pwc-archive/papers-with-abstracts huggingface dataset:
Compression Artifacts Reduction by a Deep Convolutional Network
Authors: Chao Dong, Yubin Deng, Chen Change Loy, Xiaoou Tang
Date: 2015-04-27 00:00:00
Proceeding: ICCV 2015 12
Tasks: Blocking, Denoising, JPEG Artifact Correction, Transfer Learning

Lossy compression introduces complex compression artifacts, particularly the
blocking artifacts, ringing effects and blurring. Existing algorithms either
focus on removing blocking artifacts and produce blurred output, or restores
sharpened images that are accompanied with ringing effects. Inspired by the
deep convolutional networks (DCN) on super-resolution, we formulate a compact
and efficient network for seamless attenuation of different compression
artifacts. We also demonstrate that a deeper model can be effectively trained
with the features learned in a shallow network. Following a similar "easy to
hard" idea, we systematically investigate several practical transfer settings
and show the effectiveness of transfer learning in low-level vision problems.
Our method shows superior performance than the state-of-the-arts both on the
benchmark datasets and the real-world use case (i.e. Twitter). In addition, we
show that our method can be applied as pre-processing to facilitate other
low-level vision routines when they take compressed images as input.
Concept Extraction to Identify Adverse Drug Reactions in Medical Forums: A Comparison of Algorithms
Authors: Alejandro Metke-Jimenez, Sarvnaz Karimi
Date: 2015-04-27 00:00:00
Tasks: BIG-bench Machine Learning, Pharmacovigilance

Social media is becoming an increasingly important source of information to
complement traditional pharmacovigilance methods. In order to identify signals
of potential adverse drug reactions, it is necessary to first identify medical
concepts in the social media text. Most of the existing studies use
dictionary-based methods which are not evaluated independently from the overall
signal detection task.
  We compare different approaches to automatically identify and normalise
medical concepts in consumer reviews in medical forums. Specifically, we
implement several dictionary-based methods popular in the relevant literature,
as well as a method we suggest based on a state-of-the-art machine learning
method for entity recognition. MetaMap, a popular biomedical concept extraction
tool, is used as a baseline. Our evaluations were performed in a controlled
setting on a common corpus which is a collection of medical forum posts
annotated with concepts and linked to controlled vocabularies such as MedDRA
and SNOMED CT.
  To our knowledge, our study is the first to systematically examine the effect
of popular concept extraction methods in the area of signal detection for
adverse reactions. We show that the choice of algorithm or controlled
vocabulary has a significant impact on concept extraction, which will impact
the overall signal detection process. We also show that our proposed machine
learning approach significantly outperforms all the other methods in
identification of both adverse reactions and drugs, even when trained with a
relatively small set of annotated text.
Detection and Recognition of Malaysian Special License Plate Based On SIFT Features
Authors: Hooi Sin Ng, Yong Haur Tay, Kim Meng Liang, Hamam Mokayed, Hock Woon Hon
Date: 2015-04-27 00:00:00
Tasks: Clustering, License Plate Recognition

Automated car license plate recognition systems are developed and applied for
purpose of facilitating the surveillance, law enforcement, access control and
intelligent transportation monitoring with least human intervention. In this
paper, an algorithm based on SIFT feature points clustering and matching is
proposed to address the issue of recognizing Malaysian special plates. These
special plates do not follow the format of standard car plates as they may
contain italic, cursive, connected and small letters. The algorithm is tested
with 150 Malaysian special plate images under different environment and the
promising experimental results demonstrate that the proposed algorithm is
relatively robust.
Neural Responding Machine for Short-Text Conversation
Authors: Lifeng Shang, Zhengdong Lu, Hang Li
Date: 2015-03-09 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Decoder, Retrieval, Short-Text Conversation

We propose Neural Responding Machine (NRM), a neural network-based response
generator for Short-Text Conversation. NRM takes the general encoder-decoder
framework: it formalizes the generation of response as a decoding process based
on the latent representation of the input text, while both encoding and
decoding are realized with recurrent neural networks (RNN). The NRM is trained
with a large amount of one-round conversation data collected from a
microblogging service. Empirical study shows that NRM can generate
grammatically correct and content-wise appropriate responses to over 75% of the
input text, outperforming state-of-the-arts in the same setting, including
retrieval-based and SMT-based models.
Linear Spatial Pyramid Matching Using Non-convex and non-negative Sparse Coding for Image Classification
Authors: Chengqiang Bao, Liangtian He, Yi-Lun Wang
Date: 2015-04-27 00:00:00
Tasks: General Classification, image-classification, Image Classification

Recently sparse coding have been highly successful in image classification
mainly due to its capability of incorporating the sparsity of image
representation. In this paper, we propose an improved sparse coding model based
on linear spatial pyramid matching(SPM) and Scale Invariant Feature Transform
(SIFT ) descriptors. The novelty is the simultaneous non-convex and
non-negative characters added to the sparse coding model. Our numerical
experiments show that the improved approach using non-convex and non-negative
sparse coding is superior than the original ScSPM[1] on several typical
databases.
Bayesian kernel-based system identification with quantized output data
Authors: Giulio Bottegal, Gianluigi Pillonetto, Håkan Hjalmarsson
Date: 2015-04-26 00:00:00

In this paper we introduce a novel method for linear system identification
with quantized output data. We model the impulse response as a zero-mean
Gaussian process whose covariance (kernel) is given by the recently proposed
stable spline kernel, which encodes information on regularity and exponential
stability. This serves as a starting point to cast our system identification
problem into a Bayesian framework. We employ Markov Chain Monte Carlo (MCMC)
methods to provide an estimate of the system. In particular, we show how to
design a Gibbs sampler which quickly converges to the target distribution.
Numerical simulations show a substantial improvement in the accuracy of the
estimates over state-of-the-art kernel-based methods when employed in
identification of systems with quantized data.
Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review
Authors: Gordon V. Cormack, Maura R. Grossman
Date: 2015-04-26 00:00:00
Tasks: Active Learning

We enhance the autonomy of the continuous active learning method shown by
Cormack and Grossman (SIGIR 2014) to be effective for technology-assisted
review, in which documents from a collection are retrieved and reviewed, using
relevance feedback, until substantially all of the relevant documents have been
reviewed. Autonomy is enhanced through the elimination of topic-specific and
dataset-specific tuning parameters, so that the sole input required by the user
is, at the outset, a short query, topic description, or single relevant
document; and, throughout the review, ongoing relevance assessments of the
retrieved documents. We show that our enhancements consistently yield superior
results to Cormack and Grossman's version of continuous active learning, and
other methods, not only on average, but on the vast majority of topics from
four separate sets of tasks: the legal datasets examined by Cormack and
Grossman, the Reuters RCV1-v2 subject categories, the TREC 6 AdHoc task, and
the construction of the TREC 2002 filtering test collection.
Fast Dictionary Matching for Content-based Image Retrieval
Authors: Patryk Najgebauer, Janusz Rygal, Tomasz Nowak, Jakub Romanowski, Leszek Rutkowski, Sviatoslav Voloshynovskiy, Rafal Scherer
Date: 2015-04-26 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

This paper describes a method for searching for common sets of descriptors
between collections of images. The presented method operates on local interest
keypoints, which are generated using the SURF algorithm. The use of a
dictionary of descriptors allowed achieving good performance of the
content-based image retrieval. The method can be used to initially determine a
set of similar pairs of keypoints between images. For this purpose, we use a
certain level of tolerance between values of descriptors, as values of feature
descriptors are almost never equal but similar between different images. After
that, the method compares the structure of rotation and location of interest
points in one image with the point structure in other images. Thus, we were
able to find similar areas in images and determine the level of similarity
between them, even when images contain different scenes.
When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization
Authors: Fernando G. Lobo, Mosab Bazargani
Date: 2015-04-26 00:00:00
Tasks: Diversity

It has been shown in the past that a multistart hillclimbing strategy compares favourably to a standard genetic algorithm with respect to solving instances of the multimodal problem generator. We extend that work and verify if the utilization of diversity preservation techniques in the genetic algorithm changes the outcome of the comparison. We do so under two scenarios: (1) when the goal is to find the global optimum, (2) when the goal is to find all optima. A mathematical analysis is performed for the multistart hillclimbing algorithm and a through empirical study is conducted for solving instances of the multimodal problem generator with increasing number of optima, both with the hillclimbing strategy as well as with genetic algorithms with niching. Although niching improves the performance of the genetic algorithm, it is still inferior to the multistart hillclimbing strategy on this class of problems. An idealized niching strategy is also presented and it is argued that its performance should be close to a lower bound of what any evolutionary algorithm can do on this class of problems.
Unregularized Online Learning Algorithms with General Loss Functions
Authors: Yiming Ying, Ding-Xuan Zhou
Date: 2015-03-02 00:00:00

In this paper, we consider unregularized online learning algorithms in a
Reproducing Kernel Hilbert Spaces (RKHS). Firstly, we derive explicit
convergence rates of the unregularized online learning algorithms for
classification associated with a general gamma-activating loss (see Definition
1 in the paper). Our results extend and refine the results in Ying and Pontil
(2008) for the least-square loss and the recent result in Bach and Moulines
(2011) for the loss function with a Lipschitz-continuous gradient. Moreover, we
establish a very general condition on the step sizes which guarantees the
convergence of the last iterate of such algorithms. Secondly, we establish, for
the first time, the convergence of the unregularized pairwise learning
algorithm with a general loss function and derive explicit rates under the
assumption of polynomially decaying step sizes. Concrete examples are used to
illustrate our main results. The main techniques are tools from convex
analysis, refined inequalities of Gaussian averages, and an induction approach.
Maximum a Posteriori Estimation by Search in Probabilistic Programs
Authors: David Tolpin, Frank Wood
Date: 2015-04-26 00:00:00

We introduce an approximate search algorithm for fast maximum a posteriori
probability estimation in probabilistic programs, which we call Bayesian ascent
Monte Carlo (BaMC). Probabilistic programs represent probabilistic models with
varying number of mutually dependent finite, countable, and continuous random
variables. BaMC is an anytime MAP search algorithm applicable to any
combination of random variables and dependencies. We compare BaMC to other MAP
estimation algorithms and show that BaMC is faster and more robust on a range
of probabilistic models.
Comparison of Training Methods for Deep Neural Networks
Authors: Patrick O. Glauner
Date: 2015-04-26 00:00:00

This report describes the difficulties of training neural networks and in
particular deep neural networks. It then provides a literature review of
training methods for deep neural networks, with a focus on pre-training. It
focuses on Deep Belief Networks composed of Restricted Boltzmann Machines and
Stacked Autoencoders and provides an outreach on further and alternative
approaches. It also includes related practical recommendations from the
literature on training them. In the second part, initial experiments using some
of the covered methods are performed on two databases. In particular,
experiments are performed on the MNIST hand-written digit dataset and on facial
emotion data from a Kaggle competition. The results are discussed in the
context of results reported in other research papers. An error rate lower than
the best contribution to the Kaggle competition is achieved using an optimized
Stacked Autoencoder.
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion
Authors: Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
Date: 2015-04-26 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

In this paper, we develop a relative error bound for nuclear norm regularized matrix completion, with the focus on the completion of full-rank matrices. Under the assumption that the top eigenspaces of the target matrix are incoherent, we derive a relative upper bound for recovering the best low-rank approximation of the unknown matrix. Although multiple works have been devoted to analyzing the recovery error of full-rank matrix completion, their error bounds are usually additive, making it impossible to obtain the perfect recovery case and more generally difficult to leverage the skewed distribution of eigenvalues. Our analysis is built upon the optimality condition of the regularized formulation and existing guarantees for low-rank matrix completion. To the best of our knowledge, this is the first relative bound that has been proved for the regularized formulation of matrix completion.
Overlapping Community Detection by Online Cluster Aggregation
Authors: Mark Kozdoba, Shie Mannor
Date: 2015-04-26 00:00:00
Tasks: Community Detection

We present a new online algorithm for detecting overlapping communities. The
main ingredients are a modification of an online k-means algorithm and a new
approach to modelling overlap in communities. An evaluation on large benchmark
graphs shows that the quality of discovered communities compares favorably to
several methods in the recent literature, while the running time is
significantly improved.
Viewpoints and Keypoints
Authors: Shubham Tulsiani, Jitendra Malik
Date: 2014-11-22 00:00:00
Proceeding: CVPR 2015 6
Tasks: Keypoint Detection, Pose Estimation

We characterize the problem of pose estimation for rigid objects in terms of
determining viewpoint to explain coarse pose and keypoint prediction to capture
the finer details. We address both these tasks in two different settings - the
constrained setting with known bounding boxes and the more challenging
detection setting where the aim is to simultaneously detect and correctly
estimate pose of objects. We present Convolutional Neural Network based
architectures for these and demonstrate that leveraging viewpoint estimates can
substantially improve local appearance based keypoint predictions. In addition
to achieving significant improvements over state-of-the-art in the above tasks,
we analyze the error modes and effect of object characteristics on performance
to guide future efforts towards this goal.
Spy vs. Spy: Rumor Source Obfuscation
Authors: Giulia Fanti, Peter Kairouz, Sewoong Oh, Pramod Viswanath
Date: 2014-12-29 00:00:00

Anonymous messaging platforms, such as Secret and Whisper, have emerged as
important social media for sharing one's thoughts without the fear of being
judged by friends, family, or the public. Further, such anonymous platforms are
crucial in nations with authoritarian governments; the right to free expression
and sometimes the personal safety of the author of the message depend on
anonymity. Whether for fear of judgment or personal endangerment, it is crucial
to keep anonymous the identity of the user who initially posted a sensitive
message. In this paper, we consider an adversary who observes a snapshot of the
spread of a message at a certain time. Recent advances in rumor source
detection shows that the existing messaging protocols are vulnerable against
such an adversary. We introduce a novel messaging protocol, which we call
adaptive diffusion, and show that it spreads the messages fast and achieves a
perfect obfuscation of the source when the underlying contact network is an
infinite regular tree: all users with the message are nearly equally likely to
have been the origin of the message. Experiments on a sampled Facebook network
show that it effectively hides the location of the source even when the graph
is finite, irregular and has cycles. We further consider a stronger adversarial
model where a subset of colluding users track the reception of messages. We
show that the adaptive diffusion provides a strong protection of the anonymity
of the source even under this scenario.
Exploring Transfer Function Nonlinearity in Echo State Networks
Authors: Alireza Goudarzi, Alireza Shabani, Darko Stefanovic
Date: 2015-02-16 00:00:00
Tasks: Time Series Analysis, Time Series Prediction

Supralinear and sublinear pre-synaptic and dendritic integration is
considered to be responsible for nonlinear computation power of biological
neurons, emphasizing the role of nonlinear integration as opposed to nonlinear
output thresholding. How, why, and to what degree the transfer function
nonlinearity helps biologically inspired neural network models is not fully
understood. Here, we study these questions in the context of echo state
networks (ESN). ESN is a simple neural network architecture in which a fixed
recurrent network is driven with an input signal, and the output is generated
by a readout layer from the measurements of the network states. ESN
architecture enjoys efficient training and good performance on certain
signal-processing tasks, such as system identification and time series
prediction. ESN performance has been analyzed with respect to the connectivity
pattern in the network structure and the input bias. However, the effects of
the transfer function in the network have not been studied systematically.
Here, we use an approach tanh on the Taylor expansion of a frequently used
transfer function, the hyperbolic tangent function, to systematically study the
effect of increasing nonlinearity of the transfer function on the memory,
nonlinear capacity, and signal processing performance of ESN. Interestingly, we
find that a quadratic approximation is enough to capture the computational
power of ESN with tanh function. The results of this study apply to both
software and hardware implementation of ESN.
Hierarchical Composition of Memristive Networks for Real-Time Computing
Authors: Jens Bürger, Alireza Goudarzi, Darko Stefanovic, Christof Teuscher
Date: 2015-04-11 00:00:00

Advances in materials science have led to physical instantiations of
self-assembled networks of memristive devices and demonstrations of their
computational capability through reservoir computing. Reservoir computing is an
approach that takes advantage of collective system dynamics for real-time
computing. A dynamical system, called a reservoir, is excited with a
time-varying signal and observations of its states are used to reconstruct a
desired output signal. However, such a monolithic assembly limits the
computational power due to signal interdependency and the resulting correlated
readouts. Here, we introduce an approach that hierarchically composes a set of
interconnected memristive networks into a larger reservoir. We use signal
amplification and restoration to reduce reservoir state correlation, which
improves the feature extraction from the input signals. Using the same number
of output signals, such a hierarchical composition of heterogeneous small
networks outperforms monolithic memristive networks by at least 20% on waveform
generation tasks. On the NARMA-10 task, we reduce the error by up to a factor
of 2 compared to homogeneous reservoirs with sigmoidal neurons, whereas single
memristive networks are unable to produce the correct result. Hierarchical
composition is key for solving more complex tasks with such novel nano-scale
hardware.
Product Reservoir Computing: Time-Series Computation with Multiplicative Neurons
Authors: Alireza Goudarzi, Alireza Shabani, Darko Stefanovic
Date: 2015-02-03 00:00:00
Tasks: Time Series, Time Series Analysis

Echo state networks (ESN), a type of reservoir computing (RC) architecture,
are efficient and accurate artificial neural systems for time series processing
and learning. An ESN consists of a core of recurrent neural networks, called a
reservoir, with a small number of tunable parameters to generate a
high-dimensional representation of an input, and a readout layer which is
easily trained using regression to produce a desired output from the reservoir
states. Certain computational tasks involve real-time calculation of high-order
time correlations, which requires nonlinear transformation either in the
reservoir or the readout layer. Traditional ESN employs a reservoir with
sigmoid or tanh function neurons. In contrast, some types of biological neurons
obey response curves that can be described as a product unit rather than a sum
and threshold. Inspired by this class of neurons, we introduce a RC
architecture with a reservoir of product nodes for time series computation. We
find that the product RC shows many properties of standard ESN such as
short-term memory and nonlinear capacity. On standard benchmarks for chaotic
prediction tasks, the product RC maintains the performance of a standard
nonlinear ESN while being more amenable to mathematical analysis. Our study
provides evidence that such networks are powerful in highly nonlinear tasks
owing to high-order statistics generated by the recurrent product node
reservoir.
Hypercolumns for Object Segmentation and Fine-grained Localization
Authors: Bharath Hariharan, Pablo Arbeláez, Ross Girshick, Jitendra Malik
Date: 2014-11-21 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, Semantic Segmentation

Recognition algorithms based on convolutional networks (CNNs) typically use
the output of the last layer as feature representation. However, the
information in this layer may be too coarse to allow precise localization. On
the contrary, earlier layers may be precise in localization but will not
capture semantics. To get the best of both worlds, we define the hypercolumn at
a pixel as the vector of activations of all CNN units above that pixel. Using
hypercolumns as pixel descriptors, we show results on three fine-grained
localization tasks: simultaneous detection and segmentation[22], where we
improve state-of-the-art from 49.7[22] mean AP^r to 60.0, keypoint
localization, where we get a 3.3 point boost over[20] and part labeling, where
we show a 6.6 point gain over a strong baseline.
SIFT Vs SURF: Quantifying the Variation in Transformations
Authors: Siddharth Srivastava
Date: 2015-04-25 00:00:00

This paper studies the robustness of SIFT and SURF against different image
transforms (rigid body, similarity, affine and projective) by quantitatively
analyzing the variations in the extent of transformations. Previous studies
have been comparing the two techniques on absolute transformations rather than
the specific amount of deformation caused by the transformation. The paper
establishes an exhaustive empirical analysis of such deformations and matching
capability of SIFT and SURF with variations in matching parameters and the
amount of tolerance. This is helpful in choosing the specific use case for
applying these techniques.