pwc-archive/papers-with-abstracts huggingface dataset:
A Powerful Generative Model Using Random Weights for the Deep Image Representation
Authors: Kun He, Yan Wang, John Hopcroft
Date: 2016-06-15 00:00:00
Proceeding: NeurIPS 2016 12

To what extent is the success of deep visualization due to the training?
Could we do deep visualization using untrained, random weight networks? To
address this issue, we explore new and powerful generative models for three
popular deep visualization tasks using untrained, random weight convolutional
neural networks. First we invert representations in feature spaces and
reconstruct images from white noise inputs. The reconstruction quality is
statistically higher than that of the same method applied on well trained
networks with the same architecture. Next we synthesize textures using scaled
correlations of representations in multiple layers and our results are almost
indistinguishable with the original natural texture and the synthesized
textures based on the trained network. Third, by recasting the content of an
image in the style of various artworks, we create artistic images with high
perceptual quality, highly competitive to the prior work of Gatys et al. on
pretrained networks. To our knowledge this is the first demonstration of image
representations using untrained deep neural networks. Our work provides a new
and fascinating tool to study the representation of deep network architecture
and sheds light on new understandings on deep visualization.
Auxiliary Deep Generative Models
Authors: Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, Ole Winther
Date: 2016-02-17 00:00:00

Deep generative models parameterized by neural networks have recently
achieved state-of-the-art performance in unsupervised and semi-supervised
learning. We extend deep generative models with auxiliary variables which
improves the variational approximation. The auxiliary variables leave the
generative model unchanged but make the variational distribution more
expressive. Inspired by the structure of the auxiliary variable we also propose
a model with two stochastic layers and skip connections. Our findings suggest
that more expressive and properly specified deep generative models converge
faster with better results. We show state-of-the-art performance within
semi-supervised learning on MNIST, SVHN and NORB datasets.
Pruning Random Forests for Prediction on a Budget
Authors: Feng Nan, Joseph Wang, Venkatesh Saligrama
Date: 2016-06-16 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Combinatorial Optimization, Prediction

We propose to prune a random forest (RF) for resource-constrained prediction.
We first construct a RF and then prune it to optimize expected feature cost &
accuracy. We pose pruning RFs as a novel 0-1 integer program with linear
constraints that encourages feature re-use. We establish total unimodularity of
the constraint set to prove that the corresponding LP relaxation solves the
original integer program. We then exploit connections to combinatorial
optimization and develop an efficient primal-dual algorithm, scalable to large
datasets. In contrast to our bottom-up approach, which benefits from good RF
initialization, conventional methods are top-down acquiring features based on
their utility value and is generally intractable, requiring heuristics.
Empirically, our pruning algorithm outperforms existing state-of-the-art
resource-constrained algorithms.
A Size-Free CLT for Poisson Multinomials and its Applications
Authors: Constantinos Daskalakis, Anindya De, Gautam Kamath, Christos Tzamos
Date: 2015-11-11 00:00:00

An $(n,k)$-Poisson Multinomial Distribution (PMD) is the distribution of the
sum of $n$ independent random vectors supported on the set ${\cal
B}_k=\{e_1,\ldots,e_k\}$ of standard basis vectors in $\mathbb{R}^k$. We show
that any $(n,k)$-PMD is ${\rm poly}\left({k\over \sigma}\right)$-close in total
variation distance to the (appropriately discretized) multi-dimensional
Gaussian with the same first two moments, removing the dependence on $n$ from
the Central Limit Theorem of Valiant and Valiant. Interestingly, our CLT is
obtained by bootstrapping the Valiant-Valiant CLT itself through the structural
characterization of PMDs shown in recent work by Daskalakis, Kamath, and
Tzamos. In turn, our stronger CLT can be leveraged to obtain an efficient PTAS
for approximate Nash equilibria in anonymous games, significantly improving the
state of the art, and matching qualitatively the running time dependence on $n$
and $1/\varepsilon$ of the best known algorithm for two-strategy anonymous
games. Our new CLT also enables the construction of covers for the set of
$(n,k)$-PMDs, which are proper and whose size is shown to be essentially
optimal. Our cover construction combines our CLT with the Shapley-Folkman
theorem and recent sparsification results for Laplacian matrices by Batson,
Spielman, and Srivastava. Our cover size lower bound is based on an algebraic
geometric construction. Finally, leveraging the structural properties of the
Fourier spectrum of PMDs we show that these distributions can be learned from
$O_k(1/\varepsilon^2)$ samples in ${\rm poly}_k(1/\varepsilon)$-time, removing
the quasi-polynomial dependence of the running time on $1/\varepsilon$ from the
algorithm of Daskalakis, Kamath, and Tzamos.
MARLow: A Joint Multiplanar Autoregressive and Low-Rank Approach for Image Completion
Authors: Mading Li, Jiaying Liu, Zhiwei Xiong, Xiaoyan Sun, Zongming Guo
Date: 2016-05-03 00:00:00

In this paper, we propose a novel multiplanar autoregressive (AR) model to
exploit the correlation in cross-dimensional planes of a similar patch group
collected in an image, which has long been neglected by previous AR models. On
that basis, we then present a joint multiplanar AR and low-rank based approach
(MARLow) for image completion from random sampling, which exploits the nonlocal
self-similarity within natural images more effectively. Specifically, the
multiplanar AR model constraints the local stationarity in different
cross-sections of the patch group, while the low-rank minimization captures the
intrinsic coherence of nonlocal patches. The proposed approach can be readily
extended to multichannel images (e.g. color images), by simultaneously
considering the correlation in different channels. Experimental results
demonstrate that the proposed approach significantly outperforms
state-of-the-art methods, even if the pixel missing rate is as high as 90%.
Zero-Shot Hashing via Transferring Supervised Knowledge
Authors: Yang Yang, Wei-Lun Chen, Yadan Luo, Fumin Shen, Jie Shao, Heng Tao Shen
Date: 2016-06-16 00:00:00
Tasks: Image Retrieval, Retrieval, Zero-shot Image Retrieval

Hashing has shown its efficiency and effectiveness in facilitating
large-scale multimedia applications. Supervised knowledge e.g. semantic labels
or pair-wise relationship) associated to data is capable of significantly
improving the quality of hash codes and hash functions. However, confronted
with the rapid growth of newly-emerging concepts and multimedia data on the
Web, existing supervised hashing approaches may easily suffer from the scarcity
and validity of supervised information due to the expensive cost of manual
labelling. In this paper, we propose a novel hashing scheme, termed
\emph{zero-shot hashing} (ZSH), which compresses images of "unseen" categories
to binary codes with hash functions learned from limited training data of
"seen" categories. Specifically, we project independent data labels i.e.
0/1-form label vectors) into semantic embedding space, where semantic
relationships among all the labels can be precisely characterized and thus seen
supervised knowledge can be transferred to unseen classes. Moreover, in order
to cope with the semantic shift problem, we rotate the embedded space to more
suitably align the embedded semantics with the low-level visual feature space,
thereby alleviating the influence of semantic gap. In the meantime, to exert
positive effects on learning high-quality hash functions, we further propose to
preserve local structural property and discrete nature in binary codes.
Besides, we develop an efficient alternating algorithm to solve the ZSH model.
Extensive experiments conducted on various real-life datasets show the superior
zero-shot image retrieval performance of ZSH as compared to several
state-of-the-art hashing methods.
Deep Structured Energy Based Models for Anomaly Detection
Authors: Shuangfei Zhai, Yu Cheng, Weining Lu, Zhongfei Zhang
Date: 2016-05-25 00:00:00
Tasks: Anomaly Detection

In this paper, we attack the anomaly detection problem by directly modeling
the data distribution with deep architectures. We propose deep structured
energy based models (DSEBMs), where the energy function is the output of a
deterministic deep neural network with structure. We develop novel model
architectures to integrate EBMs with different types of data such as static
data, sequential data, and spatial data, and apply appropriate model
architectures to adapt to the data structure. Our training algorithm is built
upon the recent development of score matching \cite{sm}, which connects an EBM
with a regularized autoencoder, eliminating the need for complicated sampling
method. Statistically sound decision criterion can be derived for anomaly
detection purpose from the perspective of the energy landscape of the data
distribution. We investigate two decision criteria for performing anomaly
detection: the energy score and the reconstruction error. Extensive empirical
studies on benchmark tasks demonstrate that our proposed model consistently
matches or outperforms all the competing methods.
The Indian Spontaneous Expression Database for Emotion Recognition
Authors: S. L. Happy, Priyadarshi Patnaik, Aurobinda Routray, Rajlakshmi Guha
Date: 2015-12-03 00:00:00
Tasks: Emotion Recognition, Facial Expression Recognition, Facial Expression Recognition (FER)

Automatic recognition of spontaneous facial expressions is a major challenge
in the field of affective computing. Head rotation, face pose, illumination
variation, occlusion etc. are the attributes that increase the complexity of
recognition of spontaneous expressions in practical applications. Effective
recognition of expressions depends significantly on the quality of the database
used. Most well-known facial expression databases consist of posed expressions.
However, currently there is a huge demand for spontaneous expression databases
for the pragmatic implementation of the facial expression recognition
algorithms. In this paper, we propose and establish a new facial expression
database containing spontaneous expressions of both male and female
participants of Indian origin. The database consists of 428 segmented video
clips of the spontaneous facial expressions of 50 participants. In our
experiment, emotions were induced among the participants by using emotional
videos and simultaneously their self-ratings were collected for each
experienced emotion. Facial expression clips were annotated carefully by four
trained decoders, which were further validated by the nature of stimuli used
and self-report of emotions. An extensive analysis was carried out on the
database using several machine learning algorithms and the results are provided
for future reference. Such a spontaneous database will help in the development
and validation of algorithms for recognition of spontaneous expressions.
Improving Power Generation Efficiency using Deep Neural Networks
Authors: Stefan Hosein, Patrick Hosein
Date: 2016-06-16 00:00:00
Tasks: Load Forecasting

Recently there has been significant research on power generation,
distribution and transmission efficiency especially in the case of renewable
resources. The main objective is reduction of energy losses and this requires
improvements on data acquisition and analysis. In this paper we address these
concerns by using consumers' electrical smart meter readings to estimate
network loading and this information can then be used for better capacity
planning. We compare Deep Neural Network (DNN) methods with traditional methods
for load forecasting. Our results indicate that DNN methods outperform most
traditional methods. This comes at the cost of additional computational
complexity but this can be addressed with the use of cloud resources. We also
illustrate how these results can be used to better support dynamic pricing.
Automatic Pronunciation Generation by Utilizing a Semi-supervised Deep Neural Networks
Authors: Naoya Takahashi, Tofigh Naghibi, Beat Pfister
Date: 2016-06-15 00:00:00
Tasks: speech-recognition, Speech Recognition

Phonemic or phonetic sub-word units are the most commonly used atomic
elements to represent speech signals in modern ASRs. However they are not the
optimal choice due to several reasons such as: large amount of effort required
to handcraft a pronunciation dictionary, pronunciation variations, human
mistakes and under-resourced dialects and languages. Here, we propose a
data-driven pronunciation estimation and acoustic modeling method which only
takes the orthographic transcription to jointly estimate a set of sub-word
units and a reliable dictionary. Experimental results show that the proposed
method which is based on semi-supervised training of a deep neural network
largely outperforms phoneme based continuous speech recognition on the TIMIT
dataset.
Toward Efficient Task Assignment and Motion Planning for Large Scale Underwater Mission
Authors: Somaiyeh Mahmoud. Zadeh, David MW Powers, Karl Sammut, Amirmehdi Yazdani
Date: 2016-04-17 00:00:00
Tasks: Heuristic Search, Management, Motion Planning

An Autonomous Underwater Vehicle (AUV) needs to acquire a certain degree of
autonomy for any particular underwater mission to fulfill the mission
objectives successfully and ensure its safety in all stages of the mission in a
large scale operating filed. In this paper, a novel combinatorial
conflict-free-task assignment strategy consisting an interactive engagement of
a local path planner and an adaptive global route planner, is introduced. The
method is established upon the heuristic search potency of the Particle Swarm
Optimisation (PSO) algorithm to address the discrete nature of routing-task
assignment approach and the complexity of NP-hard path planning problem. The
proposed hybrid method is highly efficient for having a reactive guidance
framework that guarantees successful completion of missions specifically in
cluttered environments. To examine the performance of the method in a context
of mission productivity, mission time management and vehicle safety, a series
of simulation studies are undertaken. The results of simulations declare that
the proposed method is reliable and robust, particularly in dealing with
uncertainties, and it can significantly enhance the level of vehicle's autonomy
by relying on its reactive nature and capability of providing fast feasible
solutions.
Joint Data Compression and MAC Protocol Design for Smartgrids with Renewable Energy
Authors: Le Thanh Tan, Long Bao Le
Date: 2016-06-15 00:00:00
Tasks: compressed sensing, Data Compression

In this paper, we consider the joint design of data compression and
802.15.4-based medium access control (MAC) protocol for smartgrids with
renewable energy. We study the setting where a number of nodes, each of which
comprises electricity load and/or renewable sources, report periodically their
injected powers to a data concentrator. Our design exploits the correlation of
the reported data in both time and space to efficiently design the data
compression using the compressed sensing (CS) technique and theMAC protocol so
that the reported data can be recovered reliably within minimum reporting time.
Specifically, we perform the following design tasks: i) we employ the
two-dimensional (2D) CS technique to compress the reported data in the
distributed manner; ii) we propose to adapt the 802.15.4 MAC protocol frame
structure to enable efficient data transmission and reliable data
reconstruction; and iii) we develop an analytical model based on which we can
obtain efficient MAC parameter configuration to minimize the reporting delay.
Finally, numerical results are presented to demonstrate the effectiveness of
our proposed framework compared to existing solutions.
A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets
Authors: Ivan Lillo, Juan Carlos Niebles, Alvaro Soto
Date: 2016-06-15 00:00:00
Proceeding: CVPR 2016 6
Tasks: Action Recognition, Action Understanding, Pose Estimation, Temporal Action Localization

In this paper, we introduce a new hierarchical model for human action
recognition using body joint locations. Our model can categorize complex
actions in videos, and perform spatio-temporal annotations of the atomic
actions that compose the complex action being performed.That is, for each
atomic action, the model generates temporal action annotations by estimating
its starting and ending times, as well as, spatial annotations by inferring the
human body parts that are involved in executing the action. our model includes
three key novel properties: (i) it can be trained with no spatial supervision,
as it can automatically discover active body parts from temporal action
annotations only; (ii) it jointly learns flexible representations for motion
poselets and actionlets that encode the visual variability of body parts and
atomic actions; (iii) a mechanism to discard idle or non-informative body parts
which increases its robustness to common pose estimation errors. We evaluate
the performance of our method using multiple action recognition benchmarks. Our
model consistently outperforms baselines and state-of-the-art action
recognition methods.
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning
Authors: Aryan Mokhtari, Alec Koppel, Alejandro Ribeiro
Date: 2016-06-15 00:00:00
Tasks: image-classification, Image Classification

We consider learning problems over training sets in which both, the number of
training examples and the dimension of the feature vectors, are large. To solve
these problems we propose the random parallel stochastic algorithm (RAPSA). We
call the algorithm random parallel because it utilizes multiple parallel
processors to operate on a randomly chosen subset of blocks of the feature
vector. We call the algorithm stochastic because processors choose training
subsets uniformly at random. Algorithms that are parallel in either of these
dimensions exist, but RAPSA is the first attempt at a methodology that is
parallel in both the selection of blocks and the selection of elements of the
training set. In RAPSA, processors utilize the randomly chosen functions to
compute the stochastic gradient component associated with a randomly chosen
block. The technical contribution of this paper is to show that this minimally
coordinated algorithm converges to the optimal classifier when the training
objective is convex. Moreover, we present an accelerated version of RAPSA
(ARAPSA) that incorporates the objective function curvature information by
premultiplying the descent direction by a Hessian approximation matrix. We
further extend the results for asynchronous settings and show that if the
processors perform their updates without any coordination the algorithms are
still convergent to the optimal argument. RAPSA and its extensions are then
numerically evaluated on a linear estimation problem and a binary image
classification task using the MNIST handwritten digit dataset.
Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach
Authors: Yanwei Cui, Laetitia Chapel, Sébastien Lefèvre
Date: 2016-06-15 00:00:00
Tasks: Classification Of Hyperspectral Images, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Nowadays, hyperspectral image classification widely copes with spatial
information to improve accuracy. One of the most popular way to integrate such
information is to extract hierarchical features from a multiscale segmentation.
In the classification context, the extracted features are commonly concatenated
into a long vector (also called stacked vector), on which is applied a
conventional vector-based machine learning technique (e.g. SVM with Gaussian
kernel). In this paper, we rather propose to use a sequence structured kernel:
the spectrum kernel. We show that the conventional stacked vector-based kernel
is actually a special case of this kernel. Experiments conducted on various
publicly available hyperspectral datasets illustrate the improvement of the
proposed kernel w.r.t. conventional ones using the same hierarchical spatial
features.
The Edit Distance Transducer in Action: The University of Cambridge English-German System at WMT16
Authors: Felix Stahlberg, Eva Hasler, Bill Byrne
Date: 2016-06-15 00:00:00
Proceeding: WS 2016 8
Tasks: Machine Translation, NMT, Translation

This paper presents the University of Cambridge submission to WMT16.
Motivated by the complementary nature of syntactical machine translation and
neural machine translation (NMT), we exploit the synergies of Hiero and NMT in
different combination schemes. Starting out with a simple neural lattice
rescoring approach, we show that the Hiero lattices are often too narrow for
NMT ensembles. Therefore, instead of a hard restriction of the NMT search space
to the lattice, we propose to loosely couple NMT and Hiero by composition with
a modified version of the edit distance transducer. The loose combination
outperforms lattice rescoring, especially when using multiple NMT systems in an
ensemble.
Assessing Human Error Against a Benchmark of Perfection
Authors: Ashton Anderson, Jon Kleinberg, Sendhil Mullainathan
Date: 2016-06-15 00:00:00

An increasing number of domains are providing us with detailed trace data on
human decisions in settings where we can evaluate the quality of these
decisions via an algorithm. Motivated by this development, an emerging line of
work has begun to consider whether we can characterize and predict the kinds of
decisions where people are likely to make errors.
  To investigate what a general framework for human error prediction might look
like, we focus on a model system with a rich history in the behavioral
sciences: the decisions made by chess players as they select moves in a game.
We carry out our analysis at a large scale, employing datasets with several
million recorded games, and using chess tablebases to acquire a form of ground
truth for a subset of chess positions that have been completely solved by
computers but remain challenging even for the best players in the world.
  We organize our analysis around three categories of features that we argue
are present in most settings where the analysis of human error is applicable:
the skill of the decision-maker, the time available to make the decision, and
the inherent difficulty of the decision. We identify rich structure in all
three of these categories of features, and find strong evidence that in our
domain, features describing the inherent difficulty of an instance are
significantly more powerful than features based on skill or time.
Deep Learning for Music
Authors: Allen Huang, Raymond Wu
Date: 2016-06-15 00:00:00
Tasks: Deep Learning, Density Estimation, Music Generation, Music Modeling, Time Series, Time Series Analysis

Our goal is to be able to build a generative model from a deep neural network
architecture to try to create music that has both harmony and melody and is
passable as music composed by humans. Previous work in music generation has
mainly been focused on creating a single melody. More recent work on polyphonic
music modeling, centered around time series probability density estimation, has
met some partial success. In particular, there has been a lot of work based off
of Recurrent Neural Networks combined with Restricted Boltzmann Machines
(RNN-RBM) and other similar recurrent energy based models. Our approach,
however, is to perform end-to-end learning and generation with deep neural nets
alone.