pwc-archive/papers-with-abstracts huggingface dataset:
On-the-fly Approximation of Multivariate Total Variation Minimization
Authors: Jordan Frecon, Nelly Pustelnik, Patrice Abry, Laurent CONDAT
Date: 2015-04-22 00:00:00
Tasks: Change Point Detection

In the context of change-point detection, addressed by Total Variation
minimization strategies, an efficient on-the-fly algorithm has been designed
leading to exact solutions for univariate data. In this contribution, an
extension of such an on-the-fly strategy to multivariate data is investigated.
The proposed algorithm relies on the local validation of the Karush-Kuhn-Tucker
conditions on the dual problem. Showing that the non-local nature of the
multivariate setting precludes to obtain an exact on-the-fly solution, we
devise an on-the-fly algorithm delivering an approximate solution, whose
quality is controlled by a practitioner-tunable parameter, acting as a
trade-off between quality and computational cost. Performance assessment shows
that high quality solutions are obtained on-the-fly while benefiting of
computational costs several orders of magnitude lower than standard iterative
procedures. The proposed algorithm thus provides practitioners with an
efficient multivariate change-point detection on-the-fly procedure.
Proceedings of the 2016 ICML Workshop on #Data4Good: Machine Learning in Social Good Applications
Authors: Kush R. Varshney
Date: 2016-07-08 00:00:00
Tasks: BIG-bench Machine Learning

This is the Proceedings of the ICML Workshop on #Data4Good: Machine Learning
in Social Good Applications, which was held on June 24, 2016 in New York.
Total variation reconstruction for compressive sensing using nonlocal Lagrangian multiplier
Authors: Trinh Van Chien, Khanh Quoc Dinh, Viet Anh Nguyen, Byeungwoo Jeon
Date: 2016-08-28 00:00:00
Tasks: Compressive Sensing

Total variation has proved its effectiveness in solving inverse problems for
compressive sensing. Besides, the nonlocal means filter used as regularization
preserves texture better for recovered images, but it is quite complex to
implement. In this paper, based on existence of both noise and image
information in the Lagrangian multiplier, we propose a simple method in term of
implementation called nonlocal Lagrangian multiplier (NLLM) in order to reduce
noise and boost useful image information. Experimental results show that the
proposed NLLM is superior both in subjective and objective qualities of
recovered image over other recovery algorithms.
Multiple objects tracking in surveillance video using color and Hu moments
Authors: Chandrajit M, Girisha R, Vasudev T
Date: 2016-08-22 00:00:00
Tasks: Change Detection, Gait Recognition, Object, Object Tracking

Multiple objects tracking finds its applications in many high level vision
analysis like object behaviour interpretation and gait recognition. In this
paper, a feature based method to track the multiple moving objects in
surveillance video sequence is proposed. Object tracking is done by extracting
the color and Hu moments features from the motion segmented object blob and
establishing the association of objects in the successive frames of the video
sequence based on Chi-Square dissimilarity measure and nearest neighbor
classifier. The benchmark IEEE PETS and IEEE Change Detection datasets has been
used to show the robustness of the proposed method. The proposed method is
assessed quantitatively using the precision and recall accuracy metrics.
Further, comparative evaluation with related works has been carried out to
exhibit the efficacy of the proposed method.
Cast and Self Shadow Segmentation in Video Sequences using Interval based Eigen Value Representation
Authors: Chandrajit M, Girisha R, Vasudev T, Ashok C B
Date: 2016-08-28 00:00:00
Tasks: Change Detection, Shadow Detection

Tracking of motion objects in the surveillance videos is useful for the
monitoring and analysis. The performance of the surveillance system will
deteriorate when shadows are detected as moving objects. Therefore, shadow
detection and elimination usually benefits the next stages. To overcome this
issue, a method for detection and elimination of shadows is proposed. This
paper presents a method for segmenting moving objects in video sequences based
on determining the Euclidian distance between two pixels considering
neighborhood values in temporal domain. Further, a method that segments cast
and self shadows in video sequences by computing the Eigen values for the
neighborhood of each pixel is proposed. The dual-map for cast and self shadow
pixels is represented based on the interval of Eigen values. The proposed
methods are tested on the benchmark IEEE CHANGE DETECTION 2014 dataset.
MindX: Denoising Mixed Impulse Poisson-Gaussian Noise Using Proximal Algorithms
Authors: Mohamed Aly, Wolfgang Heidrich
Date: 2016-08-28 00:00:00
Tasks: Combinatorial Optimization, Denoising

We present a novel algorithm for blind denoising of images corrupted by mixed
impulse, Poisson, and Gaussian noises. The algorithm starts by applying the
Anscombe variance-stabilizing transformation to convert the Poisson into white
Gaussian noise. Then it applies a combinatorial optimization technique to
denoise the mixed impulse Gaussian noise using proximal algorithms. The result
is then processed by the inverse Anscombe transform. We compare our algorithm
to state of the art methods on standard images, and show its superior
performance in various noise conditions.
Investigation Into The Effectiveness Of Long Short Term Memory Networks For Stock Price Prediction
Authors: Hengjian Jia
Date: 2016-03-25 00:00:00
Tasks: Stock Price Prediction

The effectiveness of long short term memory networks trained by
backpropagation through time for stock price prediction is explored in this
paper. A range of different architecture LSTM networks are constructed trained
and tested.
The Movie Graph Argument Revisited
Authors: Russell K. Standish
Date: 2016-08-28 00:00:00

In this paper, we reexamine the Movie Graph Argument, which demonstrates a
basic incompatibility between computationalism and materialism. We discover
that the incompatibility is only manifest in singular classical-like universes.
If we accept that we live in a Multiverse, then the incompatibility goes away,
but in that case another line of argument shows that with computationalism, the
fundamental, or primitive materiality has no causal influence on what is
observed, which must must be derivable from basic arithmetic properties.
Learning Temporal Transformations From Time-Lapse Videos
Authors: Yipin Zhou, Tamara L. Berg
Date: 2016-08-27 00:00:00
Tasks: Object

Based on life-long observations of physical, chemical, and biologic phenomena
in the natural world, humans can often easily picture in their minds what an
object will look like in the future. But, what about computers? In this paper,
we learn computational models of object transformations from time-lapse videos.
In particular, we explore the use of generative models to create depictions of
objects at future times. These models explore several different prediction
tasks: generating a future state given a single depiction of an object,
generating a future state given two depictions of an object at different times,
and generating future states recursively in a recurrent framework. We provide
both qualitative and quantitative evaluations of the generated results, and
also conduct a human evaluation to compare variations of our models.
A Bi-LSTM-RNN Model for Relation Classification Using Low-Cost Sequence Features
Authors: Fei Li, Meishan Zhang, Guohong Fu, Tao Qian, Donghong Ji
Date: 2016-08-27 00:00:00
Tasks: Dependency Parsing, General Classification, Relation, Relation Classification, Sentence

Relation classification is associated with many potential applications in the
artificial intelligence area. Recent approaches usually leverage neural
networks based on structure features such as syntactic or dependency features
to solve this problem. However, high-cost structure features make such
approaches inconvenient to be directly used. In addition, structure features
are probably domain-dependent. Therefore, this paper proposes a bi-directional
long-short-term-memory recurrent-neural-network (Bi-LSTM-RNN) model based on
low-cost sequence features to address relation classification. This model
divides a sentence or text segment into five parts, namely two target entities
and their three contexts. It learns the representations of entities and their
contexts, and uses them to classify relations. We evaluate our model on two
standard benchmark datasets in different domains, namely SemEval-2010 Task 8
and BioNLP-ST 2016 Task BB3. In the former dataset, our model achieves
comparable performance compared with other models using sequence features. In
the latter dataset, our model obtains the third best results compared with
other models in the official evaluation. Moreover, we find that the context
between two target entities plays the most important role in relation
classification. Furthermore, statistic experiments show that the context
between two target entities can be used as an approximate replacement of the
shortest dependency path when dependency parsing is not used.
On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching
Authors: Vince Lyzinski, Keith Levin, Donniell E. Fishkind, Carey E. Priebe
Date: 2016-07-05 00:00:00
Tasks: Graph Matching, Stochastic Block Model

Given a graph in which a few vertices are deemed interesting a priori, the
vertex nomination task is to order the remaining vertices into a nomination
list such that there is a concentration of interesting vertices at the top of
the list. Previous work has yielded several approaches to this problem, with
theoretical results in the setting where the graph is drawn from a stochastic
block model (SBM), including a vertex nomination analogue of the Bayes optimal
classifier. In this paper, we prove that maximum likelihood (ML)-based vertex
nomination is consistent, in the sense that the performance of the ML-based
scheme asymptotically matches that of the Bayes optimal scheme. We prove
theorems of this form both when model parameters are known and unknown.
Additionally, we introduce and prove consistency of a related, more scalable
restricted-focus ML vertex nomination scheme. Finally, we incorporate vertex
and edge features into ML-based vertex nomination and briefly explore the
empirical effectiveness of this approach.
A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples
Authors: Thomas Tanay, Lewis Griffin
Date: 2016-08-27 00:00:00
Tasks: General Classification

Deep neural networks have been shown to suffer from a surprising weakness:
their classification outputs can be changed by small, non-random perturbations
of their inputs. This adversarial example phenomenon has been explained as
originating from deep networks being "too linear" (Goodfellow et al., 2014). We
show here that the linear explanation of adversarial examples presents a number
of limitations: the formal argument is not convincing, linear classifiers do
not always suffer from the phenomenon, and when they do their adversarial
examples are different from the ones affecting deep networks.
  We propose a new perspective on the phenomenon. We argue that adversarial
examples exist when the classification boundary lies close to the submanifold
of sampled data, and present a mathematical analysis of this new perspective in
the linear case. We define the notion of adversarial strength and show that it
can be reduced to the deviation angle between the classifier considered and the
nearest centroid classifier. Then, we show that the adversarial strength can be
made arbitrarily high independently of the classification performance due to a
mechanism that we call boundary tilting. This result leads us to defining a new
taxonomy of adversarial examples. Finally, we show that the adversarial
strength observed in practice is directly dependent on the level of
regularisation used and the strongest adversarial examples, symptomatic of
overfitting, can be avoided by using a proper level of regularisation.
Spatio-temporal Aware Non-negative Component Representation for Action Recognition
Authors: Jianhong Wang, Tian Lan, Xu Zhang, Limin Luo
Date: 2016-08-27 00:00:00
Tasks: Action Recognition, Temporal Action Localization

This paper presents a novel mid-level representation for action recognition,
named spatio-temporal aware non-negative component representation (STANNCR).
The proposed STANNCR is based on action component and incorporates the
spatial-temporal information. We first introduce a spatial-temporal
distribution vector (STDV) to model the distributions of local feature
locations in a compact and discriminative manner. Then we employ non-negative
matrix factorization (NMF) to learn the action components and encode the video
samples. The action component considers the correlations of visual words, which
effectively bridge the sematic gap in action recognition. To incorporate the
spatial-temporal cues for final representation, the STDV is used as the part of
graph regularization for NMF. The fusion of spatial-temporal information makes
the STANNCR more discriminative, and our fusion manner is more compact than
traditional method of concatenating vectors. The proposed approach is
extensively evaluated on three public datasets. The experimental results
demonstrate the effectiveness of STANNCR for action recognition.
Image Block Loss Restoration Using Sparsity Pattern as Side Information
Authors: Hossein Hosseini, Ali Goli, Neda Barzegar Marvasti, Masoume Azghani, Farokh Marvasti
Date: 2014-01-23 00:00:00

In this paper, we propose a method for image block loss restoration based on
the notion of sparse representation. We use the sparsity pattern as side
information to efficiently restore block losses by iteratively imposing the
constraints of spatial and transform domains on the corrupted image. Two novel
features, including a pre-interpolation and a criterion for stopping the
iterations, are proposed to improve the performance. Also, to deal with
practical applications, we develop a technique to transmit the side information
along with the image. In this technique, we first compress the side information
and then embed its LDPC coded version in the least significant bits of the
image pixels. This technique ensures the error-free transmission of the side
information, while causing only a small perturbation on the transmitted image.
Mathematical analysis and extensive simulations are performed to validate the
method and investigate the efficiency of the proposed techniques. The results
verify that the proposed method outperforms its counterparts for image block
loss restoration.
Learning to generalize to new compositions in image understanding
Authors: Yuval Atzmon, Jonathan Berant, Vahid Kezami, Amir Globerson, Gal Chechik
Date: 2016-08-27 00:00:00
Tasks: Image Captioning, Structured Prediction

Recurrent neural networks have recently been used for learning to describe
images using natural language. However, it has been observed that these models
generalize poorly to scenes that were not observed during training, possibly
depending too strongly on the statistics of the text in the training data. Here
we propose to describe images using short structured representations, aiming to
capture the crux of a description. These structured representations allow us to
tease-out and evaluate separately two types of generalization: standard
generalization to new images with similar scenes, and generalization to new
combinations of known entities. We compare two learning approaches on the
MS-COCO dataset: a state-of-the-art recurrent network based on an LSTM (Show,
Attend and Tell), and a simple structured prediction model on top of a deep
network. We find that the structured model generalizes to new compositions
substantially better than the LSTM, ~7 times the accuracy of predicting
structured representations. By providing a concrete method to quantify
generalization for unseen combinations, we argue that structured
representations and compositional splits are a useful benchmark for image
captioning, and advocate compositional models that capture linguistic and
visual structure.
Learning Temporal Dependence from Time-Series Data with Latent Variables
Authors: Hossein Hosseini, Sreeram Kannan, Baosen Zhang, Radha Poovendran
Date: 2016-08-27 00:00:00
Tasks: Time Series, Time Series Analysis

We consider the setting where a collection of time series, modeled as random
processes, evolve in a causal manner, and one is interested in learning the
graph governing the relationships of these processes. A special case of wide
interest and applicability is the setting where the noise is Gaussian and
relationships are Markov and linear. We study this setting with two additional
features: firstly, each random process has a hidden (latent) state, which we
use to model the internal memory possessed by the variables (similar to hidden
Markov models). Secondly, each variable can depend on its latent memory state
through a random lag (rather than a fixed lag), thus modeling memory recall
with differing lags at distinct times. Under this setting, we develop an
estimator and prove that under a genericity assumption, the parameters of the
model can be learned consistently. We also propose a practical adaption of this
estimator, which demonstrates significant performance gains in both synthetic
and real-world datasets.
Global analysis of Expectation Maximization for mixtures of two Gaussians
Authors: Ji Xu, Daniel Hsu, Arian Maleki
Date: 2016-08-26 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Vocal Bursts Valence Prediction

Expectation Maximization (EM) is among the most popular algorithms for
estimating parameters of statistical models. However, EM, which is an iterative
algorithm based on the maximum likelihood principle, is generally only
guaranteed to find stationary points of the likelihood objective, and these
points may be far from any maximizer. This article addresses this disconnect
between the statistical principles behind EM and its algorithmic properties.
Specifically, it provides a global analysis of EM for specific models in which
the observations comprise an i.i.d. sample from a mixture of two Gaussians.
This is achieved by (i) studying the sequence of parameters from idealized
execution of EM in the infinite sample limit, and fully characterizing the
limit points of the sequence in terms of the initial parameters; and then (ii)
based on this convergence analysis, establishing statistical consistency (or
lack thereof) for the actual sequence of parameters produced by EM.
Large Scale Behavioral Analytics via Topical Interaction
Authors: Shih-Chieh Su
Date: 2016-08-26 00:00:00
Tasks: Dimensionality Reduction

We propose the split-diffuse (SD) algorithm that takes the output of an
existing dimension reduction algorithm, and distributes the data points
uniformly across the visualization space. The result, called the topic grids,
is a set of grids on various topics which are generated from the free-form text
content of any domain of interest. The topic grids efficiently utilizes the
visualization space to provide visual summaries for massive data. Topical
analysis, comparison and interaction can be performed on the topic grids in a
more perceivable way.
Interacting with Massive Behavioral Data
Authors: Shih-Chieh Su
Date: 2016-08-26 00:00:00
Tasks: Dimensionality Reduction

In this short paper, we propose the split-diffuse (SD) algorithm that takes
the output of an existing word embedding algorithm, and distributes the data
points uniformly across the visualization space. The result improves the
perceivability and the interactability by the human.
  We apply the SD algorithm to analyze the user behavior through access logs
within the cyber security domain. The result, named the topic grids, is a set
of grids on various topics generated from the logs. On the same set of grids,
different behavioral metrics can be shown on different targets over different
periods of time, to provide visualization and interaction to the human experts.
  Analysis, investigation, and other types of interaction can be performed on
the topic grids more efficiently than on the output of existing dimension
reduction methods. In addition to the cyber security domain, the topic grids
can be further applied to other domains like e-commerce, credit card
transaction, customer service to analyze the behavior in a large scale.
Understanding the Energy and Precision Requirements for Online Learning
Authors: Charbel Sakr, Ameya Patil, Sai Zhang, Yongjune Kim, Naresh Shanbhag
Date: 2016-07-03 00:00:00
Tasks: General Classification

It is well-known that the precision of data, hyperparameters, and internal
representations employed in learning systems directly impacts its energy,
throughput, and latency. The precision requirements for the training algorithm
are also important for systems that learn on-the-fly. Prior work has shown that
the data and hyperparameters can be quantized heavily without incurring much
penalty in classification accuracy when compared to floating point
implementations. These works suffer from two key limitations. First, they
assume uniform precision for the classifier and for the training algorithm and
thus miss out on the opportunity to further reduce precision. Second, prior
works are empirical studies. In this article, we overcome both these
limitations by deriving analytical lower bounds on the precision requirements
of the commonly employed stochastic gradient descent (SGD) on-line learning
algorithm in the specific context of a support vector machine (SVM). Lower
bounds on the data precision are derived in terms of the the desired
classification accuracy and precision of the hyperparameters used in the
classifier. Additionally, lower bounds on the hyperparameter precision in the
SGD training algorithm are obtained. These bounds are validated using both
synthetic and the UCI breast cancer dataset. Additionally, the impact of these
precisions on the energy consumption of a fixed-point SVM with on-line training
is studied.
Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields
Authors: Gerda Bortsova, Michael Sterr, Lichao Wang, Fausto Milletari, Nassir Navab, Anika Böttcher, Heiko Lickert, Fabian Theis, Tingying Peng
Date: 2016-08-26 00:00:00
Tasks: Mitosis Detection

Intestinal enteroendocrine cells secrete hormones that are vital for the
regulation of glucose metabolism but their differentiation from intestinal stem
cells is not fully understood. Asymmetric stem cell divisions have been linked
to intestinal stem cell homeostasis and secretory fate commitment. We monitored
cell divisions using 4D live cell imaging of cultured intestinal crypts to
characterize division modes by means of measurable features such as orientation
or shape. A statistical analysis of these measurements requires annotation of
mitosis events, which is currently a tedious and time-consuming task that has
to be performed manually. To assist data processing, we developed a learning
based method to automatically detect mitosis events. The method contains a
dual-phase framework for joint detection of dividing cells (mothers) and their
progeny (daughters). In the first phase we detect mother and daughters
independently using Hough Forest whilst in the second phase we associate mother
and daughters by modelling their joint probability as Conditional Random Field
(CRF). The method has been evaluated on 32 movies and has achieved an AUC of
72%, which can be used in conjunction with manual correction and dramatically
speed up the processing pipeline.