pwc-archive/papers-with-abstracts huggingface dataset:
Non-negative Matrix Factorization via Archetypal Analysis
Authors: Hamid Javadi, Andrea Montanari
Date: 2017-05-08 00:00:00

Given a collection of data points, non-negative matrix factorization (NMF)
suggests to express them as convex combinations of a small set of `archetypes'
with non-negative entries. This decomposition is unique only if the true
archetypes are non-negative and sufficiently sparse (or the weights are
sufficiently sparse), a regime that is captured by the separability condition
and its generalizations.
  In this paper, we study an approach to NMF that can be traced back to the
work of Cutler and Breiman (1994) and does not require the data to be
separable, while providing a generally unique decomposition. We optimize the
trade-off between two objectives: we minimize the distance of the data points
from the convex envelope of the archetypes (which can be interpreted as an
empirical risk), while minimizing the distance of the archetypes from the
convex envelope of the data (which can be interpreted as a data-dependent
regularization). The archetypal analysis method of (Cutler, Breiman, 1994) is
recovered as the limiting case in which the last term is given infinite weight.
  We introduce a `uniqueness condition' on the data which is necessary for
exactly recovering the archetypes from noiseless data. We prove that, under
uniqueness (plus additional regularity conditions on the geometry of the
archetypes), our estimator is robust. While our approach requires solving a
non-convex optimization problem, we find that standard optimization methods
succeed in finding good solutions both for real and synthetic data.
Convolutional Random Walk Networks for Semantic Image Segmentation
Authors: Gedas Bertasius, Lorenzo Torresani, Stella X. Yu, Jianbo Shi
Date: 2016-05-24 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Segmentation, Scene Labeling, Segmentation, Semantic Segmentation

Most current semantic segmentation methods rely on fully convolutional
networks (FCNs). However, their use of large receptive fields and many pooling
layers cause low spatial resolution inside the deep layers. This leads to
predictions with poor localization around the boundaries. Prior work has
attempted to address this issue by post-processing predictions with CRFs or
MRFs. But such models often fail to capture semantic relationships between
objects, which causes spatially disjoint predictions. To overcome these
problems, recent methods integrated CRFs or MRFs into an FCN framework. The
downside of these new models is that they have much higher complexity than
traditional FCNs, which renders training and testing more challenging.
  In this work we introduce a simple, yet effective Convolutional Random Walk
Network (RWN) that addresses the issues of poor boundary localization and
spatially fragmented predictions with very little increase in model complexity.
Our proposed RWN jointly optimizes the objectives of pixelwise affinity and
semantic segmentation. It combines these two objectives via a novel random walk
layer that enforces consistent spatial grouping in the deep layers of the
network. Our RWN is implemented using standard convolution and matrix
multiplication. This allows an easy integration into existing FCN frameworks
and it enables end-to-end training of the whole network via standard
back-propagation. Our implementation of RWN requires just $131$ additional
parameters compared to the traditional FCNs, and yet it consistently produces
an improvement over the FCNs on semantic segmentation and scene labeling.
Temporal Segment Networks for Action Recognition in Videos
Authors: Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, Luc van Gool
Date: 2017-05-08 00:00:00
Tasks: Action Classification, Action Recognition, Action Recognition In Videos, Micro-Action Recognition, Temporal Action Localization, Video Classification

Deep convolutional networks have achieved great success for image
recognition. However, for action recognition in videos, their advantage over
traditional methods is not so evident. We present a general and flexible
video-level framework for learning action models in videos. This method, called
temporal segment network (TSN), aims to model long-range temporal structures
with a new segment-based sampling and aggregation module. This unique design
enables our TSN to efficiently learn action models by using the whole action
videos. The learned models could be easily adapted for action recognition in
both trimmed and untrimmed videos with simple average pooling and multi-scale
temporal window integration, respectively. We also study a series of good
practices for the instantiation of TSN framework given limited training
samples. Our approach obtains the state-the-of-art performance on four
challenging action recognition benchmarks: HMDB51 (71.0%), UCF101 (94.9%),
THUMOS14 (80.1%), and ActivityNet v1.2 (89.6%). Using the proposed RGB
difference for motion models, our method can still achieve competitive accuracy
on UCF101 (91.0%) while running at 340 FPS. Furthermore, based on the temporal
segment networks, we won the video classification track at the ActivityNet
challenge 2016 among 24 teams, which demonstrates the effectiveness of TSN and
the proposed good practices.
Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization
Authors: Xiudong Wang, Yuantao Gu
Date: 2017-05-08 00:00:00
Tasks: Classification, Computational Efficiency, Dictionary Learning, Face Recognition, General Classification, image-classification, Image Classification, Object Categorization, Scene Classification

This paper addresses image classification through learning a compact and
discriminative dictionary efficiently. Given a structured dictionary with each
atom (columns in the dictionary matrix) related to some label, we propose
cross-label suppression constraint to enlarge the difference among
representations for different classes. Meanwhile, we introduce group
regularization to enforce representations to preserve label properties of
original samples, meaning the representations for the same class are encouraged
to be similar. Upon the cross-label suppression, we don't resort to
frequently-used $\ell_0$-norm or $\ell_1$-norm for coding, and obtain
computational efficiency without losing the discriminative power for
categorization. Moreover, two simple classification schemes are also developed
to take full advantage of the learnt dictionary. Extensive experiments on six
data sets including face recognition, object categorization, scene
classification, texture recognition and sport action categorization are
conducted, and the results show that the proposed approach can outperform lots
of recently presented dictionary algorithms on both recognition accuracy and
computational efficiency.
Algorithms for Learning Sparse Additive Models with Interactions in High Dimensions
Authors: Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner, Andreas Krause
Date: 2016-05-02 00:00:00
Tasks: Additive models, compressed sensing, Vocal Bursts Intensity Prediction

A function $f: \mathbb{R}^d \rightarrow \mathbb{R}$ is a Sparse Additive
Model (SPAM), if it is of the form $f(\mathbf{x}) = \sum_{l \in
\mathcal{S}}\phi_{l}(x_l)$ where $\mathcal{S} \subset [d]$, $|\mathcal{S}| \ll
d$. Assuming $\phi$'s, $\mathcal{S}$ to be unknown, there exists extensive work
for estimating $f$ from its samples. In this work, we consider a generalized
version of SPAMs, that also allows for the presence of a sparse number of
second order interaction terms. For some $\mathcal{S}_1 \subset [d],
\mathcal{S}_2 \subset {[d] \choose 2}$, with $|\mathcal{S}_1| \ll d,
|\mathcal{S}_2| \ll d^2$, the function $f$ is now assumed to be of the form:
$\sum_{p \in \mathcal{S}_1}\phi_{p} (x_p) + \sum_{(l,l^{\prime}) \in
\mathcal{S}_2}\phi_{(l,l^{\prime})} (x_l,x_{l^{\prime}})$. Assuming we have the
freedom to query $f$ anywhere in its domain, we derive efficient algorithms
that provably recover $\mathcal{S}_1,\mathcal{S}_2$ with finite sample bounds.
Our analysis covers the noiseless setting where exact samples of $f$ are
obtained, and also extends to the noisy setting where the queries are corrupted
with noise. For the noisy setting in particular, we consider two noise models
namely: i.i.d Gaussian noise and arbitrary but bounded noise. Our main methods
for identification of $\mathcal{S}_2$ essentially rely on estimation of sparse
Hessian matrices, for which we provide two novel compressed sensing based
schemes. Once $\mathcal{S}_1, \mathcal{S}_2$ are known, we show how the
individual components $\phi_p$, $\phi_{(l,l^{\prime})}$ can be estimated via
additional queries of $f$, with uniform error bounds. Lastly, we provide
simulation results on synthetic data that validate our theoretical findings.
Ontology-Aware Token Embeddings for Prepositional Phrase Attachment
Authors: Pradeep Dasigi, Waleed Ammar, Chris Dyer, Eduard Hovy
Date: 2017-05-08 00:00:00
Proceeding: ACL 2017 7
Tasks: Prepositional Phrase Attachment, Word Embeddings

Type-level word embeddings use the same set of parameters to represent all
instances of a word regardless of its context, ignoring the inherent lexical
ambiguity in language. Instead, we embed semantic concepts (or synsets) as
defined in WordNet and represent a word token in a particular context by
estimating a distribution over relevant semantic concepts. We use the new,
context-sensitive embeddings in a model for predicting prepositional phrase(PP)
attachments and jointly learn the concept embeddings and model parameters. We
show that using context-sensitive embeddings improves the accuracy of the PP
attachment model by 5.4% absolute points, which amounts to a 34.4% relative
reduction in errors.
A Distributed Learning Dynamics in Social Groups
Authors: L. Elisa Celis, Peter M. Krafft, Nisheeth K. Vishnoi
Date: 2017-05-08 00:00:00

We study a distributed learning process observed in human groups and other
social animals. This learning process appears in settings in which each
individual in a group is trying to decide over time, in a distributed manner,
which option to select among a shared set of options. Specifically, we consider
a stochastic dynamics in a group in which every individual selects an option in
the following two-step process: (1) select a random individual and observe the
option that individual chose in the previous time step, and (2) adopt that
option if its stochastic quality was good at that time step. Various
instantiations of such distributed learning appear in nature, and have also
been studied in the social science literature. From the perspective of an
individual, an attractive feature of this learning process is that it is a
simple heuristic that requires extremely limited computational capacities. But
what does it mean for the group -- could such a simple, distributed and
essentially memoryless process lead the group as a whole to perform optimally?
We show that the answer to this question is yes -- this distributed learning is
highly effective at identifying the best option and is close to optimal for the
group overall. Our analysis also gives quantitative bounds that show fast
convergence of these stochastic dynamics. Prior to our work the only
theoretical work related to such learning dynamics has been either in
deterministic special cases or in the asymptotic setting. Finally, we observe
that our infinite population dynamics is a stochastic variant of the classic
multiplicative weights update (MWU) method. Consequently, we arrive at the
following interesting converse: the learning dynamics on a finite population
considered here can be viewed as a novel distributed and low-memory
implementation of the classic MWU method.
Machine Learning with World Knowledge: The Position and Survey
Authors: Yangqiu Song, Dan Roth
Date: 2017-05-08 00:00:00
Tasks: BIG-bench Machine Learning, Information Retrieval, Position, Retrieval, Survey, World Knowledge

Machine learning has become pervasive in multiple domains, impacting a wide
variety of applications, such as knowledge discovery and data mining, natural
language processing, information retrieval, computer vision, social and health
informatics, ubiquitous computing, etc. Two essential problems of machine
learning are how to generate features and how to acquire labels for machines to
learn. Particularly, labeling large amount of data for each domain-specific
problem can be very time consuming and costly. It has become a key obstacle in
making learning protocols realistic in applications. In this paper, we will
discuss how to use the existing general-purpose world knowledge to enhance
machine learning processes, by enriching the features or reducing the labeling
work. We start from the comparison of world knowledge with domain-specific
knowledge, and then introduce three key problems in using world knowledge in
learning processes, i.e., explicit and implicit feature representation,
inference for knowledge linking and disambiguation, and learning with direct or
indirect supervision. Finally we discuss the future directions of this research
topic.
Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning
Authors: Vlad Firoiu, William F. Whitney, Joshua B. Tenenbaum
Date: 2017-02-21 00:00:00
Tasks: Atari Games, Board Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

There has been a recent explosion in the capabilities of game-playing
artificial intelligence. Many classes of RL tasks, from Atari games to motor
control to board games, are now solvable by fairly generic algorithms, based on
deep learning, that learn to play from experience with minimal knowledge of the
specific domain of interest. In this work, we will investigate the performance
of these methods on Super Smash Bros. Melee (SSBM), a popular console fighting
game. The SSBM environment has complex dynamics and partial observability,
making it challenging for human and machine alike. The multi-player aspect
poses an additional challenge, as the vast majority of recent advances in RL
have focused on single-agent environments. Nonetheless, we will show that it is
possible to train agents that are competitive against and even surpass human
professionals, a new result for the multi-player video game setting.
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression
Authors: Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Li Chen, Michael E. Kounavis, Duen Horng Chau
Date: 2017-05-08 00:00:00

Deep neural networks (DNNs) have achieved great success in solving a variety
of machine learning (ML) problems, especially in the domain of image
recognition. However, recent research showed that DNNs can be highly vulnerable
to adversarially generated instances, which look seemingly normal to human
observers, but completely confuse DNNs. These adversarial samples are crafted
by adding small perturbations to normal, benign images. Such perturbations,
while imperceptible to the human eye, are picked up by DNNs and cause them to
misclassify the manipulated instances with high confidence. In this work, we
explore and demonstrate how systematic JPEG compression can work as an
effective pre-processing step in the classification pipeline to counter
adversarial attacks and dramatically reduce their effects (e.g., Fast Gradient
Sign Method, DeepFool). An important component of JPEG compression is its
ability to remove high frequency signal components, inside square blocks of an
image. Such an operation is equivalent to selective blurring of the image,
helping remove additive perturbations. Further, we propose an ensemble-based
technique that can be constructed quickly from a given well-performing DNN, and
empirically show how such an ensemble that leverages JPEG compression can
protect a model from multiple types of adversarial attacks, without requiring
knowledge about the model.
Multi Resolution LSTM For Long Term Prediction In Neural Activity Video
Authors: Yilin Song, Jonathan Viventi, Yao Wang
Date: 2017-05-08 00:00:00
Tasks: Decoder, Prediction, Video Prediction

Epileptic seizures are caused by abnormal, overly syn- chronized, electrical
activity in the brain. The abnor- mal electrical activity manifests as waves,
propagating across the brain. Accurate prediction of the propagation velocity
and direction of these waves could enable real- time responsive brain
stimulation to suppress or prevent the seizures entirely. However, this problem
is very chal- lenging because the algorithm must be able to predict the neural
signals in a sufficiently long time horizon to allow enough time for medical
intervention. We consider how to accomplish long term prediction using a LSTM
network. To alleviate the vanishing gradient problem, we propose two
encoder-decoder-predictor structures, both using multi-resolution
representation. The novel LSTM structure with multi-resolution layers could
significantly outperform the single-resolution benchmark with similar number of
parameters. To overcome the blurring effect associated with video prediction in
the pixel domain using standard mean square error (MSE) loss, we use energy-
based adversarial training to improve the long-term pre- diction. We
demonstrate and analyze how a discriminative model with an encoder-decoder
structure using 3D CNN model improves long term prediction.
Geometry and Dynamics for Markov Chain Monte Carlo
Authors: Alessandro Barp, Francois-Xavier Briol, Anthony D. Kennedy, Mark Girolami
Date: 2017-05-08 00:00:00

Markov Chain Monte Carlo methods have revolutionised mathematical computation
and enabled statistical inference within many previously intractable models. In
this context, Hamiltonian dynamics have been proposed as an efficient way of
building chains which can explore probability densities efficiently. The method
emerges from physics and geometry and these links have been extensively studied
by a series of authors through the last thirty years. However, there is
currently a gap between the intuitions and knowledge of users of the
methodology and our deep understanding of these theoretical foundations. The
aim of this review is to provide a comprehensive introduction to the geometric
tools used in Hamiltonian Monte Carlo at a level accessible to statisticians,
machine learners and other users of the methodology with only a basic
understanding of Monte Carlo methods. This will be complemented with some
discussion of the most recent advances in the field which we believe will
become increasingly relevant to applied scientists.
Video Processing for Barycenter Trajectory Identification in Diving
Authors: Stefano Frassinelli, Alessandro Niccolai, Riccardo E. Zich
Date: 2017-05-08 00:00:00

The aim of this paper is to show a procedure for identify the barycentre of a
diver by means of video processing. This procedure is aimed to introduce
quantitative analysis tools and diving performance measurement and therefore in
diving training. Sport performance analysis is a trend that is growing
exponentially for all level athletes: it has been applied extensively in some
sports such as cycling. Sport performance analysis has been applied mainly for
high level athletes; in order to be used also for middle or low level athletes
the proposed technique has to be flexible and low cost. Video processing is
suitable to fulfil both these requirements. In diving, the first analysis that
has to be done is the barycentre trajectory tracking.
People Counting in Crowded and Outdoor Scenes using a Hybrid Multi-Camera Approach
Authors: Fabio Dittrich, Luiz E. S. de Oliveira, Alceu S. Britto Jr., Alessandro L. Koerich
Date: 2017-04-02 00:00:00

This paper presents two novel approaches for people counting in crowded and
open environments that combine the information gathered by multiple views.
Multiple camera are used to expand the field of view as well as to mitigate the
problem of occlusion that commonly affects the performance of counting methods
using single cameras. The first approach is regarded as a direct approach and
it attempts to segment and count each individual in the crowd. For such an aim,
two head detectors trained with head images are employed: one based on support
vector machines and another based on Adaboost perceptron. The second approach,
regarded as an indirect approach employs learning algorithms and statistical
analysis on the whole crowd to achieve counting. For such an aim, corner points
are extracted from groups of people in a foreground image and computed by a
learning algorithm which estimates the number of people in the scene. Both
approaches count the number of people on the scene and not only on a given
image or video frame of the scene. The experimental results obtained on the
benchmark PETS2009 video dataset show that proposed indirect method surpasses
other methods with improvements of up to 46.7% and provides accurate counting
results for the crowded scenes. On the other hand, the direct method shows high
error rates due to the fact that the latter has much more complex problems to
solve, such as segmentation of heads.
Block-Parallel IDA* for GPUs (Extended Manuscript)
Authors: Satoru Horie, Alex Fukunaga
Date: 2017-05-08 00:00:00
Tasks: GPU

We investigate GPU-based parallelization of Iterative-Deepening A* (IDA*). We
show that straightforward thread-based parallelization techniques which were
previously proposed for massively parallel SIMD processors perform poorly due
to warp divergence and load imbalance. We propose Block-Parallel IDA* (BPIDA*),
which assigns the search of a subtree to a block (a group of threads with
access to fast shared memory) rather than a thread. On the 15-puzzle, BPIDA* on
a NVIDIA GRID K520 with 1536 CUDA cores achieves a speedup of 4.98 compared to
a highly optimized sequential IDA* implementation on a Xeon E5-2670 core.
Face Recognition Machine Vision System Using Eigenfaces
Authors: Fares Jalled
Date: 2017-05-08 00:00:00
Tasks: Face Recognition, TAG

Face Recognition is a common problem in Machine Learning. This technology has
already been widely used in our lives. For example, Facebook can automatically
tag people's faces in images, and also some mobile devices use face recognition
to protect private security. Face images comes with different background,
variant illumination, different facial expression and occlusion. There are a
large number of approaches for the face recognition. Different approaches for
face recognition have been experimented with specific databases which consist
of single type, format and composition of image. Doing so, these approaches
don't suit with different face databases. One of the basic face recognition
techniques is eigenface which is quite simple, efficient, and yields generally
good results in controlled circumstances. So, this paper presents an
experimental performance comparison of face recognition using Principal
Component Analysis (PCA) and Normalized Principal Component Analysis (NPCA).
The experiments are carried out on the ORL (ATT) and Indian face database (IFD)
which contain variability in expression, pose, and facial details. The results
obtained for the two methods have been compared by varying the number of
training images. MATLAB is used for implementing algorithms also.