pwc-archive/papers-with-abstracts huggingface dataset:
WEPSAM: Weakly Pre-Learnt Saliency Model
Authors: Avisek Lahiri, Sourya Roy, Anirban Santara, Pabitra Mitra, Prabir Kumar Biswas
Date: 2016-05-03 00:00:00
Tasks: model, Prediction, Saliency Detection, Saliency Prediction

Visual saliency detection tries to mimic human vision psychology which
concentrates on sparse, important areas in natural image. Saliency prediction
research has been traditionally based on low level features such as contrast,
edge, etc. Recent thrust in saliency prediction research is to learn high level
semantics using ground truth eye fixation datasets. In this paper we present,
WEPSAM : Weakly Pre-Learnt Saliency Model as a pioneering effort of using
domain specific pre-learing on ImageNet for saliency prediction using a light
weight CNN architecture. The paper proposes a two step hierarchical learning,
in which the first step is to develop a framework for weakly pre-training on a
large scale dataset such as ImageNet which is void of human eye fixation maps.
The second step refines the pre-trained model on a limited set of ground truth
fixations. Analysis of loss on iSUN and SALICON datasets reveal that
pre-trained network converges much faster compared to randomly initialized
network. WEPSAM also outperforms some recent state-of-the-art saliency
prediction models on the challenging MIT300 dataset.
Online Machine Learning Techniques for Predicting Operator Performance
Authors: Ahmet Anil Pala
Date: 2016-05-03 00:00:00
Tasks: BIG-bench Machine Learning

This thesis explores a number of online machine learning algorithms. From a
theoret- ical perspective, it assesses their employability for a particular
function approximation problem where the analytical models fall short.
Furthermore, it discusses the applica- tion of theoretically suitable learning
algorithms to the function approximation problem at hand through an efficient
implementation that exploits various computational and mathematical shortcuts.
Finally, this thesis work evaluates the implemented learning algorithms
according to various evaluation criteria through rigorous testing.
Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Experts
Authors: Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar
Date: 2016-05-03 00:00:00
Tasks: Transfer Learning

We develop a personalized real time risk scoring algorithm that provides
timely and granular assessments for the clinical acuity of ward patients based
on their (temporal) lab tests and vital signs. Heterogeneity of the patients
population is captured via a hierarchical latent class model. The proposed
algorithm aims to discover the number of latent classes in the patients
population, and train a mixture of Gaussian Process (GP) experts, where each
expert models the physiological data streams associated with a specific class.
Self-taught transfer learning is used to transfer the knowledge of latent
classes learned from the domain of clinically stable patients to the domain of
clinically deteriorating patients. For new patients, the posterior beliefs of
all GP experts about the patient's clinical status given her physiological data
stream are computed, and a personalized risk score is evaluated as a weighted
average of those beliefs, where the weights are learned from the patient's
hospital admission information. Experiments on a heterogeneous cohort of 6,313
patients admitted to Ronald Regan UCLA medical center show that our risk score
outperforms the currently deployed risk scores, such as MEWS and Rothman
scores.
Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video
Authors: Jing Zhou, Xiaopeng Hong, Fei Su, Guoying Zhao
Date: 2016-05-03 00:00:00
Tasks: Pain Intensity Regression, regression

Automatic pain intensity estimation possesses a significant position in
healthcare and medical field. Traditional static methods prefer to extract
features from frames separately in a video, which would result in unstable
changes and peaks among adjacent frames. To overcome this problem, we propose a
real-time regression framework based on the recurrent convolutional neural
network for automatic frame-level pain intensity estimation. Given vector
sequences of AAM-warped facial images, we used a sliding-window strategy to
obtain fixed-length input samples for the recurrent network. We then carefully
design the architecture of the recurrent network to output continuous-valued
pain intensity. The proposed end-to-end pain intensity regression framework can
predict the pain intensity of each frame by considering a sufficiently large
historical frames while limiting the scale of the parameters within the model.
Our method achieves promising results regarding both accuracy and running speed
on the published UNBC-McMaster Shoulder Pain Expression Archive Database.
Improving Image Captioning by Concept-based Sentence Reranking
Authors: Xirong Li, Qin Jin
Date: 2016-05-03 00:00:00
Tasks: Image Captioning, Language Modeling, Language Modelling, Reranking, Sentence

This paper describes our winning entry in the ImageCLEF 2015 image sentence
generation task. We improve Google's CNN-LSTM model by introducing
concept-based sentence reranking, a data-driven approach which exploits the
large amounts of concept-level annotations on Flickr. Different from previous
usage of concept detection that is tailored to specific image captioning
models, the propose approach reranks predicted sentences in terms of their
matches with detected concepts, essentially treating the underlying model as a
black box. This property makes the approach applicable to a number of existing
solutions. We also experiment with fine tuning on the deep language model,
which improves the performance further. Scoring METEOR of 0.1875 on the
ImageCLEF 2015 test set, our system outperforms the runner-up (METEOR of
0.1687) with a clear margin.
Obstacle evasion using fuzzy logic in a sliding blades problem environment
Authors: Shadrack Kimutai
Date: 2016-05-03 00:00:00
Tasks: Navigate

This paper discusses obstacle avoidance using fuzzy logic and shortest path
algorithm. This paper also introduces the sliding blades problem and
illustrates how a drone can navigate itself through the swinging blade
obstacles while tracing a semi-optimal path and also maintaining constant
velocity
Temporal Clustering of Time Series via Threshold Autoregressive Models: Application to Commodity Prices
Authors: Sipan Aslan, Ceylan Yozgatligil, Cem Iyigun
Date: 2016-05-03 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

This study aimed to find temporal clusters for several commodity prices using
the threshold non-linear autoregressive model. It is expected that the process
of determining the commodity groups that are time-dependent will advance the
current knowledge about the dynamics of co-moving and coherent prices, and can
serve as a basis for multivariate time series analyses. The clustering of
commodity prices was examined using the proposed clustering approach based on
time series models to incorporate the time varying properties of price series
into the clustering scheme. Accordingly, the primary aim in this study was
grouping time series according to the similarity between their Data Generating
Mechanisms (DGMs) rather than comparing pattern similarities in the time series
traces. The approximation to the DGM of each series was accomplished using
threshold autoregressive models, which are recognized for their ability to
represent nonlinear features in time series, such as abrupt changes,
time-irreversibility and regime-shifting behavior. Through the use of the
proposed approach, one can determine and monitor the set of co-moving time
series variables across the time dimension. Furthermore, generating a time
varying commodity price index and sub-indexes can become possible.
Consequently, we conducted a simulation study to assess the effectiveness of
the proposed clustering approach and the results are presented for both the
simulated and real data sets.
Spatially Aware Dictionary Learning and Coding for Fossil Pollen Identification
Authors: Shu Kong, Surangi Punyasena, Charless Fowlkes
Date: 2016-05-03 00:00:00
Tasks: Dictionary Learning, General Classification

We propose a robust approach for performing automatic species-level
recognition of fossil pollen grains in microscopy images that exploits both
global shape and local texture characteristics in a patch-based matching
methodology. We introduce a novel criteria for selecting meaningful and
discriminative exemplar patches. We optimize this function during training
using a greedy submodular function optimization framework that gives a
near-optimal solution with bounded approximation error. We use these selected
exemplars as a dictionary basis and propose a spatially-aware sparse coding
method to match testing images for identification while maintaining global
shape correspondence. To accelerate the coding process for fast matching, we
introduce a relaxed form that uses spatially-aware soft-thresholding during
coding. Finally, we carry out an experimental study that demonstrates the
effectiveness and efficiency of our exemplar selection and classification
mechanisms, achieving $86.13\%$ accuracy on a difficult fine-grained species
classification task distinguishing three types of fossil spruce pollen.
Exact post-selection inference, with application to the lasso
Authors: Jason D. Lee, Dennis L. Sun, Yuekai Sun, Jonathan E. Taylor
Date: 2013-11-25 00:00:00
Tasks: Model Selection, valid

We develop a general approach to valid inference after model selection. At
the core of our framework is a result that characterizes the distribution of a
post-selection estimator conditioned on the selection event. We specialize the
approach to model selection by the lasso to form valid confidence intervals for
the selected coefficients and test whether all relevant variables have been
included in the model.
Automatic Identification of Retinal Arteries and Veins in Fundus Images using Local Binary Patterns
Authors: Nima Hatami, Michael Goldbaum
Date: 2016-05-03 00:00:00
Tasks: Classification, General Classification

Artery and vein (AV) classification of retinal images is a key to necessary
tasks, such as automated measurement of arteriolar-to-venular diameter ratio
(AVR). This paper comprehensively reviews the state-of-the art in AV
classification methods. To improve on previous methods, a new Local Bi- nary
Pattern-based method (LBP) is proposed. Beside its simplicity, LBP is robust
against low contrast and low quality fundus images; and it helps the process by
including additional AV texture and shape information. Experimental results
compare the performance of the new method with the state-of-the art; and also
methods with different feature extraction and classification schemas.
Efficient Distributed Estimation of Inverse Covariance Matrices
Authors: Jesús Arroyo, Elizabeth Hou
Date: 2016-05-03 00:00:00
Tasks: Model Selection

In distributed systems, communication is a major concern due to issues such
as its vulnerability or efficiency. In this paper, we are interested in
estimating sparse inverse covariance matrices when samples are distributed into
different machines. We address communication efficiency by proposing a method
where, in a single round of communication, each machine transfers a small
subset of the entries of the inverse covariance matrix. We show that, with this
efficient distributed method, the error rates can be comparable with estimation
in a non-distributed setting, and correct model selection is still possible.
Practical performance is shown through simulations.
EIE: Efficient Inference Engine on Compressed Deep Neural Network
Authors: Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, William J. Dally
Date: 2016-02-04 00:00:00
Tasks: CPU, GPU

State-of-the-art deep neural networks (DNNs) have hundreds of millions of
connections and are both computationally and memory intensive, making them
difficult to deploy on embedded systems with limited hardware resources and
power budgets. While custom hardware helps the computation, fetching weights
from DRAM is two orders of magnitude more expensive than ALU operations, and
dominates the required power.
  Previously proposed 'Deep Compression' makes it possible to fit large DNNs
(AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by
pruning the redundant connections and having multiple connections share the
same weight. We propose an energy efficient inference engine (EIE) that
performs inference on this compressed network model and accelerates the
resulting sparse matrix-vector multiplication with weight sharing. Going from
DRAM to SRAM gives EIE 120x energy saving; Exploiting sparsity saves 10x;
Weight sharing gives 8x; Skipping zero activations from ReLU saves another 3x.
Evaluated on nine DNN benchmarks, EIE is 189x and 13x faster when compared to
CPU and GPU implementations of the same DNN without compression. EIE has a
processing power of 102GOPS/s working directly on a compressed network,
corresponding to 3TOPS/s on an uncompressed network, and processes FC layers of
AlexNet at 1.88x10^4 frames/sec with a power dissipation of only 600mW. It is
24,000x and 3,400x more energy efficient than a CPU and GPU respectively.
Compared with DaDianNao, EIE has 2.9x, 19x and 3x better throughput, energy
efficiency and area efficiency.
A Two-Stage Shape Retrieval (TSR) Method with Global and Local Features
Authors: Xiaqing Pan, Sachin Chachada, C. -C. Jay Kuo
Date: 2016-03-07 00:00:00
Tasks: Retrieval

A robust two-stage shape retrieval (TSR) method is proposed to address the 2D
shape retrieval problem. Most state-of-the-art shape retrieval methods are
based on local features matching and ranking. Their retrieval performance is
not robust since they may retrieve globally dissimilar shapes in high ranks. To
overcome this challenge, we decompose the decision process into two stages. In
the first irrelevant cluster filtering (ICF) stage, we consider both global and
local features and use them to predict the relevance of gallery shapes with
respect to the query. Irrelevant shapes are removed from the candidate shape
set. After that, a local-features-based matching and ranking (LMR) method
follows in the second stage. We apply the proposed TSR system to MPEG-7,
Kimia99 and Tari1000 three datasets and show that it outperforms all other
existing methods. The robust retrieval performance of the TSR system is
demonstrated.
Cascading A*: a Parallel Approach to Approximate Heuristic Search
Authors: Yan Gu
Date: 2014-06-04 00:00:00
Tasks: Heuristic Search

In this paper, we proposed a new approximate heuristic search algorithm:
Cascading A*, which is a two-phrase algorithm combining A* and IDA* by a new
concept "envelope ball". The new algorithm CA* is efficient, able to generate
approximate solution and any-time solution, and parallel friendly.
Learning Attributes Equals Multi-Source Domain Generalization
Authors: Chuang Gan, Tianbao Yang, Boqing Gong
Date: 2016-05-03 00:00:00
Proceeding: CVPR 2016 6
Tasks: Attribute, Domain Generalization, Image Retrieval, Object Recognition, Retrieval

Attributes possess appealing properties and benefit many computer vision
problems, such as object recognition, learning with humans in the loop, and
image retrieval. Whereas the existing work mainly pursues utilizing attributes
for various computer vision problems, we contend that the most basic
problem---how to accurately and robustly detect attributes from images---has
been left under explored. Especially, the existing work rarely explicitly
tackles the need that attribute detectors should generalize well across
different categories, including those previously unseen. Noting that this is
analogous to the objective of multi-source domain generalization, if we treat
each category as a domain, we provide a novel perspective to attribute
detection and propose to gear the techniques in multi-source domain
generalization for the purpose of learning cross-category generalizable
attribute detectors. We validate our understanding and approach with extensive
experiments on four challenging datasets and three different problems.
VLSI Extreme Learning Machine: A Design Space Exploration
Authors: Enyi Yao, Arindam Basu
Date: 2016-05-03 00:00:00
Tasks: General Classification

In this paper, we describe a compact low-power, high performance hardware
implementation of the extreme learning machine (ELM) for machine learning
applications. Mismatch in current mirrors are used to perform the vector-matrix
multiplication that forms the first stage of this classifier and is the most
computationally intensive. Both regression and classification (on UCI data
sets) are demonstrated and a design space trade-off between speed, power and
accuracy is explored. Our results indicate that for a wide set of problems,
$\sigma V_T$ in the range of $15-25$mV gives optimal results. An input weight
matrix rotation method to extend the input dimension and hidden layer size
beyond the physical limits imposed by the chip is also described. This allows
us to overcome a major limit imposed on most hardware machine learners. The
chip is implemented in a $0.35 \mu$m CMOS process and occupies a die area of
around 5 mm $\times$ 5 mm. Operating from a $1$ V power supply, it achieves an
energy efficiency of $0.47$ pJ/MAC at a classification rate of $31.6$ kHz.
A propagation matting method based on the Local Sampling and KNN Classification with adaptive feature space
Authors: Xiao Chen, Fazhi He
Date: 2016-05-03 00:00:00
Tasks: Form, General Classification, Image Matting

Closed Form is a propagation based matting algorithm, functioning well on
images with good propagation . The deficiency of the Closed Form method is that
for complex areas with poor image propagation , such as hole areas or areas of
long and narrow structures. The right results are usually hard to get. On these
areas, if certain flags are provided, it can improve the effects of matting. In
this paper, we design a matting algorithm by local sampling and the KNN
classifier propagation based matting algorithm. First of all, build the
corresponding features space according to the different components of image
colors to reduce the influence of overlapping between the foreground and
background, and to improve the classification accuracy of KNN classifier.
Second, adaptively use local sampling or using local KNN classifier for
processing based on the pros and cons of the sample performance of unknown
image areas. Finally, based on different treatment methods for the unknown
areas, we will use different weight for augmenting constraints to make the
treatment more effective. In this paper, by combining qualitative observation
and quantitative analysis, we will make evaluation of the experimental results
through online standard set of evaluation tests. It shows that on images with
good propagation , this method is as effective as the Closed Form method, while
on images in complex regions, it can perform even better than Closed Form.
Incorporating Knowledge into Structural Equation Models using Auxiliary Variables
Authors: Bryant Chen, Judea Pearl, Elias Bareinboim
Date: 2015-11-10 00:00:00

In this paper, we extend graph-based identification methods by allowing
background knowledge in the form of non-zero parameter values. Such information
could be obtained, for example, from a previously conducted randomized
experiment, from substantive understanding of the domain, or even an
identification technique. To incorporate such information systematically, we
propose the addition of auxiliary variables to the model, which are constructed
so that certain paths will be conveniently cancelled. This cancellation allows
the auxiliary variables to help conventional methods of identification (e.g.,
single-door criterion, instrumental variables, half-trek criterion), as well as
model testing (e.g., d-separation, over-identification). Moreover, by
iteratively alternating steps of identification and adding auxiliary variables,
we can improve the power of existing identification methods via a bootstrapping
approach that does not require external knowledge. We operationalize this
method for simple instrumental sets (a generalization of instrumental
variables) and show that the resulting method is able to identify at least as
many models as the most general identification method for linear systems known
to date. We further discuss the application of auxiliary variables to the tasks
of model testing and z-identification.
Data Representation and Compression Using Linear-Programming Approximations
Authors: Hristo S. Paskov, John C. Mitchell, Trevor J. Hastie
Date: 2015-11-20 00:00:00
Tasks: Diversity, feature selection

We propose `Dracula', a new framework for unsupervised feature selection from
sequential data such as text. Dracula learns a dictionary of $n$-grams that
efficiently compresses a given corpus and recursively compresses its own
dictionary; in effect, Dracula is a `deep' extension of Compressive Feature
Learning. It requires solving a binary linear program that may be relaxed to a
linear program. Both problems exhibit considerable structure, their solution
paths are well behaved, and we identify parameters which control the depth and
diversity of the dictionary. We also discuss how to derive features from the
compressed documents and show that while certain unregularized linear models
are invariant to the structure of the compressed dictionary, this structure may
be used to regularize learning. Experiments are presented that demonstrate the
efficacy of Dracula's features.
Radio Transformer Networks: Attention Models for Learning to Synchronize in Wireless Systems
Authors: Timothy J. O'Shea, Latha Pemula, Dhruv Batra, T. Charles Clancy
Date: 2016-05-03 00:00:00
Tasks: General Classification

We introduce learned attention models into the radio machine learning domain
for the task of modulation recognition by leveraging spatial transformer
networks and introducing new radio domain appropriate transformations. This
attention model allows the network to learn a localization network capable of
synchronizing and normalizing a radio signal blindly with zero knowledge of the
signals structure based on optimization of the network for classification
accuracy, sparse representation, and regularization. Using this architecture we
are able to outperform our prior results in accuracy vs signal to noise ratio
against an identical system without attention, however we believe such an
attention model has implication far beyond the task of modulation recognition.
Provable Sparse Tensor Decomposition
Authors: Will Wei Sun, Junwei Lu, Han Liu, Guang Cheng
Date: 2015-02-05 00:00:00
Tasks: Click-Through Rate Prediction, Clustering, Tensor Decomposition, Variable Selection

We propose a novel sparse tensor decomposition method, namely Tensor
Truncated Power (TTP) method, that incorporates variable selection into the
estimation of decomposition components. The sparsity is achieved via an
efficient truncation step embedded in the tensor power iteration. Our method
applies to a broad family of high dimensional latent variable models, including
high dimensional Gaussian mixture and mixtures of sparse regressions. A
thorough theoretical investigation is further conducted. In particular, we show
that the final decomposition estimator is guaranteed to achieve a local
statistical rate, and further strengthen it to the global statistical rate by
introducing a proper initialization procedure. In high dimensional regimes, the
obtained statistical rate significantly improves those shown in the existing
non-sparse decomposition methods. The empirical advantages of TTP are confirmed
in extensive simulated results and two real applications of click-through rate
prediction and high-dimensional gene clustering.