pwc-archive/papers-with-abstracts huggingface dataset:
A New Relaxation Approach to Normalized Hypergraph Cut
Authors: Cong Xie, Wu-Jun Li, Zhihua Zhang
Date: 2015-11-09 00:00:00
Tasks: Clustering

Normalized graph cut (NGC) has become a popular research topic due to its
wide applications in a large variety of areas like machine learning and very
large scale integration (VLSI) circuit design. Most of traditional NGC methods
are based on pairwise relationships (similarities). However, in real-world
applications relationships among the vertices (objects) may be more complex
than pairwise, which are typically represented as hyperedges in hypergraphs.
Thus, normalized hypergraph cut (NHC) has attracted more and more attention.
Existing NHC methods cannot achieve satisfactory performance in real
applications. In this paper, we propose a novel relaxation approach, which is
called relaxed NHC (RNHC), to solve the NHC problem. Our model is defined as an
optimization problem on the Stiefel manifold. To solve this problem, we resort
to the Cayley transformation to devise a feasible learning algorithm.
Experimental results on a set of large hypergraph benchmarks for clustering and
partitioning in VLSI domain show that RNHC can outperform the state-of-the-art
methods.
Parkinson's disease patient rehabilitation using gaming platforms: lessons learnt
Authors: Ioannis Pachoulakis, Nikolaos Papadopoulos, Cleanthe Spanaki
Date: 2015-11-09 00:00:00

Parkinson's disease (PD) is a progressive neurodegenerative movement disorder
where motor dysfunction gradually increases as the disease progress. In
addition to administering dopaminergic PD-specific drugs, attending
neurologists strongly recommend regular exercise combined with physiotherapy.
However, because of the long-term nature of the disease, patients following
traditional rehabilitation programs may get bored, lose interest and eventually
drop out as a direct result of the repeatability and predictability of the
prescribed exercises. Technology supported opportunities to liven up a daily
exercise schedule have appeared in the form of character-based, virtual reality
games which promote physical training in a non-linear and looser fashion and
provide an experience that varies from one game loop the next. Such
"exergames", a word that results from the amalgamation of the words "exercise"
and "game" challenge patients into performing movements of varying complexity
in a playful and immersive virtual environment. Today's game consoles such as
Nintendo's Wii, Sony PlayStation Eye and Microsoft's Kinect sensor present new
opportunities to infuse motivation and variety to an otherwise mundane
physiotherapy routine. In this paper we present some of these approaches,
discuss their suitability for these PD patients, mainly on the basis of demands
made on balance, agility and gesture precision, and present design principles
that exergame platforms must comply with in order to be suitable for PD
patients.
Batch-normalized Maxout Network in Network
Authors: Jia-Ren Chang, Yong-Sheng Chen
Date: 2015-11-09 00:00:00
Tasks: Image Classification

This paper reports a novel deep architecture referred to as Maxout network In
Network (MIN), which can enhance model discriminability and facilitate the
process of information abstraction within the receptive field. The proposed
network adopts the framework of the recently developed Network In Network
structure, which slides a universal approximator, multilayer perceptron (MLP)
with rectifier units, to exact features. Instead of MLP, we employ maxout MLP
to learn a variety of piecewise linear activation functions and to mediate the
problem of vanishing gradients that can occur when using rectifier units.
Moreover, batch normalization is applied to reduce the saturation of maxout
units by pre-conditioning the model and dropout is applied to prevent
overfitting. Finally, average pooling is used in all pooling layers to
regularize maxout MLP in order to facilitate information abstraction in every
receptive field while tolerating the change of object position. Because average
pooling preserves all features in the local patch, the proposed MIN model can
enforce the suppression of irrelevant information during training. Our
experiments demonstrated the state-of-the-art classification performance when
the MIN model was applied to MNIST, CIFAR-10, and CIFAR-100 datasets and
comparable performance for SVHN dataset.
How far can we go without convolution: Improving fully-connected networks
Authors: Zhouhan Lin, Roland Memisevic, Kishore Konda
Date: 2015-11-09 00:00:00
Tasks: General Classification, Unsupervised Pre-training

We propose ways to improve the performance of fully connected networks. We
found that two approaches in particular have a strong effect on performance:
linear bottleneck layers and unsupervised pre-training using autoencoders
without hidden unit biases. We show how both approaches can be related to
improving gradient flow and reducing sparsity in the network. We show that a
fully connected network can yield approximately 70% classification accuracy on
the permutation-invariant CIFAR-10 task, which is much higher than the current
state-of-the-art. By adding deformations to the training data, the fully
connected network achieves 78% accuracy, which is just 10% short of a decent
convolutional network.
A Century of Portraits: A Visual Historical Record of American High School Yearbooks
Authors: Shiry Ginosar, Kate Rakelly, Sarah Sachs, Brian Yin, Crystal Lee, Philipp Krahenbuhl, Alexei A. Efros
Date: 2015-11-09 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Imagery offers a rich description of our world and communicates a volume and type of information that cannot be captured by text alone. Since the invention of the camera, an ever-increasing number of photographs document our "visual culture" complementing historical texts. But currently, this treasure trove of knowledge can only be analyzed manually by historians, and only at small scale. In this paper we perform automated analysis on a large-scale historical image dataset. Our main contributions are: 1) A publicly-available dataset of 168,055 (37,921 frontal-facing) American high school yearbook portraits. 2) Weakly-supervised data-driven techniques to discover historical visual trends in fashion and identify date-specific visual patterns. 3) A classifier to predict when a portrait was taken, with median error of 4 years for women and 6 for men. 4) A new method for discovering and displaying the visual elements used by the CNN-based date-prediction model to date portraits, finding that they correspond to the tell-tale fashions of each era. Project page can be found at: http://people.eecs.berkeley.edu/~shiry/projects/yearbooks/yearbooks.html .
Thresholding for Top-k Recommendation with Temporal Dynamics
Authors: Lei Tang
Date: 2015-06-06 00:00:00
Tasks: Recommendation Systems

This work focuses on top-k recommendation in domains where underlying data
distribution shifts overtime. We propose to learn a time-dependent bias for
each item over whatever existing recommendation engine. Such a bias learning
process alleviates data sparsity in constructing the engine, and at the same
time captures recent trend shift observed in data. We present an alternating
optimization framework to resolve the bias learning problem, and develop
methods to handle a variety of commonly used recommendation evaluation
criteria, as well as large number of items and users in practice. The proposed
algorithm is examined, both offline and online, using real world data sets
collected from the largest retailer worldwide. Empirical results demonstrate
that the bias learning can almost always boost recommendation performance. We
encourage other practitioners to adopt it as a standard component in
recommender systems where temporal dynamics is a norm.
Sentiment Expression via Emoticons on Social Media
Authors: Hao Wang, Jorge A. Castanon
Date: 2015-11-09 00:00:00
Tasks: Clustering, Sentiment Analysis, Sentiment Classification

Emoticons (e.g., :) and :( ) have been widely used in sentiment analysis and
other NLP tasks as features to ma- chine learning algorithms or as entries of
sentiment lexicons. In this paper, we argue that while emoticons are strong and
common signals of sentiment expression on social media the relationship between
emoticons and sentiment polarity are not always clear. Thus, any algorithm that
deals with sentiment polarity should take emoticons into account but extreme
cau- tion should be exercised in which emoticons to depend on. First, to
demonstrate the prevalence of emoticons on social media, we analyzed the
frequency of emoticons in a large re- cent Twitter data set. Then we carried
out four analyses to examine the relationship between emoticons and sentiment
polarity as well as the contexts in which emoticons are used. The first
analysis surveyed a group of participants for their perceived sentiment
polarity of the most frequent emoticons. The second analysis examined
clustering of words and emoti- cons to better understand the meaning conveyed
by the emoti- cons. The third analysis compared the sentiment polarity of
microblog posts before and after emoticons were removed from the text. The last
analysis tested the hypothesis that removing emoticons from text hurts
sentiment classification by training two machine learning models with and
without emoticons in the text respectively. The results confirms the arguments
that: 1) a few emoticons are strong and reliable signals of sentiment polarity
and one should take advantage of them in any senti- ment analysis; 2) a large
group of the emoticons conveys com- plicated sentiment hence they should be
treated with extreme caution.
Sandwiching the marginal likelihood using bidirectional Monte Carlo
Authors: Roger B. Grosse, Zoubin Ghahramani, Ryan P. Adams
Date: 2015-11-08 00:00:00
Tasks: Clustering

Computing the marginal likelihood (ML) of a model requires marginalizing out
all of the parameters and latent variables, a difficult high-dimensional
summation or integration problem. To make matters worse, it is often hard to
measure the accuracy of one's ML estimates. We present bidirectional Monte
Carlo, a technique for obtaining accurate log-ML estimates on data simulated
from a model. This method obtains stochastic lower bounds on the log-ML using
annealed importance sampling or sequential Monte Carlo, and obtains stochastic
upper bounds by running these same algorithms in reverse starting from an exact
posterior sample. The true value can be sandwiched between these two stochastic
bounds with high probability. Using the ground truth log-ML estimates obtained
from our method, we quantitatively evaluate a wide variety of existing ML
estimators on several latent variable models: clustering, a low rank
approximation, and a binary attributes model. These experiments yield insights
into how to accurately estimate marginal likelihoods.
Speed learning on the fly
Authors: Pierre-Yves Massé, Yann Ollivier
Date: 2015-11-08 00:00:00

The practical performance of online stochastic gradient descent algorithms is
highly dependent on the chosen step size, which must be tediously hand-tuned in
many applications. The same is true for more advanced variants of stochastic
gradients, such as SAGA, SVRG, or AdaGrad. Here we propose to adapt the step
size by performing a gradient descent on the step size itself, viewing the
whole performance of the learning trajectory as a function of step size.
Importantly, this adaptation can be computed online at little cost, without
having to iterate backward passes over the full data.
Fast Randomized Kernel Methods With Statistical Guarantees
Authors: Ahmed El Alaoui, Michael W. Mahoney
Date: 2014-11-02 00:00:00

One approach to improving the running time of kernel-based machine learning
methods is to build a small sketch of the input and use it in lieu of the full
kernel matrix in the machine learning task of interest. Here, we describe a
version of this approach that comes with running time guarantees as well as
improved guarantees on its statistical performance. By extending the notion of
\emph{statistical leverage scores} to the setting of kernel ridge regression,
our main statistical result is to identify an importance sampling distribution
that reduces the size of the sketch (i.e., the required number of columns to be
sampled) to the \emph{effective dimensionality} of the problem. This quantity
is often much smaller than previous bounds that depend on the \emph{maximal
degrees of freedom}. Our main algorithmic result is to present a fast algorithm
to compute approximations to these scores. This algorithm runs in time that is
linear in the number of samples---more precisely, the running time is
$O(np^2)$, where the parameter $p$ depends only on the trace of the kernel
matrix and the regularization parameter---and it can be applied to the matrix
of feature vectors, without having to form the full kernel matrix. This is
obtained via a variant of length-squared sampling that we adapt to the kernel
setting in a way that is of independent interest. Lastly, we provide empirical
results illustrating our theory, and we discuss how this new notion of the
statistical leverage of a data point captures in a fine way the difficulty of
the original statistical learning problem.
Algorithmic Stability for Adaptive Data Analysis
Authors: Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, Jonathan Ullman
Date: 2015-11-08 00:00:00

Adaptivity is an important feature of data analysis---the choice of questions
to ask about a dataset often depends on previous interactions with the same
dataset. However, statistical validity is typically studied in a nonadaptive
model, where all questions are specified before the dataset is drawn. Recent
work by Dwork et al. (STOC, 2015) and Hardt and Ullman (FOCS, 2014) initiated
the formal study of this problem, and gave the first upper and lower bounds on
the achievable generalization error for adaptive data analysis.
  Specifically, suppose there is an unknown distribution $\mathbf{P}$ and a set
of $n$ independent samples $\mathbf{x}$ is drawn from $\mathbf{P}$. We seek an
algorithm that, given $\mathbf{x}$ as input, accurately answers a sequence of
adaptively chosen queries about the unknown distribution $\mathbf{P}$. How many
samples $n$ must we draw from the distribution, as a function of the type of
queries, the number of queries, and the desired level of accuracy?
  In this work we make two new contributions:
  (i) We give upper bounds on the number of samples $n$ that are needed to
answer statistical queries. The bounds improve and simplify the work of Dwork
et al. (STOC, 2015), and have been applied in subsequent work by those authors
(Science, 2015, NIPS, 2015).
  (ii) We prove the first upper bounds on the number of samples required to
answer more general families of queries. These include arbitrary
low-sensitivity queries and an important class of optimization queries.
  As in Dwork et al., our algorithms are based on a connection with algorithmic
stability in the form of differential privacy. We extend their work by giving a
quantitatively optimal, more general, and simpler proof of their main theorem
that stability implies low generalization error. We also study weaker stability
guarantees such as bounded KL divergence and total variation distance.
Towards Structured Deep Neural Network for Automatic Speech Recognition
Authors: Yi-Hsiu Liao, Hung-Yi Lee, Lin-shan Lee
Date: 2015-11-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

In this paper we propose the Structured Deep Neural Network (structured DNN)
as a structured and deep learning framework. This approach can learn to find
the best structured object (such as a label sequence) given a structured input
(such as a vector sequence) by globally considering the mapping relationships
between the structures rather than item by item.
  When automatic speech recognition is viewed as a special case of such a
structured learning problem, where we have the acoustic vector sequence as the
input and the phoneme label sequence as the output, it becomes possible to
comprehensively learn utterance by utterance as a whole, rather than frame by
frame.
  Structured Support Vector Machine (structured SVM) was proposed to perform
ASR with structured learning previously, but limited by the linear nature of
SVM. Here we propose structured DNN to use nonlinear transformations in
multi-layers as a structured and deep learning approach. This approach was
shown to beat structured SVM in preliminary experiments on TIMIT.
Flowing ConvNets for Human Pose Estimation in Videos
Authors: Tomas Pfister, James Charles, Andrew Zisserman
Date: 2015-06-09 00:00:00
Proceeding: ICCV 2015 12
Tasks: Optical Flow Estimation, Pose Estimation

The objective of this work is human pose estimation in videos, where multiple
frames are available. We investigate a ConvNet architecture that is able to
benefit from temporal context by combining information across the multiple
frames using optical flow.
  To this end we propose a network architecture with the following novelties:
(i) a deeper network than previously investigated for regressing heatmaps; (ii)
spatial fusion layers that learn an implicit spatial model; (iii) optical flow
is used to align heatmap predictions from neighbouring frames; and (iv) a final
parametric pooling layer which learns to combine the aligned heatmaps into a
pooled confidence map.
  We show that this architecture outperforms a number of others, including one
that uses optical flow solely at the input layers, one that regresses joint
coordinates directly, and one that predicts heatmaps without spatial fusion.
  The new architecture outperforms the state of the art by a large margin on
three video pose estimation datasets, including the very challenging Poses in
the Wild dataset, and outperforms other deep methods that don't use a graphical
model on the single-image FLIC benchmark (and also Chen & Yuille and Tompson et
al. in the high precision region).
An Egocentric Look at Video Photographer Identity
Authors: Yedid Hoshen, Shmuel Peleg
Date: 2014-11-27 00:00:00
Proceeding: CVPR 2016 6

Egocentric cameras are being worn by an increasing number of users, among
them many security forces worldwide. GoPro cameras already penetrated the mass
market, reporting substantial increase in sales every year. As head-worn
cameras do not capture the photographer, it may seem that the anonymity of the
photographer is preserved even when the video is publicly distributed.
  We show that camera motion, as can be computed from the egocentric video,
provides unique identity information. The photographer can be reliably
recognized from a few seconds of video captured when walking. The proposed
method achieves more than 90% recognition accuracy in cases where the random
success rate is only 3%.
  Applications can include theft prevention by locking the camera when not worn
by its rightful owner. Searching video sharing services (e.g. YouTube) for
egocentric videos shot by a specific photographer may also become possible. An
important message in this paper is that photographers should be aware that
sharing egocentric video will compromise their anonymity, even when their face
is not visible.
Poisson Inverse Problems by the Plug-and-Play scheme
Authors: Arie Rond, Raja Giryes, Michael Elad
Date: 2015-11-08 00:00:00
Tasks: Denoising

The Anscombe transform offers an approximate conversion of a Poisson random
variable into a Gaussian one. This transform is important and appealing, as it
is easy to compute, and becomes handy in various inverse problems with Poisson
noise contamination. Solution to such problems can be done by first applying
the Anscombe transform, then applying a Gaussian-noise-oriented restoration
algorithm of choice, and finally applying an inverse Anscombe transform. The
appeal in this approach is due to the abundance of high-performance restoration
algorithms designed for white additive Gaussian noise (we will refer to these
hereafter as "Gaussian-solvers"). This process is known to work well for high
SNR images, where the Anscombe transform provides a rather accurate
approximation. When the noise level is high, the above path loses much of its
effectiveness, and the common practice is to replace it with a direct treatment
of the Poisson distribution. Naturally, with this we lose the ability to
leverage on vastly available Gaussian-solvers.
  In this work we suggest a novel method for coupling Gaussian denoising
algorithms to Poisson noisy inverse problems, which is based on a general
approach termed "Plug-and-Play". Deploying the Plug-and-Play approach to such
problems leads to an iterative scheme that repeats several key steps: 1) A
convex programming task of simple form that can be easily treated; 2) A
powerful Gaussian denoising algorithm of choice; and 3) A simple update step.
  Such a modular method, just like the Anscombe transform, enables other
developers to plug their own Gaussian denoising algorithms to our scheme in an
easy way. While the proposed method bares some similarity to the Anscombe
operation, it is in fact based on a different mathematical basis, which holds
true for all SNR ranges.
VideoStory Embeddings Recognize Events when Examples are Scarce
Authors: Amirhossein Habibian, Thomas Mensink, Cees G. M. Snoek
Date: 2015-11-08 00:00:00
Tasks: Attribute, Event Detection

This paper aims for event recognition when video examples are scarce or even
completely absent. The key in such a challenging setting is a semantic video
representation. Rather than building the representation from individual
attribute detectors and their annotations, we propose to learn the entire
representation from freely available web videos and their descriptions using an
embedding between video features and term vectors. In our proposed embedding,
which we call VideoStory, the correlations between the terms are utilized to
learn a more effective representation by optimizing a joint objective balancing
descriptiveness and predictability.We show how learning the VideoStory using a
multimodal predictability loss, including appearance, motion and audio
features, results in a better predictable representation. We also propose a
variant of VideoStory to recognize an event in video from just the important
terms in a text query by introducing a term sensitive descriptiveness loss. Our
experiments on three challenging collections of web videos from the NIST
TRECVID Multimedia Event Detection and Columbia Consumer Videos datasets
demonstrate: i) the advantages of VideoStory over representations using
attributes or alternative embeddings, ii) the benefit of fusing video
modalities by an embedding over common strategies, iii) the complementarity of
term sensitive descriptiveness and multimodal predictability for event
recognition without examples. By it abilities to improve predictability upon
any underlying video feature while at the same time maximizing semantic
descriptiveness, VideoStory leads to state-of-the-art accuracy for both few-
and zero-example recognition of events in video.