pwc-archive/papers-with-abstracts huggingface dataset:
Gradual DropIn of Layers to Train Very Deep Neural Networks
Authors: Leslie N. Smith, Emily M. Hand, Timothy Doster
Date: 2015-11-22 00:00:00
Proceeding: CVPR 2016 6

We introduce the concept of dynamically growing a neural network during
training. In particular, an untrainable deep network starts as a trainable
shallow network and newly added layers are slowly, organically added during
training, thereby increasing the network's depth. This is accomplished by a new
layer, which we call DropIn. The DropIn layer starts by passing the output from
a previous layer (effectively skipping over the newly added layers), then
increasingly including units from the new layers for both feedforward and
backpropagation. We show that deep networks, which are untrainable with
conventional methods, will converge with DropIn layers interspersed in the
architecture. In addition, we demonstrate that DropIn provides regularization
during training in an analogous way as dropout. Experiments are described with
the MNIST dataset and various expanded LeNet architectures, CIFAR-10 dataset
with its architecture expanded from 3 to 11 layers, and on the ImageNet dataset
with the AlexNet architecture expanded to 13 layers and the VGG 16-layer
architecture.
Real-Time Anomaly Detection and Localization in Crowded Scenes
Authors: Mohammad Sabokrou, Mahmood Fathy, Mojtaba Hosseini, Reinhard klette
Date: 2015-11-21 00:00:00
Tasks: Anomaly Detection

In this paper, we propose a method for real-time anomaly detection and
localization in crowded scenes. Each video is defined as a set of
non-overlapping cubic patches, and is described using two local and global
descriptors. These descriptors capture the video properties from different
aspects. By incorporating simple and cost-effective Gaussian classifiers, we
can distinguish normal activities and anomalies in videos. The local and global
features are based on structure similarity between adjacent patches and the
features learned in an unsupervised way, using a sparse auto- encoder.
Experimental results show that our algorithm is comparable to a
state-of-the-art procedure on UCSD ped2 and UMN benchmarks, but even more
time-efficient. The experiments confirm that our system can reliably detect and
localize anomalies as soon as they happen in a video.
Screen Content Image Segmentation Using Sparse-Smooth Decomposition
Authors: Shervin Minaee, Amirali Abdolrashidi, Yao Wang
Date: 2015-11-21 00:00:00
Tasks: Clustering, Denoising, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Video Compression

Sparse decomposition has been extensively used for different applications
including signal compression and denoising and document analysis. In this
paper, sparse decomposition is used for image segmentation. The proposed
algorithm separates the background and foreground using a sparse-smooth
decomposition technique such that the smooth and sparse components correspond
to the background and foreground respectively. This algorithm is tested on
several test images from HEVC test sequences and is shown to have superior
performance over other methods, such as the hierarchical k-means clustering in
DjVu. This segmentation algorithm can also be used for text extraction, video
compression and medical image segmentation.
ICU Patient Deterioration prediction: a Data-Mining Approach
Authors: Noura AlNuaimi, Mohammad M Masud, Farhan Mohammed
Date: 2015-11-21 00:00:00
Tasks: Dimensionality Reduction, feature selection, Prediction

A huge amount of medical data is generated every day, which presents a
challenge in analysing these data. The obvious solution to this challenge is to
reduce the amount of data without information loss. Dimension reduction is
considered the most popular approach for reducing data size and also to reduce
noise and redundancies in data. In this paper, we investigate the effect of
feature selection in improving the prediction of patient deterioration in ICUs.
We consider lab tests as features. Thus, choosing a subset of features would
mean choosing the most important lab tests to perform. If the number of tests
can be reduced by identifying the most important tests, then we could also
identify the redundant tests. By omitting the redundant tests, observation time
could be reduced and early treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be avoided. Our approach uses
state-ofthe- art feature selection for predicting ICU patient deterioration
using the medical lab results. We apply our technique on the publicly available
MIMIC-II database and show the effectiveness of the feature selection. We also
provide a detailed analysis of the best features identified by our approach.
Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond
Authors: Chun Kai Ling, Kian Hsiang Low, Patrick Jaillet
Date: 2015-11-21 00:00:00
Tasks: Active Learning, Bayesian Optimization

This paper presents a novel nonmyopic adaptive Gaussian process planning
(GPP) framework endowed with a general class of Lipschitz continuous reward
functions that can unify some active learning/sensing and Bayesian optimization
criteria and offer practitioners some flexibility to specify their desired
choices for defining new tasks/problems. In particular, it utilizes a
principled Bayesian sequential decision problem framework for jointly and
naturally optimizing the exploration-exploitation trade-off. In general, the
resulting induced GPP policy cannot be derived exactly due to an uncountable
set of candidate observations. A key contribution of our work here thus lies in
exploiting the Lipschitz continuity of the reward functions to solve for a
nonmyopic adaptive epsilon-optimal GPP (epsilon-GPP) policy. To plan in real
time, we further propose an asymptotically optimal, branch-and-bound anytime
variant of epsilon-GPP with performance guarantee. We empirically demonstrate
the effectiveness of our epsilon-GPP policy and its anytime variant in Bayesian
optimization and an energy harvesting task.
Recurrent Polynomial Network for Dialogue State Tracking
Authors: Kai Sun, Qizhe Xie, Kai Yu
Date: 2015-07-14 00:00:00
Tasks: dialog state tracking, Dialogue State Tracking

Dialogue state tracking (DST) is a process to estimate the distribution of
the dialogue states as a dialogue progresses. Recent studies on constrained
Markov Bayesian polynomial (CMBP) framework take the first step towards
bridging the gap between rule-based and statistical approaches for DST. In this
paper, the gap is further bridged by a novel framework -- recurrent polynomial
network (RPN). RPN's unique structure enables the framework to have all the
advantages of CMBP including efficiency, portability and interpretability.
Additionally, RPN achieves more properties of statistical approaches than CMBP.
RPN was evaluated on the data corpora of the second and the third Dialog State
Tracking Challenge (DSTC-2/3). Experiments showed that RPN can significantly
outperform both traditional rule-based approaches and statistical approaches
with similar feature set. Compared with the state-of-the-art statistical DST
approaches with a lot richer features, RPN is also competitive.
TransCut: Transparent Object Segmentation from a Light-Field Image
Authors: Yichao Xu, Hajime Nagahara, Atsushi Shimada, Rin-ichiro Taniguchi
Date: 2015-11-21 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Segmentation, Object, Segmentation, Semantic Segmentation, Transparent objects

The segmentation of transparent objects can be very useful in computer vision
applications. However, because they borrow texture from their background and
have a similar appearance to their surroundings, transparent objects are not
handled well by regular image segmentation methods. We propose a method that
overcomes these problems using the consistency and distortion properties of a
light-field image. Graph-cut optimization is applied for the pixel labeling
problem. The light-field linearity is used to estimate the likelihood of a
pixel belonging to the transparent object or Lambertian background, and the
occlusion detector is used to find the occlusion boundary. We acquire a light
field dataset for the transparent object, and use this dataset to evaluate our
method. The results demonstrate that the proposed method successfully segments
transparent objects from the background.
Mapping Images to Sentiment Adjective Noun Pairs with Factorized Neural Nets
Authors: Takuya Narihira, Damian Borth, Stella X. Yu, Karl Ni, Trevor Darrell
Date: 2015-11-21 00:00:00
Tasks: Image Captioning

We consider the visual sentiment task of mapping an image to an adjective
noun pair (ANP) such as "cute baby". To capture the two-factor structure of our
ANP semantics as well as to overcome annotation noise and ambiguity, we propose
a novel factorized CNN model which learns separate representations for
adjectives and nouns but optimizes the classification performance over their
product. Our experiments on the publicly available SentiBank dataset show that
our model significantly outperforms not only independent ANP classifiers on
unseen ANPs and on retrieving images of novel ANPs, but also image captioning
models which capture word semantics from co-occurrence of natural text; the
latter turn out to be surprisingly poor at capturing the sentiment evoked by
pure visual experience. That is, our factorized ANP CNN not only trains better
from noisy labels, generalizes better to new images, but can also expands the
ANP vocabulary on its own.
Fidelity-Naturalness Evaluation of Single Image Super Resolution
Authors: Xuan Dong, Yu Zhu, Weixin Li, Lingxi Xie, Alex Wong, Alan Yuille
Date: 2015-11-21 00:00:00
Tasks: Image Quality Assessment, Image Super-Resolution, Super-Resolution

We study the problem of evaluating super resolution methods. Traditional
evaluation methods usually judge the quality of super resolved images based on
a single measure of their difference with the original high resolution images.
In this paper, we proposed to use both fidelity (the difference with original
images) and naturalness (human visual perception of super resolved images) for
evaluation. For fidelity evaluation, a new metric is proposed to solve the bias
problem of traditional evaluation. For naturalness evaluation, we let humans
label preference of super resolution results using pair-wise comparison, and
test the correlation between human labeling results and image quality
assessment metrics' outputs. Experimental results show that our
fidelity-naturalness method is better than the traditional evaluation method
for super resolution methods, which could help future research on single-image
super resolution.
Semi-supervised Bootstrapping approach for Named Entity Recognition
Authors: S. Thenmalar, J. Balaji, T. V. Geetha
Date: 2015-11-21 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

The aim of Named Entity Recognition (NER) is to identify references of named
entities in unstructured documents, and to classify them into pre-defined
semantic categories. NER often aids from added background knowledge in the form
of gazetteers. However using such a collection does not deal with name variants
and cannot resolve ambiguities associated in identifying the entities in
context and associating them with predefined categories. We present a
semi-supervised NER approach that starts with identifying named entities with a
small set of training data. Using the identified named entities, the word and
the context features are used to define the pattern. This pattern of each named
entity category is used as a seed pattern to identify the named entities in the
test set. Pattern scoring and tuple value score enables the generation of the
new patterns to identify the named entity categories. We have evaluated the
proposed system for English language with the dataset of tagged (IEER) and
untagged (CoNLL 2003) named entity corpus and for Tamil language with the
documents from the FIRE corpus and yield an average f-measure of 75% for both
the languages.
GradNets: Dynamic Interpolation Between Neural Architectures
Authors: Diogo Almeida, Nate Sauder
Date: 2015-11-21 00:00:00

In machine learning, there is a fundamental trade-off between ease of
optimization and expressive power. Neural Networks, in particular, have
enormous expressive power and yet are notoriously challenging to train. The
nature of that optimization challenge changes over the course of learning.
Traditionally in deep learning, one makes a static trade-off between the needs
of early and late optimization. In this paper, we investigate a novel
framework, GradNets, for dynamically adapting architectures during training to
get the benefits of both. For example, we can gradually transition from linear
to non-linear networks, deterministic to stochastic computation, shallow to
deep architectures, or even simple downsampling to fully differentiable
attention mechanisms. Benefits include increased accuracy, easier convergence
with more complex architectures, solutions to test-time execution of batch
normalization, and the ability to train networks of up to 200 layers.
Kernel Additive Principal Components
Authors: Xin Lu Tan, Andreas Buja, Zongming Ma
Date: 2015-11-21 00:00:00

Additive principal components (APCs for short) are a nonlinear generalization
of linear principal components. We focus on smallest APCs to describe additive
nonlinear constraints that are approximately satisfied by the data. Thus APCs
fit data with implicit equations that treat the variables symmetrically, as
opposed to regression analyses which fit data with explicit equations that
treat the data asymmetrically by singling out a response variable. We propose a
regularized data-analytic procedure for APC estimation using kernel methods. In
contrast to existing approaches to APCs that are based on regularization
through subspace restriction, kernel methods achieve regularization through
shrinkage and therefore grant distinctive flexibility in APC estimation by
allowing the use of infinite-dimensional functions spaces for searching APC
transformation while retaining computational feasibility. To connect population
APCs and kernelized finite-sample APCs, we study kernelized population APCs and
their associated eigenproblems, which eventually lead to the establishment of
consistency of the estimated APCs. Lastly, we discuss an iterative algorithm
for computing kernelized finite-sample APCs.
An Immersive Telepresence System using RGB-D Sensors and Head Mounted Display
Authors: Xinzhong Lu, Ju Shen, Saverio Perugini, Jianjun Yang
Date: 2015-11-21 00:00:00

We present a tele-immersive system that enables people to interact with each
other in a virtual world using body gestures in addition to verbal
communication. Beyond the obvious applications, including general online
conversations and gaming, we hypothesize that our proposed system would be
particularly beneficial to education by offering rich visual contents and
interactivity. One distinct feature is the integration of egocentric pose
recognition that allows participants to use their gestures to demonstrate and
manipulate virtual objects simultaneously. This functionality enables the
instructor to ef- fectively and efficiently explain and illustrate complex
concepts or sophisticated problems in an intuitive manner. The highly
interactive and flexible environment can capture and sustain more student
attention than the traditional classroom setting and, thus, delivers a
compelling experience to the students. Our main focus here is to investigate
possible solutions for the system design and implementation and devise
strategies for fast, efficient computation suitable for visual data processing
and network transmission. We describe the technique and experiments in details
and provide quantitative performance results, demonstrating our system can be
run comfortably and reliably for different application scenarios. Our
preliminary results are promising and demonstrate the potential for more
compelling directions in cyberlearning.
Learning visual groups from co-occurrences in space and time
Authors: Phillip Isola, Daniel Zoran, Dilip Krishnan, Edward H. Adelson
Date: 2015-11-21 00:00:00
Tasks: Binary Classification

We propose a self-supervised framework that learns to group visual entities
based on their rate of co-occurrence in space and time. To model statistical
dependencies between the entities, we set up a simple binary classification
problem in which the goal is to predict if two visual primitives occur in the
same spatial or temporal context. We apply this framework to three domains:
learning patch affinities from spatial adjacency in images, learning frame
affinities from temporal adjacency in videos, and learning photo affinities
from geospatial proximity in image collections. We demonstrate that in each
case the learned affinities uncover meaningful semantic groupings. From patch
affinities we generate object proposals that are competitive with
state-of-the-art supervised methods. From frame affinities we generate movie
scene segmentations that correlate well with DVD chapter structure. Finally,
from geospatial affinities we learn groups that relate well to semantic place
categories.
Adding Gradient Noise Improves Learning for Very Deep Networks
Authors: Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, James Martens
Date: 2015-11-21 00:00:00
Tasks: Question Answering

Deep feedforward and recurrent networks have achieved impressive results in
many perception and language processing applications. This success is partially
attributed to architectural innovations such as convolutional and long
short-term memory networks. The main motivation for these architectural
innovations is that they capture better domain knowledge, and importantly are
easier to optimize than more basic architectures. Recently, more complex
architectures such as Neural Turing Machines and Memory Networks have been
proposed for tasks including question answering and general computation,
creating a new set of optimization challenges. In this paper, we discuss a
low-overhead and easy-to-implement technique of adding gradient noise which we
find to be surprisingly effective when training these very deep architectures.
The technique not only helps to avoid overfitting, but also can result in lower
training loss. This method alone allows a fully-connected 20-layer deep network
to be trained with standard gradient descent, even starting from a poor
initialization. We see consistent improvements for many complex models,
including a 72% relative reduction in error rate over a carefully-tuned
baseline on a challenging question-answering task, and a doubling of the number
of accurate binary multiplication models learned across 7,000 random restarts.
We encourage further application of this technique to additional complex modern
architectures.
Training recurrent networks online without backtracking
Authors: Yann Ollivier, Corentin Tallec, Guillaume Charpiat
Date: 2015-07-28 00:00:00

We introduce the "NoBackTrack" algorithm to train the parameters of dynamical
systems such as recurrent neural networks. This algorithm works in an online,
memoryless setting, thus requiring no backpropagation through time, and is
scalable, avoiding the large computational and memory cost of maintaining the
full gradient of the current state with respect to the parameters.
  The algorithm essentially maintains, at each time, a single search direction
in parameter space. The evolution of this search direction is partly stochastic
and is constructed in such a way to provide, at every time, an unbiased random
estimate of the gradient of the loss function with respect to the parameters.
Because the gradient estimate is unbiased, on average over time the parameter
is updated as it should.
  The resulting gradient estimate can then be fed to a lightweight Kalman-like
filter to yield an improved algorithm. For recurrent neural networks, the
resulting algorithms scale linearly with the number of parameters.
  Small-scale experiments confirm the suitability of the approach, showing that
the stochastic approximation of the gradient introduced in the algorithm is not
detrimental to learning. In particular, the Kalman-like version of NoBackTrack
is superior to backpropagation through time (BPTT) when the time span of
dependencies in the data is longer than the truncation span for BPTT.
Variational Bayes Factor Analysis for i-Vector Extraction
Authors: Jesús Villalba
Date: 2015-11-20 00:00:00

In this document we are going to derive the equations needed to implement a
Variational Bayes i-vector extractor. This can be used to extract longer
i-vectors reducing the risk of overfittig or to adapt an i-vector extractor
from a database to another with scarce development data. This work is based on
Patrick Kenny's joint factor analysis and Christopher Bishop's variational
principal components.
Unsupervised Adaptation of SPLDA
Authors: Jesús Villalba
Date: 2015-11-20 00:00:00
Tasks: speaker-diarization, Speaker Diarization, Speaker Recognition

State-of-the-art speaker recognition relays on models that need a large
amount of training data. This models are successful in tasks like NIST SRE
because there is sufficient data available. However, in real applications, we
usually do not have so much data and, in many cases, the speaker labels are
unknown. We present a method to adapt a PLDA model from a domain with a large
amount of labeled data to another with unlabeled data. We describe a generative
model that produces both sets of data where the unknown labels are modeled like
latent variables. We used variational Bayes to estimate the hidden variables.
Here, we derive the equations for this model. This model has been used in the
papers: "UNSUPERVISED ADAPTATION OF PLDA BY USING VARIATIONAL BAYES METHODS"
publised at ICASSP 2014, "Unsupervised Training of PLDA with Variational Bayes"
published at Iberspeech 2014, and "VARIATIONAL BAYESIAN PLDA FOR SPEAKER
DIARIZATION IN THE MGB CHALLENGE" published at ASRU 2015.
PLDA with Two Sources of Inter-session Variability
Authors: Jesús Villalba
Date: 2015-11-20 00:00:00
Tasks: Speaker Recognition, Vocal Bursts Valence Prediction

In some speaker recognition scenarios we find conversations recorded
simultaneously over multiple channels. That is the case of the interviews in
the NIST SRE dataset. To take advantage of that, we propose a modification of
the PLDA model that considers two different inter-session variability terms.
The first term is tied between all the recordings belonging to the same
conversation whereas the second is not. Thus, the former mainly intends to
capture the variability due to the phonetic content of the conversation while
the latter tries to capture the channel variability. In this document, we
derive the equations for this model. This model was applied in the paper
"Handling Recordings Acquired Simultaneously over Multiple Channels with PLDA"
published at Interspeech 2013.
Bayesian SPLDA
Authors: Jesús Villalba
Date: 2015-11-20 00:00:00

In this document we are going to derive the equations needed to implement a
Variational Bayes estimation of the parameters of the simplified probabilistic
linear discriminant analysis (SPLDA) model. This can be used to adapt SPLDA
from one database to another with few development data or to implement the
fully Bayesian recipe. Our approach is similar to Bishop's VB PPCA.