quality of the
presented paradigm.
Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks
Authors: Stefano V. Albrecht, Subramanian Ramamoorthy
Date: 2014-01-30 00:00:00

Dynamic Bayesian networks (DBNs) are a general model for stochastic processes
with partially observed states. Belief filtering in DBNs is the task of
inferring the belief state (i.e. the probability distribution over process
states) based on incomplete and noisy observations. This can be a hard problem
in complex processes with large state spaces. In this article, we explore the
idea of accelerating the filtering task by automatically exploiting causality
in the process. We consider a specific type of causal relation, called
passivity, which pertains to how state variables cause changes in other
variables. We present the Passivity-based Selective Belief Filtering (PSBF)
method, which maintains a factored belief representation and exploits passivity
to perform selective updates over the belief factors. PSBF produces exact
belief states under certain assumptions and approximate belief states
otherwise, where the approximation error is bounded by the degree of
uncertainty in the process. We show empirically, in synthetic processes with
varying sizes and degrees of passivity, that PSBF is faster than several
alternative methods while achieving competitive accuracy. Furthermore, we
demonstrate how passivity occurs naturally in a complex system such as a
multi-robot warehouse, and how PSBF can exploit this to accelerate the
filtering task.
Scalable Gaussian Processes for Supervised Hashing
Authors: Bahadir Ozdemir, Larry S. Davis
Date: 2016-04-25 00:00:00
Tasks: Binary Classification, Gaussian Processes, General Classification, Image Retrieval, Semantic Similarity, Semantic Textual Similarity

We propose a flexible procedure for large-scale image search by hash
functions with kernels. Our method treats binary codes and pairwise semantic
similarity as latent and observed variables, respectively, in a probabilistic
model based on Gaussian processes for binary classification. We present an
efficient inference algorithm with the sparse pseudo-input Gaussian process
(SPGP) model and parallelization. Experiments on three large-scale image
dataset demonstrate the effectiveness of the proposed hashing method, Gaussian
Process Hashing (GPH), for short binary codes and the datasets without
predefined classes in comparison to the state-of-the-art supervised hashing
methods.