pwc-archive/papers-with-abstracts huggingface dataset:
Face Prediction Model for an Automatic Age-invariant Face Recognition System
Authors: Poonam Yadav
Date: 2015-04-16 00:00:00
Tasks: Age-Invariant Face Recognition, Face Detection, Face Identification, Face Recognition, Human Aging, Image-Variation

Automated face recognition and identification softwares are becoming part of
our daily life; it finds its abode not only with Facebook's auto photo tagging,
Apple's iPhoto, Google's Picasa, Microsoft's Kinect, but also in Homeland
Security Department's dedicated biometric face detection systems. Most of these
automatic face identification systems fail where the effects of aging come into
the picture. Little work exists in the literature on the subject of face
prediction that accounts for aging, which is a vital part of the computer face
recognition systems. In recent years, individual face components' (e.g. eyes,
nose, mouth) features based matching algorithms have emerged, but these
approaches are still not efficient. Therefore, in this work we describe a Face
Prediction Model (FPM), which predicts human face aging or growth related image
variation using Principle Component Analysis (PCA) and Artificial Neural
Network (ANN) learning techniques. The FPM captures the facial changes, which
occur with human aging and predicts the facial image with a few years of gap
with an acceptable accuracy of face matching from 76 to 86%.
Temporal Pyramid Pooling Based Convolutional Neural Networks for Action Recognition
Authors: Peng Wang, Yuanzhouhan Cao, Chunhua Shen, Lingqiao Liu, Heng Tao Shen
Date: 2015-03-04 00:00:00
Tasks: Action Recognition, image-classification, Image Classification, Temporal Action Localization

Encouraged by the success of Convolutional Neural Networks (CNNs) in image
classification, recently much effort is spent on applying CNNs to video based
action recognition problems. One challenge is that video contains a varying
number of frames which is incompatible to the standard input format of CNNs.
Existing methods handle this issue either by directly sampling a fixed number
of frames or bypassing this issue by introducing a 3D convolutional layer which
conducts convolution in spatial-temporal domain.
  To solve this issue, here we propose a novel network structure which allows
an arbitrary number of frames as the network input. The key of our solution is
to introduce a module consisting of an encoding layer and a temporal pyramid
pooling layer. The encoding layer maps the activation from previous layers to a
feature vector suitable for pooling while the temporal pyramid pooling layer
converts multiple frame-level activations into a fixed-length video-level
representation. In addition, we adopt a feature concatenation layer which
combines appearance information and motion information. Compared with the frame
sampling strategy, our method avoids the risk of missing any important frames.
Compared with the 3D convolutional method which requires a huge video dataset
for network training, our model can be learned on a small target dataset
because we can leverage the off-the-shelf image-level CNN for model parameter
initialization. Experiments on two challenging datasets, Hollywood2 and HMDB51,
demonstrate that our method achieves superior performance over state-of-the-art
methods while requiring much fewer training data.
Multichannel sparse recovery of complex-valued signals using Huber's criterion
Authors: Esa Ollila
Date: 2015-04-16 00:00:00
Tasks: regression

In this paper, we generalize Huber's criterion to multichannel sparse
recovery problem of complex-valued measurements where the objective is to find
good recovery of jointly sparse unknown signal vectors from the given multiple
measurement vectors which are different linear combinations of the same known
elementary vectors. This requires careful characterization of robust
complex-valued loss functions as well as Huber's criterion function for the
multivariate sparse regression problem. We devise a greedy algorithm based on
simultaneous normalized iterative hard thresholding (SNIHT) algorithm. Unlike
the conventional SNIHT method, our algorithm, referred to as HUB-SNIHT, is
robust under heavy-tailed non-Gaussian noise conditions, yet has a negligible
performance loss compared to SNIHT under Gaussian noise. Usefulness of the
method is illustrated in source localization application with sensor arrays.
Inducing Semantic Representation from Text by Jointly Predicting and Factorizing Relations
Authors: Ivan Titov, Ehsan Khoddam
Date: 2014-12-19 00:00:00
Tasks: Semantic Role Labeling

In this work, we propose a new method to integrate two recent lines of work:
unsupervised induction of shallow semantics (e.g., semantic roles) and
factorization of relations in text and knowledge bases. Our model consists of
two components: (1) an encoding component: a semantic role labeling model which
predicts roles given a rich set of syntactic and lexical features; (2) a
reconstruction component: a tensor factorization model which relies on roles to
predict argument fillers. When the components are estimated jointly to minimize
errors in argument reconstruction, the induced roles largely correspond to
roles defined in annotated resources. Our method performs on par with most
accurate role induction methods on English, even though, unlike these previous
approaches, we do not incorporate any prior linguistic knowledge about the
language.
Learning linearly separable features for speech recognition using convolutional neural networks
Authors: Dimitri Palaz, Mathew Magimai. -Doss, Ronan Collobert
Date: 2014-12-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Phoneme Recognition, speech-recognition, Speech Recognition

Automatic speech recognition systems usually rely on spectral-based features,
such as MFCC of PLP. These features are extracted based on prior knowledge such
as, speech perception or/and speech production. Recently, convolutional neural
networks have been shown to be able to estimate phoneme conditional
probabilities in a completely data-driven manner, i.e. using directly temporal
raw speech signal as input. This system was shown to yield similar or better
performance than HMM/ANN based system on phoneme recognition task and on large
scale continuous speech recognition task, using less parameters. Motivated by
these studies, we investigate the use of simple linear classifier in the
CNN-based framework. Thus, the network learns linearly separable features from
raw speech. We show that such system yields similar or better performance than
MLP based system using cepstral-based features as input.
Actively Learning to Attract Followers on Twitter
Authors: Nir Levine, Timothy A. Mann, Shie Mannor
Date: 2015-04-16 00:00:00
Tasks: BIG-bench Machine Learning

Twitter, a popular social network, presents great opportunities for on-line
machine learning research. However, previous research has focused almost
entirely on learning from passively collected data. We study the problem of
learning to acquire followers through normative user behavior, as opposed to
the mass following policies applied by many bots. We formalize the problem as a
contextual bandit problem, in which we consider retweeting content to be the
action chosen and each tweet (content) is accompanied by context. We design
reward signals based on the change in followers. The result of our month long
experiment with 60 agents suggests that (1) aggregating experience across
agents can adversely impact prediction accuracy and (2) the Twitter community's
response to different actions is non-stationary. Our findings suggest that
actively learning on-line can provide deeper insights about how to attract
followers than machine learning over passively collected data alone.
Faster Algorithms for Testing under Conditional Sampling
Authors: Moein Falahatgar, Ashkan Jafarpour, Alon Orlitsky, Venkatadheeraj Pichapathi, Ananda Theertha Suresh
Date: 2015-04-16 00:00:00

There has been considerable recent interest in distribution-tests whose
run-time and sample requirements are sublinear in the domain-size $k$. We study
two of the most important tests under the conditional-sampling model where each
query specifies a subset $S$ of the domain, and the response is a sample drawn
from $S$ according to the underlying distribution.
  For identity testing, which asks whether the underlying distribution equals a
specific given distribution or $\epsilon$-differs from it, we reduce the known
time and sample complexities from $\tilde{\mathcal{O}}(\epsilon^{-4})$ to
$\tilde{\mathcal{O}}(\epsilon^{-2})$, thereby matching the information
theoretic lower bound. For closeness testing, which asks whether two
distributions underlying observed data sets are equal or different, we reduce
existing complexity from $\tilde{\mathcal{O}}(\epsilon^{-4} \log^5 k)$ to an
even sub-logarithmic $\tilde{\mathcal{O}}(\epsilon^{-5} \log \log k)$ thus
providing a better bound to an open problem in Bertinoro Workshop on Sublinear
Algorithms [Fisher, 2004].
Bayesian Robust Tensor Factorization for Incomplete Multiway Data
Authors: Qibin Zhao, Guoxu Zhou, Liqing Zhang, Andrzej Cichocki, Shun-ichi Amari
Date: 2014-10-09 00:00:00
Tasks: Model Selection, Variational Inference

We propose a generative model for robust tensor factorization in the presence
of both missing data and outliers. The objective is to explicitly infer the
underlying low-CP-rank tensor capturing the global information and a sparse
tensor capturing the local information (also considered as outliers), thus
providing the robust predictive distribution over missing entries. The
low-CP-rank tensor is modeled by multilinear interactions between multiple
latent factors on which the column sparsity is enforced by a hierarchical
prior, while the sparse tensor is modeled by a hierarchical view of Student-$t$
distribution that associates an individual hyperparameter with each element
independently. For model learning, we develop an efficient closed-form
variational inference under a fully Bayesian treatment, which can effectively
prevent the overfitting problem and scales linearly with data size. In contrast
to existing related works, our method can perform model selection automatically
and implicitly without need of tuning parameters. More specifically, it can
discover the groundtruth of CP rank and automatically adapt the sparsity
inducing priors to various types of outliers. In addition, the tradeoff between
the low-rank approximation and the sparse representation can be optimized in
the sense of maximum model evidence. The extensive experiments and comparisons
with many state-of-the-art algorithms on both synthetic and real-world datasets
demonstrate the superiorities of our method from several perspectives.
Comparisons of wavelet functions in QRS signal to noise ratio enhancement and detection accuracy
Authors: Pornchai Phukpattaranont
Date: 2015-04-15 00:00:00

We compare the capability of wavelet functions used for noise removal in
preprocessing step of a QRS detection algorithm in the electrocardiogram (ECG)
signal. The QRS signal to noise ratio enhancement and the detection accuracy of
each wavelet function are evaluated using three measures: (1) the ratio of the
maximum beat amplitude to the minimum beat amplitude (RMM), (2) the mean of
absolute of time error (MATE), and (3) the figure of merit (FOM). Three wavelet
functions from previous well-known publications are explored, i.e., Bior1.3,
Db10, and Mexican hat wavelet functions. Results evaluated with the ECG signal
from MIT-BIH arrhythmia database show that the Mexican hat wavelet function is
better than the others. While the scale 8 of Mexican hat wavelet function can
provide the best enhancement in QRS signal to noise ratio, the scale 4 of
Mexican hat wavelet function can provide the best detection accuracy. These
results may be combined and may enable the use of a single fixed threshold for
all ECG records leading to the reduction in computational complexity of the QRS
detection algorithm.
Segmentation of Subspaces in Sequential Data
Authors: Stephen Tierney, Yi Guo, Junbin Gao
Date: 2015-04-16 00:00:00
Tasks: Clustering, Segmentation

We propose Ordered Subspace Clustering (OSC) to segment data drawn from a
sequentially ordered union of subspaces. Similar to Sparse Subspace Clustering
(SSC) we formulate the problem as one of finding a sparse representation but
include an additional penalty term to take care of sequential data. We test our
method on data drawn from infrared hyper spectral, video and motion capture
data. Experiments show that our method, OSC, outperforms the state of the art
methods: Spatial Subspace Clustering (SpatSC), Low-Rank Representation (LRR)
and SSC.
FPA-CS: Focal Plane Array-based Compressive Imaging in Short-wave Infrared
Authors: Huaijin Chen, M. Salman Asif, Aswin C. Sankaranarayanan, Ashok Veeraraghavan
Date: 2015-04-16 00:00:00
Proceeding: CVPR 2015 6
Tasks: Compressive Sensing

Cameras for imaging in short and mid-wave infrared spectra are significantly
more expensive than their counterparts in visible imaging. As a result,
high-resolution imaging in those spectrum remains beyond the reach of most
consumers. Over the last decade, compressive sensing (CS) has emerged as a
potential means to realize inexpensive short-wave infrared cameras. One
approach for doing this is the single-pixel camera (SPC) where a single
detector acquires coded measurements of a high-resolution image. A
computational reconstruction algorithm is then used to recover the image from
these coded measurements. Unfortunately, the measurement rate of a SPC is
insufficient to enable imaging at high spatial and temporal resolutions.
  We present a focal plane array-based compressive sensing (FPA-CS)
architecture that achieves high spatial and temporal resolutions. The idea is
to use an array of SPCs that sense in parallel to increase the measurement
rate, and consequently, the achievable spatio-temporal resolution of the
camera. We develop a proof-of-concept prototype in the short-wave infrared
using a sensor with 64$\times$ 64 pixels; the prototype provides a 4096$\times$
increase in the measurement rate compared to the SPC and achieves a megapixel
resolution at video rate using CS techniques.
Unsupervised Domain Adaptation with Feature Embeddings
Authors: Yi Yang, Jacob Eisenstein
Date: 2014-12-14 00:00:00
Tasks: Domain Adaptation, Representation Learning, Unsupervised Domain Adaptation

Representation learning is the dominant technique for unsupervised domain
adaptation, but existing approaches often require the specification of "pivot
features" that generalize across domains, which are selected by task-specific
heuristics. We show that a novel but simple feature embedding approach provides
better performance, by exploiting the feature template structure common in NLP
problems.
Unsupervised Feature Learning from Temporal Data
Authors: Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann Lecun
Date: 2015-04-09 00:00:00
Tasks: General Classification, Metric Learning

Current state-of-the-art classification and detection algorithms rely on
supervised training. In this work we study unsupervised feature learning in the
context of temporally coherent video data. We focus on feature learning from
unlabeled video data, using the assumption that adjacent video frames contain
semantically similar information. This assumption is exploited to train a
convolutional pooling auto-encoder regularized by slowness and sparsity. We
establish a connection between slow feature learning to metric learning and
show that the trained encoder can be used to define a more temporally and
semantically coherent metric.
A Generative Model for Deep Convolutional Learning
Authors: Yunchen Pu, Xin Yuan, Lawrence Carin
Date: 2015-04-15 00:00:00
Tasks: Dictionary Learning, General Classification, model

A generative model is developed for deep (multi-layered) convolutional
dictionary learning. A novel probabilistic pooling operation is integrated into
the deep model, yielding efficient bottom-up (pretraining) and top-down
(refinement) probabilistic learning. Experimental results demonstrate powerful
capabilities of the model to learn multi-layer features from images, and
excellent classification results are obtained on the MNIST and Caltech 101
datasets.
Diverse Embedding Neural Network Language Models
Authors: Kartik Audhkhasi, Abhinav Sethy, Bhuvana Ramabhadran
Date: 2014-12-22 00:00:00
Tasks: Language Modeling, Language Modelling

We propose Diverse Embedding Neural Network (DENN), a novel architecture for
language models (LMs). A DENNLM projects the input word history vector onto
multiple diverse low-dimensional sub-spaces instead of a single
higher-dimensional sub-space as in conventional feed-forward neural network
LMs. We encourage these sub-spaces to be diverse during network training
through an augmented loss function. Our language modeling experiments on the
Penn Treebank data set show the performance benefit of using a DENNLM.
Anatomy-specific classification of medical images using deep convolutional nets
Authors: Holger R. Roth, Christopher T. Lee, Hoo-chang Shin, Ari Seff, Lauren Kim, Jianhua Yao, Le Lu, Ronald M. Summers
Date: 2015-04-15 00:00:00
Tasks: Anatomy, Classification, Computed Tomography (CT), Data Augmentation, General Classification, image-classification, Image Classification

Automated classification of human anatomy is an important prerequisite for
many computer-aided diagnosis systems. The spatial complexity and variability
of anatomy throughout the human body makes classification difficult. "Deep
learning" methods such as convolutional networks (ConvNets) outperform other
state-of-the-art methods in image classification tasks. In this work, we
present a method for organ- or body-part-specific anatomical classification of
medical images acquired using computed tomography (CT) with ConvNets. We train
a ConvNet, using 4,298 separate axial 2D key-images to learn 5 anatomical
classes. Key-images were mined from a hospital PACS archive, using a set of
1,675 patients. We show that a data augmentation approach can help to enrich
the data set and improve classification performance. Using ConvNets and data
augmentation, we achieve anatomy-specific classification error of 5.9 % and
area-under-the-curve (AUC) values of an average of 0.998 in testing. We
demonstrate that deep learning can be used to train very reliable and accurate
classifiers that could initialize further computer-aided diagnosis.
Training Deep Neural Networks on Noisy Labels with Bootstrapping
Authors: Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, Andrew Rabinovich
Date: 2014-12-20 00:00:00
Tasks: Emotion Recognition, Object Recognition

Current state-of-the-art deep learning systems for visual object recognition
and detection use purely supervised training with regularization such as
dropout to avoid overfitting. The performance depends critically on the amount
of labeled examples, and in current practice the labels are assumed to be
unambiguous and accurate. However, this assumption often does not hold; e.g. in
recognition, class labels may be missing; in detection, objects in the image
may not be localized; and in general, the labeling may be subjective. In this
work we propose a generic way to handle noisy and incomplete labeling by
augmenting the prediction objective with a notion of consistency. We consider a
prediction consistent if the same prediction is made given similar percepts,
where the notion of similarity is between deep network features computed from
the input data. In experiments we demonstrate that our approach yields
substantial robustness to label noise on several datasets. On MNIST handwritten
digits, we show that our model is robust to label corruption. On the Toronto
Face Database, we show that our model handles well the case of subjective
labels in emotion recognition, achieving state-of-the- art results, and can
also benefit from unlabeled face images with no modification to our method. On
the ILSVRC2014 detection challenge data, we show that our approach extends to
very deep networks, high resolution images and structured outputs, and results
in improved scalable detection.
Object Detectors Emerge in Deep Scene CNNs
Authors: Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba
Date: 2014-12-22 00:00:00
Tasks: General Classification, Object, Object Localization, Scene Classification, Scene Recognition

With the success of new computational architectures for visual processing,
such as convolutional neural networks (CNN) and access to image databases with
millions of labeled examples (e.g., ImageNet, Places), the state of the art in
computer vision is advancing rapidly. One important factor for continued
progress is to understand the representations that are learned by the inner
layers of these deep architectures. Here we show that object detectors emerge
from training CNNs to perform scene classification. As scenes are composed of
objects, the CNN for scene classification automatically discovers meaningful
objects detectors, representative of the learned scene categories. With object
detectors emerging as a result of learning to recognize scenes, our work
demonstrates that the same network can perform both scene recognition and
object localization in a single forward-pass, without ever having been
explicitly taught the notion of objects.
Deep convolutional networks for pancreas segmentation in CT imaging
Authors: Holger R. Roth, Amal Farag, Le Lu, Evrim B. Turkbey, Ronald M. Summers
Date: 2015-04-15 00:00:00
Tasks: Clustering, Computed Tomography (CT), General Classification, image-classification, Image Classification, Organ Segmentation, Pancreas Segmentation, Segmentation, Superpixels

Automatic organ segmentation is an important prerequisite for many
computer-aided diagnosis systems. The high anatomical variability of organs in
the abdomen, such as the pancreas, prevents many segmentation methods from
achieving high accuracies when compared to other segmentation of organs like
the liver, heart or kidneys. Recently, the availability of large annotated
training sets and the accessibility of affordable parallel computing resources
via GPUs have made it feasible for "deep learning" methods such as
convolutional networks (ConvNets) to succeed in image classification tasks.
These methods have the advantage that used classification features are trained
directly from the imaging data. We present a fully-automated bottom-up method
for pancreas segmentation in computed tomography (CT) images of the abdomen.
The method is based on hierarchical coarse-to-fine classification of local
image regions (superpixels). Superpixels are extracted from the abdominal
region using Simple Linear Iterative Clustering (SLIC). An initial probability
response map is generated, using patch-level confidences and a two-level
cascade of random forest classifiers, from which superpixel regions with
probabilities larger 0.5 are retained. These retained superpixels serve as a
highly sensitive initial input of the pancreas and its surroundings to a
ConvNet that samples a bounding box around each superpixel at different scales
(and random non-rigid deformations at training time) in order to assign a more
distinct probability of each superpixel region being pancreas or not. We
evaluate our method on CT images of 82 patients (60 for training, 2 for
validation, and 20 for testing). Using ConvNets we achieve average Dice scores
of 68%+-10% (range, 43-80%) in testing. This shows promise for accurate
pancreas segmentation, using a deep learning approach and compares favorably to
state-of-the-art methods.