pwc-archive/papers-with-abstracts huggingface dataset:
Compressive phase-only filtering at extreme compression rates
Authors: David Pastor-Calle, Anna Pastuszczak, Michal Mikolajczyk, Rafal Kotynski
Date: 2016-04-26 00:00:00
Tasks: Compressive Sensing

We introduce an efficient method for the reconstruction of the correlation
between a compressively measured image and a phase-only filter. The proposed
method is based on two properties of phase-only filtering: such filtering is a
unitary circulant transform, and the correlation plane it produces is usually
sparse. Thanks to these properties, phase-only filters are perfectly compatible
with the framework of compressive sensing. Moreover, the lasso-based recovery
algorithm is very fast when phase-only filtering is used as the compression
matrix. The proposed method can be seen as a generalisation of the
correlation-based pattern recognition technique, which is hereby applied
directly to non-adaptively acquired compressed data. At the time of
measurement, any prior knowledge of the target object for which the data will
be scanned is not required. We show that images measured at extremely high
compression rates may still contain sufficient information for target
classification and localization, even if the compression rate is high enough,
that visual recognition of the target in the reconstructed image is no longer
possible. The method has been applied by us to highly undersampled measurements
obtained from a single-pixel camera, with sampling based on randomly chosen
Walsh-Hadamard patterns.
Structure-Aware Classification using Supervised Dictionary Learning
Authors: Yael Yankelevsky, Michael Elad
Date: 2016-09-29 00:00:00
Tasks: Classification, Dictionary Learning, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

In this paper, we propose a supervised dictionary learning algorithm that
aims to preserve the local geometry in both dimensions of the data. A
graph-based regularization explicitly takes into account the local manifold
structure of the data points. A second graph regularization gives similar
treatment to the feature domain and helps in learning a more robust dictionary.
Both graphs can be constructed from the training data or learned and adapted
along the dictionary learning process. The combination of these two terms
promotes the discriminative power of the learned sparse representations and
leads to improved classification accuracy. The proposed method was evaluated on
several different datasets, representing both single-label and multi-label
classification problems, and demonstrated better performance compared with
other dictionary based approaches.
EXTRACT: Strong Examples from Weakly-Labeled Sensor Data
Authors: Davis W. Blalock, John V. Guttag
Date: 2016-09-29 00:00:00
Tasks: Time Series, Time Series Analysis

Thanks to the rise of wearable and connected devices, sensor-generated time
series comprise a large and growing fraction of the world's data.
Unfortunately, extracting value from this data can be challenging, since
sensors report low-level signals (e.g., acceleration), not the high-level
events that are typically of interest (e.g., gestures). We introduce a
technique to bridge this gap by automatically extracting examples of real-world
events in low-level data, given only a rough estimate of when these events have
taken place.
  By identifying sets of features that repeat in the same temporal arrangement,
we isolate examples of such diverse events as human actions, power consumption
patterns, and spoken words with up to 96% precision and recall. Our method is
fast enough to run in real time and assumes only minimal knowledge of which
variables are relevant or the lengths of events. Our evaluation uses numerous
publicly available datasets and over 1 million samples of manually labeled
sensor data.
Multi Model Data mining approach for Heart failure prediction
Authors: Priyanka H U, Vivek R
Date: 2016-09-29 00:00:00
Tasks: Prediction

Developing predictive modelling solutions for risk estimation is extremely
challenging in health-care informatics. Risk estimation involves integration of
heterogeneous clinical sources having different representation from different
health-care provider making the task increasingly complex. Such sources are
typically voluminous, diverse, and significantly change over the time.
Therefore, distributed and parallel computing tools collectively termed big
data tools are in need which can synthesize and assist the physician to make
right clinical decisions. In this work we propose multi-model predictive
architecture, a novel approach for combining the predictive ability of multiple
models for better prediction accuracy. We demonstrate the effectiveness and
efficiency of the proposed work on data from Framingham Heart study. Results
show that the proposed multi-model predictive architecture is able to provide
better accuracy than best model approach. By modelling the error of predictive
models we are able to choose sub set of models which yields accurate results.
More information was modelled into system by multi-level mining which has
resulted in enhanced predictive accuracy.
Gambler's Ruin Bandit Problem
Authors: Nima Akbarzadeh, Cem Tekin
Date: 2016-05-21 00:00:00

In this paper, we propose a new multi-armed bandit problem called the
Gambler's Ruin Bandit Problem (GRBP). In the GRBP, the learner proceeds in a
sequence of rounds, where each round is a Markov Decision Process (MDP) with
two actions (arms): a continuation action that moves the learner randomly over
the state space around the current state; and a terminal action that moves the
learner directly into one of the two terminal states (goal and dead-end state).
The current round ends when a terminal state is reached, and the learner incurs
a positive reward only when the goal state is reached. The objective of the
learner is to maximize its long-term reward (expected number of times the goal
state is reached), without having any prior knowledge on the state transition
probabilities. We first prove a result on the form of the optimal policy for
the GRBP. Then, we define the regret of the learner with respect to an
omnipotent oracle, which acts optimally in each round, and prove that it
increases logarithmically over rounds. We also identify a condition under which
the learner's regret is bounded. A potential application of the GRBP is optimal
medical treatment assignment, in which the continuation action corresponds to a
conservative treatment and the terminal action corresponds to a risky treatment
such as surgery.
Topic Browsing for Research Papers with Hierarchical Latent Tree Analysis
Authors: Leonard K. M. Poon, Nevin L. Zhang
Date: 2016-09-29 00:00:00

Academic researchers often need to face with a large collection of research
papers in the literature. This problem may be even worse for postgraduate
students who are new to a field and may not know where to start. To address
this problem, we have developed an online catalog of research papers where the
papers have been automatically categorized by a topic model. The catalog
contains 7719 papers from the proceedings of two artificial intelligence
conferences from 2000 to 2015. Rather than the commonly used Latent Dirichlet
Allocation, we use a recently proposed method called hierarchical latent tree
analysis for topic modeling. The resulting topic model contains a hierarchy of
topics so that users can browse the topics from the top level to the bottom
level. The topic model contains a manageable number of general topics at the
top level and allows thousands of fine-grained topics at the bottom level. It
also can detect topics that have emerged recently.
OPML: A One-Pass Closed-Form Solution for Online Metric Learning
Authors: Wenbin Li, Yang Gao, Lei Wang, Luping Zhou, Jing Huo, Yinghuan Shi
Date: 2016-09-29 00:00:00
Tasks: Event Detection, Face Verification, Form, Metric Learning, Triplet

To achieve a low computational cost when performing online metric learning
for large-scale data, we present a one-pass closed-form solution namely OPML in
this paper. Typically, the proposed OPML first adopts a one-pass triplet
construction strategy, which aims to use only a very small number of triplets
to approximate the representation ability of whole original triplets obtained
by batch-manner methods. Then, OPML employs a closed-form solution to update
the metric for new coming samples, which leads to a low space (i.e., $O(d)$)
and time (i.e., $O(d^2)$) complexity, where $d$ is the feature dimensionality.
In addition, an extension of OPML (namely COPML) is further proposed to enhance
the robustness when in real case the first several samples come from the same
class (i.e., cold start problem). In the experiments, we have systematically
evaluated our methods (OPML and COPML) on three typical tasks, including UCI
data classification, face verification, and abnormal event detection in videos,
which aims to fully evaluate the proposed methods on different sample number,
different feature dimensionalities and different feature extraction ways (i.e.,
hand-crafted and deeply-learned). The results show that OPML and COPML can
obtain the promising performance with a very low computational cost. Also, the
effectiveness of COPML under the cold start setting is experimentally verified.
Decoupled Asynchronous Proximal Stochastic Gradient Descent with Variance Reduction
Authors: Zhouyuan Huo, Bin Gu, Heng Huang
Date: 2016-09-22 00:00:00

In the era of big data, optimizing large scale machine learning problems
becomes a challenging task and draws significant attention. Asynchronous
optimization algorithms come out as a promising solution. Recently, decoupled
asynchronous proximal stochastic gradient descent (DAP-SGD) is proposed to
minimize a composite function. It is claimed to be able to off-loads the
computation bottleneck from server to workers by allowing workers to evaluate
the proximal operators, therefore, server just need to do element-wise
operations. However, it still suffers from slow convergence rate because of the
variance of stochastic gradient is nonzero. In this paper, we propose a faster
method, decoupled asynchronous proximal stochastic variance reduced gradient
descent method (DAP-SVRG). We prove that our method has linear convergence for
strongly convex problem. Large-scale experiments are also conducted in this
paper, and results demonstrate our theoretical analysis.
Learning Compatibility Across Categories for Heterogeneous Item Recommendation
Authors: Ruining He, Charles Packer, Julian McAuley
Date: 2016-03-31 00:00:00
Tasks: Product Recommendation, Recommendation Systems

Identifying relationships between items is a key task of an online
recommender system, in order to help users discover items that are functionally
complementary or visually compatible. In domains like clothing recommendation,
this task is particularly challenging since a successful system should be
capable of handling a large corpus of items, a huge amount of relationships
among them, as well as the high-dimensional and semantically complicated
features involved. Furthermore, the human notion of "compatibility" to capture
goes beyond mere similarity: For two items to be compatible---whether jeans and
a t-shirt, or a laptop and a charger---they should be similar in some ways, but
systematically different in others.
  In this paper we propose a novel method, Monomer, to learn complicated and
heterogeneous relationships between items in product recommendation settings.
Recently, scalable methods have been developed that address this task by
learning similarity metrics on top of the content of the products involved.
Here our method relaxes the metricity assumption inherent in previous work and
models multiple localized notions of 'relatedness,' so as to uncover ways in
which related items should be systematically similar, and systematically
different. Quantitatively, we show that our system achieves state-of-the-art
performance on large-scale compatibility prediction tasks, especially in cases
where there is substantial heterogeneity between related items. Qualitatively,
we demonstrate that richer notions of compatibility can be learned that go
beyond similarity, and that our model can make effective recommendations of
heterogeneous content.
Universum Learning for Multiclass SVM
Authors: Sauptik Dhar, Naveen Ramakrishnan, Vladimir Cherkassky, Mohak Shah
Date: 2016-09-29 00:00:00
Tasks: Model Selection

We introduce Universum learning for multiclass problems and propose a novel
formulation for multiclass universum SVM (MU-SVM). We also propose a span bound
for MU-SVM that can be used for model selection thereby avoiding resampling.
Empirical results demonstrate the effectiveness of MU-SVM and the proposed
bound.
Proposal for a Leaky-Integrate-Fire Spiking Neuron based on Magneto-Electric Switching of Ferro-magnets
Authors: Akhilesh Jaiswal, Sourjya Roy, Gopalakrishnan Srinivasan, Kaushik Roy
Date: 2016-09-29 00:00:00

The efficiency of the human brain in performing classification tasks has
attracted considerable research interest in brain-inspired neuromorphic
computing. Hardware implementations of a neuromorphic system aims to mimic the
computations in the brain through interconnection of neurons and synaptic
weights. A leaky-integrate-fire (LIF) spiking model is widely used to emulate
the dynamics of neuronal action potentials. In this work, we propose a spin
based LIF spiking neuron using the magneto-electric (ME) switching of
ferro-magnets. The voltage across the ME oxide exhibits a typical
leaky-integrate behavior, which in turn switches an underlying ferro-magnet.
Due to the effect of thermal noise, the ferro-magnet exhibits probabilistic
switching dynamics, which is reminiscent of the stochasticity exhibited by
biological neurons. The energy-efficiency of the ME switching mechanism coupled
with the intrinsic non-volatility of ferro-magnets result in lower energy
consumption, when compared to a CMOS LIF neuron. A device to system-level
simulation framework has been developed to investigate the feasibility of the
proposed LIF neuron for a hand-written digit recognition problem
MPI-FAUN: An MPI-Based Framework for Alternating-Updating Nonnegative Matrix Factorization
Authors: Ramakrishnan Kannan, Grey Ballard, Haesun Park
Date: 2016-09-28 00:00:00
Tasks: Community Detection

Non-negative matrix factorization (NMF) is the problem of determining two
non-negative low rank factors $W$ and $H$, for the given input matrix $A$, such
that $A \approx W H$. NMF is a useful tool for many applications in different
domains such as topic modeling in text mining, background separation in video
analysis, and community detection in social networks. Despite its popularity in
the data mining community, there is a lack of efficient parallel algorithms to
solve the problem for big data sets.
  The main contribution of this work is a new, high-performance parallel
computational framework for a broad class of NMF algorithms that iteratively
solves alternating non-negative least squares (NLS) subproblems for $W$ and
$H$. It maintains the data and factor matrices in memory (distributed across
processors), uses MPI for interprocessor communication, and, in the dense case,
provably minimizes communication costs (under mild assumptions). The framework
is flexible and able to leverage a variety of NMF and NLS algorithms, including
Multiplicative Update, Hierarchical Alternating Least Squares, and Block
Principal Pivoting. Our implementation allows us to benchmark and compare
different algorithms on massive dense and sparse data matrices of size that
spans for few hundreds of millions to billions. We demonstrate the scalability
of our algorithm and compare it with baseline implementations, showing
significant performance improvements. The code and the datasets used for
conducting the experiments are available online.
SSSC-AM: A Unified Framework for Video Co-Segmentation by Structured Sparse Subspace Clustering with Appearance and Motion Features
Authors: Junlin Yao, Frank Nielsen
Date: 2016-03-14 00:00:00
Tasks: Clustering, Segmentation

Video co-segmentation refers to the task of jointly segmenting common objects
appearing in a given group of videos. In practice, high-dimensional data such
as videos can be conceptually thought as being drawn from a union of subspaces
corresponding to categories rather than from a smooth manifold. Therefore,
segmenting data into respective subspaces --- subspace clustering --- finds
widespread applications in computer vision, including co-segmentation.
State-of-the-art methods via subspace clustering seek to solve the problem in
two steps:
  First, an affinity matrix is built from data, with appearance features or
motion patterns. Second, the data are segmented by applying spectral clustering
to the affinity matrix. However, this process is insufficient to obtain an
optimal solution since it does not take into account the {\em interdependence}
of the affinity matrix with the segmentation. In this work, we present a novel
unified video co-segmentation framework inspired by the recent Structured
Sparse Subspace Clustering ($\mathrm{S^{3}C}$) based on the {\em
self-expressiveness} model. Our method yields more consistent segmentation
results. In order to improve the detectability of motion features with missing
trajectories due to occlusion or tracked points moving out of frames, we add an
extra-dimensional signature to the motion trajectories. Moreover, we
reformulate the $\mathrm{S^{3}C}$ algorithm by adding the affine subspace
constraint in order to make it more suitable to segment rigid motions lying in
affine subspaces of dimension at most $3$. Our experiments on MOViCS dataset
show that our framework achieves the highest overall performance among baseline
algorithms and demonstrate its robustness to heavy noise.
A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image
Authors: Ruiqi Zhao, Yan Wang, Aleix Martinez
Date: 2016-09-28 00:00:00
Tasks: 3D Face Alignment, 3D Shape Reconstruction, Data Augmentation, Face Alignment

Three-dimensional shape reconstruction of 2D landmark points on a single
image is a hallmark of human vision, but is a task that has been proven
difficult for computer vision algorithms. We define a feed-forward deep neural
network algorithm that can reconstruct 3D shapes from 2D landmark points almost
perfectly (i.e., with extremely small reconstruction errors), even when these
2D landmarks are from a single image. Our experimental results show an
improvement of up to two-fold over state-of-the-art computer vision algorithms;
3D shape reconstruction of human faces is given at a reconstruction error <
.004, cars at .0022, human bodies at .022, and highly-deformable flags at an
error of .0004. Our algorithm was also a top performer at the 2016 3D Face
Alignment in the Wild Challenge competition (done in conjunction with the
European Conference on Computer Vision, ECCV) that required the reconstruction
of 3D face shape from a single image. The derived algorithm can be trained in a
couple hours and testing runs at more than 1, 000 frames/s on an i7 desktop. We
also present an innovative data augmentation approach that allows us to train
the system efficiently with small number of samples. And the system is robust
to noise (e.g., imprecise landmark points) and missing data (e.g., occluded or
undetected landmark points).
Learning to Push by Grasping: Using multiple tasks for effective learning
Authors: Lerrel Pinto, Abhinav Gupta
Date: 2016-09-28 00:00:00
Tasks: Multi-Task Learning

Recently, end-to-end learning frameworks are gaining prevalence in the field
of robot control. These frameworks input states/images and directly predict the
torques or the action parameters. However, these approaches are often critiqued
due to their huge data requirements for learning a task. The argument of the
difficulty in scalability to multiple tasks is well founded, since training
these tasks often require hundreds or thousands of examples. But do end-to-end
approaches need to learn a unique model for every task? Intuitively, it seems
that sharing across tasks should help since all tasks require some common
understanding of the environment. In this paper, we attempt to take the next
step in data-driven end-to-end learning frameworks: move from the realm of
task-specific models to joint learning of multiple robot tasks. In an
astonishing result we show that models with multi-task learning tend to perform
better than task-specific models trained with same amounts of data. For
example, a deep-network learned with 2.5K grasp and 2.5K push examples performs
better on grasping than a network trained on 5K grasp examples.
Psychologically Motivated Text Mining
Authors: Ekaterina Shutova, Patricia Lichtenstein
Date: 2016-09-28 00:00:00

Natural language processing techniques are increasingly applied to identify
social trends and predict behavior based on large text collections. Existing
methods typically rely on surface lexical and syntactic information. Yet,
research in psychology shows that patterns of human conceptualisation, such as
metaphorical framing, are reliable predictors of human expectations and
decisions. In this paper, we present a method to learn patterns of metaphorical
framing from large text collections, using statistical techniques. We apply the
method to data in three different languages and evaluate the identified
patterns, demonstrating their psychological validity.
Deep Architectures for Face Attributes
Authors: Tobi Baumgartner, Jack Culpepper
Date: 2016-09-28 00:00:00
Tasks: Attribute, Classification, General Classification

We train a deep convolutional neural network to perform identity
classification using a new dataset of public figures annotated with age,
gender, ethnicity and emotion labels, and then fine-tune it for attribute
classification. An optimal sharing pattern of computational resources within
this network is determined by experiment, requiring only 1 G flops to produce
all predictions. Rather than fine-tune by relearning weights in one additional
layer after the penultimate layer of the identity network, we try several
different depths for each attribute. We find that prediction of age and emotion
is improved by fine-tuning from earlier layers onward, presumably because
deeper layers are progressively invariant to non-identity related changes in
the input.
Minimum Density Hyperplanes
Authors: Nicos G. Pavlidis, David P. Hofmeyr, Sotiris K. Tasoulis
Date: 2015-07-15 00:00:00
Tasks: Classification, Clustering, General Classification

Associating distinct groups of objects (clusters) with contiguous regions of
high probability density (high-density clusters), is central to many
statistical and machine learning approaches to the classification of unlabelled
data. We propose a novel hyperplane classifier for clustering and
semi-supervised classification which is motivated by this objective. The
proposed minimum density hyperplane minimises the integral of the empirical
probability density function along it, thereby avoiding intersection with high
density clusters. We show that the minimum density and the maximum margin
hyperplanes are asymptotically equivalent, thus linking this approach to
maximum margin clustering and semi-supervised support vector classifiers. We
propose a projection pursuit formulation of the associated optimisation problem
which allows us to find minimum density hyperplanes efficiently in practice,
and evaluate its performance on a range of benchmark datasets. The proposed
approach is found to be very competitive with state of the art methods for
clustering and semi-supervised classification.
Unsupervised Neural Hidden Markov Models
Authors: Ke Tran, Yonatan Bisk, Ashish Vaswani, Daniel Marcu, Kevin Knight
Date: 2016-09-28 00:00:00
Proceeding: WS 2016 11
Tasks: TAG

In this work, we present the first results for neuralizing an Unsupervised
Hidden Markov Model. We evaluate our approach on tag in- duction. Our approach
outperforms existing generative models and is competitive with the
state-of-the-art though with a simpler model easily extended to include
additional context.