pwc-archive/papers-with-abstracts huggingface dataset:
Disentangling factors of variation in deep representations using adversarial training
Authors: Michael Mathieu, Junbo Zhao, Pablo Sprechmann, Aditya Ramesh, Yann Lecun
Date: 2016-11-10 00:00:00
Tasks: Disentanglement

We introduce a conditional generative model for learning to disentangle the
hidden factors of variation within a set of labeled observations, and separate
them into complementary codes. One code summarizes the specified factors of
variation associated with the labels. The other summarizes the remaining
unspecified variability. During training, the only available source of
supervision comes from our ability to distinguish among different observations
belonging to the same class. Examples of such observations include images of a
set of labeled objects captured at different viewpoints, or recordings of set
of speakers dictating multiple phrases. In both instances, the intra-class
diversity is the source of the unspecified factors of variation: each object is
observed at multiple viewpoints, and each speaker dictates multiple phrases.
Learning to disentangle the specified factors from the unspecified ones becomes
easier when strong supervision is possible. Suppose that during training, we
have access to pairs of images, where each pair shows two different objects
captured from the same viewpoint. This source of alignment allows us to solve
our task using existing methods. However, labels for the unspecified factors
are usually unavailable in realistic scenarios where data acquisition is not
strictly controlled. We address the problem of disentanglement in this more
general setting by combining deep convolutional autoencoders with a form of
adversarial training. Both factors of variation are implicitly captured in the
organization of the learned embedding space, and can be used for solving
single-image analogies. Experimental results on synthetic and real datasets
show that the proposed method is capable of generalizing to unseen classes and
intra-class variabilities.
Efficient Summarization with Read-Again and Copy Mechanism
Authors: Wenyuan Zeng, Wenjie Luo, Sanja Fidler, Raquel Urtasun
Date: 2016-11-10 00:00:00
Tasks: Decoder

Encoder-decoder models have been widely used to solve sequence to sequence
prediction tasks. However current approaches suffer from two shortcomings.
First, the encoders compute a representation of each word taking into account
only the history of the words it has read so far, yielding suboptimal
representations. Second, current decoders utilize large vocabularies in order
to minimize the problem of unknown words, resulting in slow decoding times. In
this paper we address both shortcomings. Towards this goal, we first introduce
a simple mechanism that first reads the input sequence before committing to a
representation of each word. Furthermore, we propose a simple copy mechanism
that is able to exploit very small vocabularies and handle out-of-vocabulary
words. We demonstrate the effectiveness of our approach on the Gigaword dataset
and DUC competition outperforming the state-of-the-art.
Advancing Memristive Analog Neuromorphic Networks: Increasing Complexity, and Coping with Imperfect Hardware Components
Authors: F. Merrikh Bayat, M. Prezioso, B. Chakrabarti, I. Kataeva, D. B. Strukov
Date: 2016-11-10 00:00:00

We experimentally demonstrate classification of 4x4 binary images into 4
classes, using a 3-layer mixed-signal neuromorphic network ("MLP perceptron"),
based on two passive 20x20 memristive crossbar arrays, board-integrated with
discrete CMOS components. The network features 10 hidden-layer and 4
output-layer analog CMOS neurons and 428 metal-oxide memristors, i.e. is almost
an order of magnitude more complex than any previously reported functional
memristor circuit. Moreover, the inference operation of this classifier is
performed entirely in the integrated hardware. To deal with larger crossbar
arrays, we have developed a semi-automatic approach to their forming and
testing, and compared several memristor training schemes for coping with
imperfect behavior of these devices, as well as with variability of analog CMOS
neurons. The effectiveness of the proposed schemes for defect and variation
tolerance was verified experimentally using the implemented network and,
additionally, by modeling the operation of a larger network, with 300
hidden-layer neurons, on the MNIST benchmark. Finally, we propose a simple
modification of the implemented memristor-based vector-by-matrix multiplier to
allow its operation in a wider temperature range.
A stochastically verifiable autonomous control architecture with reasoning
Authors: Paolo Izzo, Hongyang Qu, Sandor M. Veres
Date: 2016-11-10 00:00:00

A new agent architecture called Limited Instruction Set Agent (LISA) is
introduced for autonomous control. The new architecture is based on previous
implementations of AgentSpeak and it is structurally simpler than its
predecessors with the aim of facilitating design-time and run-time verification
methods. The process of abstracting the LISA system to two different types of
discrete probabilistic models (DTMC and MDP) is investigated and illustrated.
The LISA system provides a tool for complete modelling of the agent and the
environment for probabilistic verification. The agent program can be
automatically compiled into a DTMC or a MDP model for verification with Prism.
The automatically generated Prism model can be used for both design-time and
run-time verification. The run-time verification is investigated and
illustrated in the LISA system as an internal modelling mechanism for
prediction of future outcomes.
Distributed Estimation and Learning over Heterogeneous Networks
Authors: M. Amin Rahimian, Ali Jadbabaie
Date: 2016-11-10 00:00:00

We consider several estimation and learning problems that networked agents
face when making decisions given their uncertainty about an unknown variable.
Our methods are designed to efficiently deal with heterogeneity in both size
and quality of the observed data, as well as heterogeneity over time
(intermittence). The goal of the studied aggregation schemes is to efficiently
combine the observed data that is spread over time and across several network
nodes, accounting for all the network heterogeneities. Moreover, we require no
form of coordination beyond the local neighborhood of every network agent or
sensor node. The three problems that we consider are (i) maximum likelihood
estimation of the unknown given initial data sets, (ii) learning the true model
parameter from streams of data that the agents receive intermittently over
time, and (iii) minimum variance estimation of a complete sufficient statistic
from several data points that the networked agents collect over time. In each
case we rely on an aggregation scheme to combine the observations of all
agents; moreover, when the agents receive streams of data over time, we modify
the update rules to accommodate the most recent observations. In every case, we
demonstrate the efficiency of our algorithms by proving convergence to the
globally efficient estimators given the observations of all agents. We
supplement these results by investigating the rate of convergence and providing
finite-time performance guarantees.
Computing threshold functions using dendrites
Authors: Romain Cazé, Bartozs Teleńczuk, Alain Destexhe
Date: 2016-11-10 00:00:00

Neurons, modeled as linear threshold unit (LTU), can in theory compute all
thresh- old functions. In practice, however, some of these functions require
synaptic weights of arbitrary large precision. We show here that dendrites can
alleviate this requirement. We introduce here the non-Linear Threshold Unit
(nLTU) that integrates synaptic input sub-linearly within distinct subunits to
take into account local saturation in dendrites. We systematically search
parameter space of the nTLU and TLU to compare them. Firstly, this shows that
the nLTU can compute all threshold functions with smaller precision weights
than the LTU. Secondly, we show that a nLTU can compute significantly more
functions than a LTU when an input can only make a single synapse. This work
paves the way for a new generation of network made of nLTU with binary
synapses.
Variables effecting photomosaic reconstruction and ortho-rectification from aerial survey datasets
Authors: Jonathan Byrne, Debra Laefer
Date: 2016-11-10 00:00:00
Tasks: Image Stitching

Unmanned aerial vehicles now make it possible to obtain high quality aerial
imagery at a low cost, but processing those images into a single, useful entity
is neither simple nor seamless. Specifically, there are factors that must be
addressed when merging multiple images into a single coherent one. While
ortho-rectification can be done, it tends to be expensive and time consuming.
Image stitching offers a more economical, low-tech approach. However direct
application tends to fail for low-elevation imagery due to one or more factors
including insufficient keypoints, parallax issues, and homogeneity of the
surveyed area. This paper discusses these problems and possible solutions when
using techniques such as image stitching and structure from motion for
generating ortho-rectified imagery. These are presented in terms of actual
Irish projects including the Boland's Mills building in Dublin's city centre,
the Kilmoon Cross Farm, and the Richview buildings on the University College
Dublin campus. Implications for various Irish industries are explained in terms
of both urban and rural projects.
X-ray Scattering Image Classification Using Deep Learning
Authors: Boyu Wang, Kevin Yager, Dantong Yu, Minh Hoai
Date: 2016-11-10 00:00:00
Tasks: Classification, Deep Learning, General Classification, image-classification, Image Classification

Visual inspection of x-ray scattering images is a powerful technique for
probing the physical structure of materials at the molecular scale. In this
paper, we explore the use of deep learning to develop methods for automatically
analyzing x-ray scattering images. In particular, we apply Convolutional Neural
Networks and Convolutional Autoencoders for x-ray scattering image
classification. To acquire enough training data for deep learning, we use
simulation software to generate synthetic x-ray scattering images. Experiments
show that deep learning methods outperform previously published methods by 10\%
on synthetic and real datasets.
Recognizing and Presenting the Storytelling Video Structure with Deep Multimodal Networks
Authors: Lorenzo Baraldi, Costantino Grana, Rita Cucchiara
Date: 2016-10-05 00:00:00
Tasks: Change Detection, Retrieval, Semantic Segmentation

This paper presents a novel approach for temporal and semantic segmentation
of edited videos into meaningful segments, from the point of view of the
storytelling structure. The objective is to decompose a long video into more
manageable sequences, which can in turn be used to retrieve the most
significant parts of it given a textual query and to provide an effective
summarization. Previous video decomposition methods mainly employed perceptual
cues, tackling the problem either as a story change detection, or as a
similarity grouping task, and the lack of semantics limited their ability to
identify story boundaries. Our proposal connects together perceptual, audio and
semantic cues in a specialized deep network architecture designed with a
combination of CNNs which generate an appropriate embedding, and clusters shots
into connected sequences of semantic scenes, i.e. stories. A retrieval
presentation strategy is also proposed, by selecting the semantically and
aesthetically "most valuable" thumbnails to present, considering the query in
order to improve the storytelling presentation. Finally, the subjective nature
of the task is considered, by conducting experiments with different annotators
and by proposing an algorithm to maximize the agreement between automatic
results and human annotators.
A fast PC algorithm for high dimensional causal discovery with multi-core PCs
Authors: Thuc Duy Le, Tao Hoang, Jiuyong Li, Lin Liu, Huawen Liu
Date: 2015-02-09 00:00:00
Tasks: Causal Discovery, Causal Inference, CPU

Discovering causal relationships from observational data is a crucial problem
and it has applications in many research areas. The PC algorithm is the
state-of-the-art constraint based method for causal discovery. However, runtime
of the PC algorithm, in the worst-case, is exponential to the number of nodes
(variables), and thus it is inefficient when being applied to high dimensional
data, e.g. gene expression datasets. On another note, the advancement of
computer hardware in the last decade has resulted in the widespread
availability of multi-core personal computers. There is a significant
motivation for designing a parallelised PC algorithm that is suitable for
personal computers and does not require end users' parallel computing knowledge
beyond their competency in using the PC algorithm. In this paper, we develop
parallel-PC, a fast and memory efficient PC algorithm using the parallel
computing technique. We apply our method to a range of synthetic and real-world
high dimensional datasets. Experimental results on a dataset from the DREAM 5
challenge show that the original PC algorithm could not produce any results
after running more than 24 hours; meanwhile, our parallel-PC algorithm managed
to finish within around 12 hours with a 4-core CPU computer, and less than 6
hours with a 8-core CPU computer. Furthermore, we integrate parallel-PC into a
causal inference method for inferring miRNA-mRNA regulatory relationships. The
experimental results show that parallel-PC helps improve both the efficiency
and accuracy of the causal inference algorithm.
Tracing metaphors in time through self-distance in vector spaces
Authors: Marco Del Tredici, Malvina Nissim, Andrea Zaninello
Date: 2016-11-10 00:00:00

From a diachronic corpus of Italian, we build consecutive vector spaces in
time and use them to compare a term's cosine similarity to itself in different
time spans. We assume that a drop in similarity might be related to the
emergence of a metaphorical sense at a given time. Similarity-based
observations are matched to the actual year when a figurative meaning was
documented in a reference dictionary and through manual inspection of corpus
occurrences.
Detecting Moving Regions in CrowdCam Images
Authors: Adi Dafni, Yael Moses, Shai Avidan
Date: 2016-11-10 00:00:00

We address the novel problem of detecting dynamic regions in CrowdCam images,
a set of still images captured by a group of people. These regions capture the
most interesting parts of the scene, and detecting them plays an important role
in the analysis of visual data. Our method is based on the observation that
matching static points must satisfy the epipolar geometry constraints, but
computing exact matches is challenging. Instead, we compute the probability
that a pixel has a match, not necessarily the correct one, along the
corresponding epipolar line. The complement of this probability is not
necessarily the probability of a dynamic point because of occlusions, noise,
and matching errors. Therefore, information from all pairs of images is
aggregated to obtain a high quality dynamic probability map, per image.
Experiments on challenging datasets demonstrate the effectiveness of the
algorithm on a broad range of settings; no prior knowledge about the scene, the
camera characteristics or the camera locations is required.
Learning Natural Language Inference with LSTM
Authors: Shuohang Wang, Jing Jiang
Date: 2015-12-30 00:00:00
Proceeding: NAACL 2016 6
Tasks: Natural Language Inference, Sentence, Sentence Embeddings

Natural language inference (NLI) is a fundamentally important task in natural
language processing that has many applications. The recently released Stanford
Natural Language Inference (SNLI) corpus has made it possible to develop and
evaluate learning-centered methods such as deep neural networks for natural
language inference (NLI). In this paper, we propose a special long short-term
memory (LSTM) architecture for NLI. Our model builds on top of a recently
proposed neural attention model for NLI but is based on a significantly
different idea. Instead of deriving sentence embeddings for the premise and the
hypothesis to be used for classification, our solution uses a match-LSTM to
perform word-by-word matching of the hypothesis with the premise. This LSTM is
able to place more emphasis on important word-level matching results. In
particular, we observe that this LSTM remembers important mismatches that are
critical for predicting the contradiction or the neutral relationship label. On
the SNLI corpus, our model achieves an accuracy of 86.1%, outperforming the
state of the art.
Error concealment by means of motion refinement and regularized Bregman divergence
Authors: Alessandra M. Coelho, Vania V. Estrela, Felipe P. do Carmo, Sandro R. Fernandes
Date: 2016-11-10 00:00:00
Tasks: parameter estimation

This work addresses the problem of error concealment in video transmission
systems over noisy channels employing Bregman divergences along with
regularization. Error concealment intends to improve the effects of
disturbances at the reception due to bit-errors or cell loss in packet
networks. Bregman regularization gives accurate answers after just some
iterations with fast convergence, better accuracy, and stability. This
technique has an adaptive nature: the regularization functional is updated
according to Bregman functions that change from iteration to iteration
according to the nature of the neighborhood under study at iteration n.
Numerical experiments show that high-quality regularization parameter estimates
can be obtained. The convergence is sped up while turning the regularization
parameter estimation less empiric, and more automatic.
Sigma Delta Quantized Networks
Authors: Peter O'Connor, Max Welling
Date: 2016-11-07 00:00:00

Deep neural networks can be obscenely wasteful. When processing video, a
convolutional network expends a fixed amount of computation for each frame with
no regard to the similarity between neighbouring frames. As a result, it ends
up repeatedly doing very similar computations. To put an end to such waste, we
introduce Sigma-Delta networks. With each new input, each layer in this network
sends a discretized form of its change in activation to the next layer. Thus
the amount of computation that the network does scales with the amount of
change in the input and layer activations, rather than the size of the network.
We introduce an optimization method for converting any pre-trained deep network
into an optimally efficient Sigma-Delta network, and show that our algorithm,
if run on the appropriate hardware, could cut at least an order of magnitude
from the computational cost of processing video data.
Policy Search with High-Dimensional Context Variables
Authors: Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama
Date: 2016-11-10 00:00:00
Tasks: Dimensionality Reduction, Vocal Bursts Intensity Prediction

Direct contextual policy search methods learn to improve policy parameters
and simultaneously generalize these parameters to different context or task
variables. However, learning from high-dimensional context variables, such as
camera images, is still a prominent problem in many real-world tasks. A naive
application of unsupervised dimensionality reduction methods to the context
variables, such as principal component analysis, is insufficient as
task-relevant input may be ignored. In this paper, we propose a contextual
policy search method in the model-based relative entropy stochastic search
framework with integrated dimensionality reduction. We learn a model of the
reward that is locally quadratic in both the policy parameters and the context
variables. Furthermore, we perform supervised linear dimensionality reduction
on the context variables by nuclear norm regularization. The experimental
results show that the proposed method outperforms naive dimensionality
reduction via principal component analysis and a state-of-the-art contextual
policy search method.
Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets
Authors: Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos
Date: 2016-11-10 00:00:00
Tasks: feature selection, Survival Analysis

The statistically equivalent signature (SES) algorithm is a method for
feature selection inspired by the principles of constrained-based learning of
Bayesian Networks. Most of the currently available feature-selection methods
return only a single subset of features, supposedly the one with the highest
predictive power. We argue that in several domains multiple subsets can achieve
close to maximal predictive accuracy, and that arbitrarily providing only one
has several drawbacks. The SES method attempts to identify multiple, predictive
feature subsets whose performances are statistically equivalent. Under that
respect SES subsumes and extends previous feature selection algorithms, like
the max-min parent children algorithm. SES is implemented in an homonym
function included in the R package MXM, standing for mens ex machina, meaning
'mind from the machine' in Latin. The MXM implementation of SES handles several
data-analysis tasks, namely classification, regression and survival analysis.
In this paper we present the SES algorithm, its implementation, and provide
examples of use of the SES function in R. Furthermore, we analyze three
publicly available data sets to illustrate the equivalence of the signatures
retrieved by SES and to contrast SES against the state-of-the-art feature
selection method LASSO. Our results provide initial evidence that the two
methods perform comparably well in terms of predictive accuracy and that
multiple, equally predictive signatures are actually present in real world
data.
Real Time Video Analysis using Smart Phone Camera for Stroboscopic Image
Authors: Somnath Mukherjee, Soumyajit Ganguly
Date: 2016-11-10 00:00:00
Tasks: Object

Motion capturing and there by segmentation of the motion of any moving object
from a sequence of continuous images or a video is not an exceptional task in
computer vision area. Smart-phone camera application is an added integration
for the development of such tasks and it also provides for a smooth testing. A
new approach has been proposed for segmenting out the foreground moving object
from the background and then masking the sequential motion with the static
background which is commonly known as stroboscopic image. In this paper the
whole process of the stroboscopic image construction technique has been clearly
described along with some necessary constraints which is due to the traditional
problem of estimating and modeling dynamic background changes. The background
subtraction technique has been properly estimated here and number of sequential
motion have also been calculated with the correlation between the motion of the
object and its time of occurrence. This can be a very effective application
that can replace the traditional stroboscopic system using high end SLR
cameras, tripod stand, shutter speed control and position etc.
Ultimate tensorization: compressing convolutional and FC layers alike
Authors: Timur Garipov, Dmitry Podoprikhin, Alexander Novikov, Dmitry Vetrov
Date: 2016-11-10 00:00:00

Convolutional neural networks excel in image recognition tasks, but this
comes at the cost of high computational and memory complexity. To tackle this
problem, [1] developed a tensor factorization framework to compress
fully-connected layers. In this paper, we focus on compressing convolutional
layers. We show that while the direct application of the tensor framework [1]
to the 4-dimensional kernel of convolution does compress the layer, we can do
better. We reshape the convolutional kernel into a tensor of higher order and
factorize it. We combine the proposed approach with the previous work to
compress both convolutional and fully-connected layers of a network and achieve
80x network compression rate with 1.1% accuracy drop on the CIFAR-10 dataset.