pwc-archive/papers-with-abstracts huggingface dataset:
SwiDeN : Convolutional Neural Networks For Depiction Invariant Object Recognition
Authors: Ravi Kiran Sarvadevabhatla, Shiv Surya, Srinivas S. S. Kruthiventi, Venkatesh Babu R
Date: 2016-07-29 00:00:00
Tasks: Depiction Invariant Object Recognition, Object, Object Recognition

Current state of the art object recognition architectures achieve impressive
performance but are typically specialized for a single depictive style (e.g.
photos only, sketches only). In this paper, we present SwiDeN : our
Convolutional Neural Network (CNN) architecture which recognizes objects
regardless of how they are visually depicted (line drawing, realistic shaded
drawing, photograph etc.). In SwiDeN, we utilize a novel `deep' depictive
style-based switching mechanism which appropriately addresses the
depiction-specific and depiction-invariant aspects of the problem. We compare
SwiDeN with alternative architectures and prior work on a 50-category Photo-Art
dataset containing objects depicted in multiple styles. Experimental results
show that SwiDeN outperforms other approaches for the depiction-invariant
object recognition problem.
SEMBED: Semantic Embedding of Egocentric Action Videos
Authors: Michael Wray, Davide Moltisanti, Walterio Mayol-Cuevas, Dima Damen
Date: 2016-07-28 00:00:00
Tasks: General Classification, Object

We present SEMBED, an approach for embedding an egocentric object interaction
video in a semantic-visual graph to estimate the probability distribution over
its potential semantic labels. When object interactions are annotated using
unbounded choice of verbs, we embrace the wealth and ambiguity of these labels
by capturing the semantic relationships as well as the visual similarities over
motion and appearance features. We show how SEMBED can interpret a challenging
dataset of 1225 freely annotated egocentric videos, outperforming SVM
classification by more than 5%.
Cseq2seq: Cyclic Sequence-to-Sequence Learning
Authors: Biao Zhang, Deyi Xiong, Jinsong Su
Date: 2016-07-29 00:00:00
Tasks: Machine Translation, Translation, Word Embeddings

The vanilla sequence-to-sequence learning (seq2seq) reads and encodes a
source sequence into a fixed-length vector only once, suffering from its
insufficiency in modeling structural correspondence between the source and
target sequence. Instead of handling this insufficiency with a linearly
weighted attention mechanism, in this paper, we propose to use a recurrent
neural network (RNN) as an alternative (Cseq2seq-I). During decoding,
Cseq2seq-I cyclically feeds the previous decoding state back to the encoder as
the initial state of the RNN, and reencodes source representations to produce
context vectors. We surprisingly find that the introduced RNN succeeds in
dynamically detecting translationrelated source tokens according to the partial
target sequence. Based on this finding, we further hypothesize that the partial
target sequence can act as a feedback to improve the understanding of the
source sequence. To test this hypothesis, we propose cyclic
sequence-to-sequence learning (Cseq2seq-II) which differs from the seq2seq only
in the reintroduction of previous decoding state into the same encoder. We
further perform parameter sharing on Cseq2seq-II to reduce parameter redundancy
and enhance regularization. In particular, we share the weights of the encoder
and decoder, and two targetside word embeddings, making Cseq2seq-II equivalent
to a single conditional RNN model, with 31% parameters pruned but even better
performance. Cseq2seq-II not only preserves the simplicity of seq2seq but also
yields comparable and promising results on machine translation tasks.
Experiments on Chinese- English and English-German translation show that
Cseq2seq achieves significant and consistent improvements over seq2seq and is
as competitive as the attention-based seq2seq model.
A Review of Features for the Discrimination of Twitter Users: Application to the Prediction of Offline Influence
Authors: Jean-Valère Cossu, Vincent Labatut, Nicolas Dugué
Date: 2015-09-22 00:00:00

Many works related to Twitter aim at characterizing its users in some way:
role on the service (spammers, bots, organizations, etc.), nature of the user
(socio-professional category, age, etc.), topics of interest , and others.
However, for a given user classification problem, it is very difficult to
select a set of appropriate features, because the many features described in
the literature are very heterogeneous, with name overlaps and collisions, and
numerous very close variants. In this article, we review a wide range of such
features. In order to present a clear state-of-the-art description, we unify
their names, definitions and relationships, and we propose a new, neutral,
typology. We then illustrate the interest of our review by applying a selection
of these features to the offline influence detection problem. This task
consists in identifying users which are influential in real-life, based on
their Twitter account and related data. We show that most features deemed
efficient to predict online influence, such as the numbers of retweets and
followers, are not relevant to this problem. However, We propose several
content-based approaches to label Twitter users as Influencers or not. We also
rank them according to a predicted influence level. Our proposals are evaluated
over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art methods.
Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network
Authors: Hanming Zhang, Liang Li, Kai Qiao, Linyuan Wang, Bin Yan, Lei LI, Guoen Hu
Date: 2016-07-29 00:00:00
Tasks: Computed Tomography (CT)

Limited angle problem is a challenging issue in x-ray computed tomography
(CT) field. Iterative reconstruction methods that utilize the additional prior
can suppress artifacts and improve image quality, but unfortunately require
increased computation time. An interesting way is to restrain the artifacts in
the images reconstructed from the practical filtered back projection (FBP)
method. Frikel and Quinto have proved that the streak artifacts in FBP results
could be characterized. It indicates that the artifacts created by FBP method
have specific and similar characteristics in a stationary limited-angle
scanning configuration. Based on this understanding, this work aims at
developing a method to extract and suppress specific artifacts of FBP
reconstructions for limited-angle tomography. A data-driven learning-based
method is proposed based on a deep convolutional neural network. An end-to-end
mapping between the FBP and artifact-free images is learned and the implicit
features involving artifacts will be extracted and suppressed via nonlinear
mapping. The qualitative and quantitative evaluations of experimental results
indicate that the proposed method show a stable and prospective performance on
artifacts reduction and detail recovery for limited angle tomography. The
presented strategy provides a simple and efficient approach for improving image
quality of the reconstruction results from limited projection data.
Video Summarization with Long Short-term Memory
Authors: Ke Zhang, Wei-Lun Chao, Fei Sha, Kristen Grauman
Date: 2016-05-26 00:00:00
Tasks: Domain Adaptation, Structured Prediction, Video Summarization

We propose a novel supervised learning technique for summarizing videos by
automatically selecting keyframes or key subshots. Casting the problem as a
structured prediction problem on sequential data, our main idea is to use Long
Short-Term Memory (LSTM), a special type of recurrent neural networks to model
the variable-range dependencies entailed in the task of video summarization.
Our learning models attain the state-of-the-art results on two benchmark video
datasets. Detailed analysis justifies the design of the models. In particular,
we show that it is crucial to take into consideration the sequential structures
in videos and model them. Besides advances in modeling techniques, we introduce
techniques to address the need of a large number of annotated data for training
complex learning models. There, our main idea is to exploit the existence of
auxiliary annotated video datasets, albeit heterogeneous in visual styles and
contents. Specifically, we show domain adaptation techniques can improve
summarization by reducing the discrepancies in statistical properties across
those datasets.
Semi-supervised evidential label propagation algorithm for graph data
Authors: Kuang Zhou, Arnaud Martin, Quan Pan
Date: 2016-07-29 00:00:00
Tasks: Clustering, Community Detection

In the task of community detection, there often exists some useful prior
information. In this paper, a Semi-supervised clustering approach using a new
Evidential Label Propagation strategy (SELP) is proposed to incorporate the
domain knowledge into the community detection model. The main advantage of SELP
is that it can take limited supervised knowledge to guide the detection
process. The prior information of community labels is expressed in the form of
mass functions initially. Then a new evidential label propagation rule is
adopted to propagate the labels from labeled data to unlabeled ones. The
outliers can be identified to be in a special class. The experimental results
demonstrate the effectiveness of SELP.
Connecting Phrase based Statistical Machine Translation Adaptation
Authors: Rui Wang, Hai Zhao, Bao-liang Lu, Masao Utiyama, Eiichro Sumita
Date: 2016-07-29 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation, Sentence, Translation

Although more additional corpora are now available for Statistical Machine
Translation (SMT), only the ones which belong to the same or similar domains
with the original corpus can indeed enhance SMT performance directly. Most of
the existing adaptation methods focus on sentence selection. In comparison,
phrase is a smaller and more fine grained unit for data selection, therefore we
propose a straightforward and efficient connecting phrase based adaptation
method, which is applied to both bilingual phrase pair and monolingual n-gram
adaptation. The proposed method is evaluated on IWSLT/NIST data sets, and the
results show that phrase based SMT performance are significantly improved (up
to +1.6 in comparison with phrase based SMT baseline system and +0.9 in
comparison with existing methods).
Stochastic Frank-Wolfe Methods for Nonconvex Optimization
Authors: Sashank J. Reddi, Suvrit Sra, Barnabas Poczos, Alex Smola
Date: 2016-07-27 00:00:00

We study Frank-Wolfe methods for nonconvex stochastic and finite-sum
optimization problems. Frank-Wolfe methods (in the convex case) have gained
tremendous recent interest in machine learning and optimization communities due
to their projection-free property and their ability to exploit structured
constraints. However, our understanding of these algorithms in the nonconvex
setting is fairly limited. In this paper, we propose nonconvex stochastic
Frank-Wolfe methods and analyze their convergence properties. For objective
functions that decompose into a finite-sum, we leverage ideas from variance
reduction techniques for convex optimization to obtain new variance reduced
nonconvex Frank-Wolfe methods that have provably faster convergence than the
classical Frank-Wolfe method. Finally, we show that the faster convergence
rates of our variance reduced methods also translate into improved convergence
rates for the stochastic setting.
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
Authors: Ravi Garg, Vijay Kumar BG, Gustavo Carneiro, Ian Reid
Date: 2016-03-16 00:00:00
Tasks: Depth Estimation

A significant weakness of most current deep Convolutional Neural Networks is
the need to train them using vast amounts of manu- ally labelled data. In this
work we propose a unsupervised framework to learn a deep convolutional neural
network for single view depth predic- tion, without requiring a pre-training
stage or annotated ground truth depths. We achieve this by training the network
in a manner analogous to an autoencoder. At training time we consider a pair of
images, source and target, with small, known camera motion between the two such
as a stereo pair. We train the convolutional encoder for the task of predicting
the depth map for the source image. To do so, we explicitly generate an inverse
warp of the target image using the predicted depth and known inter-view
displacement, to reconstruct the source image; the photomet- ric error in the
reconstruction is the reconstruction loss for the encoder. The acquisition of
this training data is considerably simpler than for equivalent systems,
requiring no manual annotation, nor calibration of depth sensor to camera. We
show that our network trained on less than half of the KITTI dataset (without
any further augmentation) gives com- parable performance to that of the state
of art supervised methods for single view depth estimation.
Optimal Parameter Settings for the $(1+(λ, λ))$ Genetic Algorithm
Authors: Benjamin Doerr
Date: 2016-04-04 00:00:00

The $(1+(\lambda,\lambda))$ genetic algorithm is one of the few algorithms
for which a super-constant speed-up through the use of crossover could be
proven. So far, this algorithm has been used with parameters based also on
intuitive considerations. In this work, we rigorously regard the whole
parameter space and show that the asymptotic time complexity proven by Doerr
and Doerr (GECCO 2015) for the intuitive choice is best possible among all
settings for population size, mutation probability, and crossover bias.
Introspective Perception: Learning to Predict Failures in Vision Systems
Authors: Shreyansh Daftry, Sam Zeng, J. Andrew Bagnell, Martial Hebert
Date: 2016-07-28 00:00:00

As robots aspire for long-term autonomous operations in complex dynamic
environments, the ability to reliably take mission-critical decisions in
ambiguous situations becomes critical. This motivates the need to build systems
that have situational awareness to assess how qualified they are at that moment
to make a decision. We call this self-evaluating capability as introspection.
In this paper, we take a small step in this direction and propose a generic
framework for introspective behavior in perception systems. Our goal is to
learn a model to reliably predict failures in a given system, with respect to a
task, directly from input sensor data. We present this in the context of
vision-based autonomous MAV flight in outdoor natural environments, and show
that it effectively handles uncertain situations.
Deep Networks with Stochastic Depth
Authors: Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, Kilian Weinberger
Date: 2016-03-30 00:00:00
Tasks: Image Classification

Very deep convolutional networks with hundreds of layers have led to
significant reductions in error on competitive benchmarks. Although the
unmatched expressiveness of the many layers can be highly desirable at test
time, training very deep networks comes with its own set of challenges. The
gradients can vanish, the forward flow often diminishes, and the training time
can be painfully slow. To address these problems, we propose stochastic depth,
a training procedure that enables the seemingly contradictory setup to train
short networks and use deep networks at test time. We start with very deep
networks but during training, for each mini-batch, randomly drop a subset of
layers and bypass them with the identity function. This simple approach
complements the recent success of residual networks. It reduces training time
substantially and improves the test error significantly on almost all data sets
that we used for evaluation. With stochastic depth we can increase the depth of
residual networks even beyond 1200 layers and still yield meaningful
improvements in test error (4.91% on CIFAR-10).
vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design
Authors: Minsoo Rhu, Natalia Gimelshein, Jason Clemons, Arslan Zulfiqar, Stephen W. Keckler
Date: 2016-02-25 00:00:00
Tasks: BIG-bench Machine Learning, CPU, Efficient Neural Network, GPU

The most widely used machine learning frameworks require users to carefully
tune their memory usage so that the deep neural network (DNN) fits into the
DRAM capacity of a GPU. This restriction hampers a researcher's flexibility to
study different machine learning algorithms, forcing them to either use a less
desirable network architecture or parallelize the processing across multiple
GPUs. We propose a runtime memory manager that virtualizes the memory usage of
DNNs such that both GPU and CPU memory can simultaneously be utilized for
training larger DNNs. Our virtualized DNN (vDNN) reduces the average GPU memory
usage of AlexNet by up to 89%, OverFeat by 91%, and GoogLeNet by 95%, a
significant reduction in memory requirements of DNNs. Similar experiments on
VGG-16, one of the deepest and memory hungry DNNs to date, demonstrate the
memory-efficiency of our proposal. vDNN enables VGG-16 with batch size 256
(requiring 28 GB of memory) to be trained on a single NVIDIA Titan X GPU card
containing 12 GB of memory, with 18% performance loss compared to a
hypothetical, oracular GPU with enough memory to hold the entire DNN.
Sparse Linear Regression via Generalized Orthogonal Least-Squares
Authors: Abolfazl Hashemi, Haris Vikalo
Date: 2016-02-22 00:00:00
Tasks: regression

Sparse linear regression, which entails finding a sparse solution to an
underdetermined system of linear equations, can formally be expressed as an
$l_0$-constrained least-squares problem. The Orthogonal Least-Squares (OLS)
algorithm sequentially selects the features (i.e., columns of the coefficient
matrix) to greedily find an approximate sparse solution. In this paper, a
generalization of Orthogonal Least-Squares which relies on a recursive relation
between the components of the optimal solution to select L features at each
step and solve the resulting overdetermined system of equations is proposed.
Simulation results demonstrate that the generalized OLS algorithm is
computationally efficient and achieves performance superior to that of existing
greedy algorithms broadly used in the literature.
Asymptotic properties of Principal Component Analysis and shrinkage-bias adjustment under the Generalized Spiked Population model
Authors: Rounak Dey, Seunggeun Lee
Date: 2016-07-28 00:00:00

With the development of high-throughput technologies, principal component
analysis (PCA) in the high-dimensional regime is of great interest. Most of the
existing theoretical and methodological results for high-dimensional PCA are
based on the spiked population model in which all the population eigenvalues
are equal except for a few large ones. Due to the presence of local correlation
among features, however, this assumption may not be satisfied in many
real-world datasets. To address this issue, we investigated the asymptotic
behaviors of PCA under the generalized spiked population model. Based on the
theoretical results, we proposed a series of methods for the consistent
estimation of population eigenvalues, angles between the sample and population
eigenvectors, correlation coefficients between the sample and population
principal component (PC) scores, and the shrinkage bias adjustment for the
predicted PC scores. Using numerical experiments and real data examples from
the genetics literature, we showed that our methods can greatly reduce bias and
improve prediction accuracy.
Attribute Learning for Network Intrusion Detection
Authors: Jorge Luis Rivero Pérez, Bernardete Ribeiro
Date: 2016-07-28 00:00:00
Tasks: Attribute, Intrusion Detection, Network Intrusion Detection, Zero-Shot Learning

Network intrusion detection is one of the most visible uses for Big Data
analytics. One of the main problems in this application is the constant rise of
new attacks. This scenario, characterized by the fact that not enough labeled
examples are available for the new classes of attacks is hardly addressed by
traditional machine learning approaches. New findings on the capabilities of
Zero-Shot learning (ZSL) approach makes it an interesting solution for this
problem because it has the ability to classify instances of unseen classes. ZSL
has inherently two stages: the attribute learning and the inference stage. In
this paper we propose a new algorithm for the attribute learning stage of ZSL.
The idea is to learn new values for the attributes based on decision trees
(DT). Our results show that based on the rules extracted from the DT a better
distribution for the attribute values can be found. We also propose an
experimental setup for the evaluation of ZSL on network intrusion detection
(NID).
Limit theorems for eigenvectors of the normalized Laplacian for random graphs
Authors: Minh Tang, Carey E. Priebe
Date: 2016-07-28 00:00:00

We prove a central limit theorem for the components of the eigenvectors
corresponding to the $d$ largest eigenvalues of the normalized Laplacian matrix
of a finite dimensional random dot product graph. As a corollary, we show that
for stochastic blockmodel graphs, the rows of the spectral embedding of the
normalized Laplacian converge to multivariate normals and furthermore the mean
and the covariance matrix of each row are functions of the associated vertex's
block membership. Together with prior results for the eigenvectors of the
adjacency matrix, we then compare, via the Chernoff information between
multivariate normal distributions, how the choice of embedding method impacts
subsequent inference. We demonstrate that neither embedding method dominates
with respect to the inference task of recovering the latent block assignments.
Modeling selectional restrictions in a relational type system
Authors: Erkki Luuk
Date: 2016-07-28 00:00:00
Tasks: Vocal Bursts Type Prediction

Selectional restrictions are semantic constraints on forming certain complex
types in natural language. The paper gives an overview of modeling selectional
restrictions in a relational type system with morphological and syntactic
types. We discuss some foundations of the system and ways of formalizing
selectional restrictions.
  Keywords: type theory, selectional restrictions, syntax, morphology
Connectionist Temporal Modeling for Weakly Supervised Action Labeling
Authors: De-An Huang, Li Fei-Fei, Juan Carlos Niebles
Date: 2016-07-28 00:00:00
Tasks: General Classification

We propose a weakly-supervised framework for action labeling in video, where
only the order of occurring actions is required during training time. The key
challenge is that the per-frame alignments between the input (video) and label
(action) sequences are unknown during training. We address this by introducing
the Extended Connectionist Temporal Classification (ECTC) framework to
efficiently evaluate all possible alignments via dynamic programming and
explicitly enforce their consistency with frame-to-frame visual similarities.
This protects the model from distractions of visually inconsistent or
degenerated alignments without the need of temporal supervision. We further
extend our framework to the semi-supervised case when a few frames are sparsely
annotated in a video. With less than 1% of labeled frames per video, our method
is able to outperform existing semi-supervised approaches and achieve
comparable performance to that of fully supervised approaches.