pwc-archive/papers-with-abstracts huggingface dataset:
Prosodic Event Recognition using Convolutional Neural Networks with Context Information
Authors: Sabrina Stehwien, Ngoc Thang Vu
Date: 2017-06-02 00:00:00
Tasks: Position

This paper demonstrates the potential of convolutional neural networks (CNN)
for detecting and classifying prosodic events on words, specifically pitch
accents and phrase boundary tones, from frame-based acoustic features. Typical
approaches use not only feature representations of the word in question but
also its surrounding context. We show that adding position features indicating
the current word benefits the CNN. In addition, this paper discusses the
generalization from a speaker-dependent modelling approach to a
speaker-independent setup. The proposed method is simple and efficient and
yields strong results not only in speaker-dependent but also
speaker-independent cases.
Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks
Authors: Jae Y. Shin, Nima Tajbakhsh, R. Todd Hurst, Christopher B. Kendall, Jianming Liang
Date: 2017-06-02 00:00:00
Proceeding: CVPR 2016 6

Cardiovascular disease (CVD) is the leading cause of mortality yet largely
preventable, but the key to prevention is to identify at-risk individuals
before adverse events. For predicting individual CVD risk, carotid intima-media
thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable,
offering several advantages over CT coronary artery calcium score. However,
each CIMT examination includes several ultrasound videos, and interpreting each
of these CIMT videos involves three operations: (1) select three end-diastolic
ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI)
in each selected frame, and (3) trace the lumen-intima interface and the
media-adventitia interface in each ROI to measure CIMT. These operations are
tedious, laborious, and time consuming, a serious limitation that hinders the
widespread utilization of CIMT in clinical practice. To overcome this
limitation, this paper presents a new system to automate CIMT video
interpretation. Our extensive experiments demonstrate that the suggested system
significantly outperforms the state-of-the-art methods. The superior
performance is attributable to our unified framework based on convolutional
neural networks (CNNs) coupled with our informative image representation and
effective post-processing of the CNN outputs, which are uniquely designed for
each of the above three operations.
On the ability of neural nets to express distributions
Authors: Holden Lee, Rong Ge, Tengyu Ma, Andrej Risteski, Sanjeev Arora
Date: 2017-02-22 00:00:00

Deep neural nets have caused a revolution in many classification tasks. A related ongoing revolution -- also theoretically not understood -- concerns their ability to serve as generative models for complicated types of data such as images and texts. These models are trained using ideas like variational autoencoders and Generative Adversarial Networks. We take a first cut at explaining the expressivity of multilayer nets by giving a sufficient criterion for a function to be approximable by a neural network with $n$ hidden layers. A key ingredient is Barron's Theorem \cite{Barron1993}, which gives a Fourier criterion for approximability of a function by a neural network with 1 hidden layer. We show that a composition of $n$ functions which satisfy certain Fourier conditions ("Barron functions") can be approximated by a $n+1$-layer neural network. For probability distributions, this translates into a criterion for a probability distribution to be approximable in Wasserstein distance -- a natural metric on probability distributions -- by a neural network applied to a fixed base distribution (e.g., multivariate gaussian). Building up recent lower bound work, we also give an example function that shows that composition of Barron functions is more expressive than Barron functions alone.
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Authors: Nima Tajbakhsh, Jae Y. Shin, Suryakanth R. Gurudu, R. Todd Hurst, Christopher B. Kendall, Michael B. Gotway, Jianming Liang
Date: 2017-06-02 00:00:00
Tasks: Medical Image Analysis, Transfer Learning

Training a deep convolutional neural network (CNN) from scratch is difficult
because it requires a large amount of labeled training data and a great deal of
expertise to ensure proper convergence. A promising alternative is to fine-tune
a CNN that has been pre-trained using, for instance, a large set of labeled
natural images. However, the substantial differences between natural and
medical images may advise against such knowledge transfer. In this paper, we
seek to answer the following central question in the context of medical image
analysis: \emph{Can the use of pre-trained deep CNNs with sufficient
fine-tuning eliminate the need for training a deep CNN from scratch?} To
address this question, we considered 4 distinct medical imaging applications in
3 specialties (radiology, cardiology, and gastroenterology) involving
classification, detection, and segmentation from 3 different imaging
modalities, and investigated how the performance of deep CNNs trained from
scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner.
Our experiments consistently demonstrated that (1) the use of a pre-trained CNN
with adequate fine-tuning outperformed or, in the worst case, performed as well
as a CNN trained from scratch; (2) fine-tuned CNNs were more robust to the size
of training sets than CNNs trained from scratch; (3) neither shallow tuning nor
deep tuning was the optimal choice for a particular application; and (4) our
layer-wise fine-tuning scheme could offer a practical way to reach the best
performance for the application at hand based on the amount of available data.
Speedup from a different parametrization within the Neural Network algorithm
Authors: Michael F. Zimmer
Date: 2017-05-20 00:00:00

A different parametrization of the hyperplanes is used in the neural network
algorithm. As demonstrated on several autoencoder examples it significantly
outperforms the usual parametrization, reaching lower training error values
with only a fraction of the number of epochs. It's argued that it makes it
easier to understand and initialize the parameters.
Streaming Bayesian inference: theoretical limits and mini-batch approximate message-passing
Authors: Andre Manoel, Florent Krzakala, Eric W. Tramel, Lenka Zdeborová
Date: 2017-06-02 00:00:00
Tasks: Bayesian Inference, Clustering

In statistical learning for real-world large-scale data problems, one must
often resort to "streaming" algorithms which operate sequentially on small
batches of data. In this work, we present an analysis of the
information-theoretic limits of mini-batch inference in the context of
generalized linear models and low-rank matrix factorization. In a controlled
Bayes-optimal setting, we characterize the optimal performance and phase
transitions as a function of mini-batch size. We base part of our results on a
detailed analysis of a mini-batch version of the approximate message-passing
algorithm (Mini-AMP), which we introduce. Additionally, we show that this
theoretical optimality carries over into real-data problems by illustrating
that Mini-AMP is competitive with standard streaming algorithms for clustering.
Weight Sharing is Crucial to Succesful Optimization
Authors: Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah
Date: 2017-06-02 00:00:00

Exploiting the great expressive power of Deep Neural Network architectures,
relies on the ability to train them. While current theoretical work provides,
mostly, results showing the hardness of this task, empirical evidence usually
differs from this line, with success stories in abundance. A strong position
among empirically successful architectures is captured by networks where
extensive weight sharing is used, either by Convolutional or Recurrent layers.
Additionally, characterizing specific aspects of different tasks, making them
"harder" or "easier", is an interesting direction explored both theoretically
and empirically. We consider a family of ConvNet architectures, and prove that
weight sharing can be crucial, from an optimization point of view. We explore
different notions of the frequency, of the target function, proving necessity
of the target function having some low frequency components. This necessity is
not sufficient - only with weight sharing can it be exploited, thus
theoretically separating architectures using it, from others which do not. Our
theoretical results are aligned with empirical experiments in an even more
general setting, suggesting viability of examination of the role played by
interleaving those aspects in broader families of tasks.
Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks
Authors: Samuli Laine, Tero Karras, Timo Aila, Antti Herva, Shunsuke Saito, Ronald Yu, Hao Li, Jaakko Lehtinen
Date: 2016-09-21 00:00:00

We present a real-time deep learning framework for video-based facial
performance capture -- the dense 3D tracking of an actor's face given a
monocular video. Our pipeline begins with accurately capturing a subject using
a high-end production facial capture pipeline based on multi-view stereo
tracking and artist-enhanced animations. With 5-10 minutes of captured footage,
we train a convolutional neural network to produce high-quality output,
including self-occluded regions, from a monocular video sequence of that
subject. Since this 3D facial performance capture is fully automated, our
system can drastically reduce the amount of labor involved in the development
of modern narrative-driven video games or films involving realistic digital
doubles of actors and potentially hours of animated dialogue per character. We
compare our results with several state-of-the-art monocular real-time facial
capture techniques and demonstrate compelling animation inference in
challenging areas such as eyes and lips.
Ordinal Common-sense Inference
Authors: Sheng Zhang, Rachel Rudinger, Kevin Duh, Benjamin Van Durme
Date: 2016-11-02 00:00:00
Proceeding: TACL 2017 1
Tasks: Common Sense Reasoning, Natural Language Inference

Humans have the capacity to draw common-sense inferences from natural
language: various things that are likely but not certain to hold based on
established discourse, and are rarely stated explicitly. We propose an
evaluation of automated common-sense inference based on an extension of
recognizing textual entailment: predicting ordinal human responses on the
subjective likelihood of an inference holding in a given context. We describe a
framework for extracting common-sense knowledge from corpora, which is then
used to construct a dataset for this ordinal entailment task. We train a neural
sequence-to-sequence model on this dataset, which we use to score and generate
possible inferences. Further, we annotate subsets of previously established
datasets via our ordinal annotation protocol in order to then analyze the
distinctions between these and what we have constructed.
A New Measure of Conditional Dependence
Authors: Jalal Etesami, Kun Zhang, Negar Kiyavash
Date: 2017-03-31 00:00:00

Measuring conditional dependencies among the variables of a network is of
great interest to many disciplines. This paper studies some shortcomings of the
existing dependency measures in detecting direct causal influences or their
lack of ability for group selection to capture strong dependencies and
accordingly introduces a new statistical dependency measure to overcome them.
This measure is inspired by Dobrushin's coefficients and based on the fact that
there is no dependency between $X$ and $Y$ given another variable $Z$, if and
only if the conditional distribution of $Y$ given $X=x$ and $Z=z$ does not
change when $X$ takes another realization $x'$ while $Z$ takes the same
realization $z$. We show the advantages of this measure over the related
measures in the literature. Moreover, we establish the connection between our
measure and the integral probability metric (IPM) that helps to develop
estimators of the measure with lower complexity compared to other relevant
information theoretic based measures. Finally, we show the performance of this
measure through numerical simulations.
Long-term Temporal Convolutions for Action Recognition
Authors: Gül Varol, Ivan Laptev, Cordelia Schmid
Date: 2016-04-15 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization

Typical human actions last several seconds and exhibit characteristic
spatio-temporal structure. Recent methods attempt to capture this structure and
learn action representations with convolutional neural networks. Such
representations, however, are typically learned at the level of a few video
frames failing to model actions at their full temporal extent. In this work we
learn video representations using neural networks with long-term temporal
convolutions (LTC). We demonstrate that LTC-CNN models with increased temporal
extents improve the accuracy of action recognition. We also study the impact of
different low-level representations, such as raw values of video pixels and
optical flow vector fields and demonstrate the importance of high-quality
optical flow estimation for learning accurate action models. We report
state-of-the-art results on two challenging benchmarks for human action
recognition UCF101 (92.7%) and HMDB51 (67.2%).
ICABiDAS: Intuition Centred Architecture for Big Data Analysis and Synthesis
Authors: Amit Kumar Mishra
Date: 2017-06-02 00:00:00

Humans are expert in the amount of sensory data they deal with each moment.
Human brain not only analyses these data but also starts synthesizing new
information from the existing data. The current age Big-data systems are needed
not just to analyze data but also to come up new interpretation. We believe
that the pivotal ability in human brain which enables us to do this is what is
known as "intuition". Here, we present an intuition based architecture for big
data analysis and synthesis.
Joint Matrix-Tensor Factorization for Knowledge Base Inference
Authors: Prachi Jain, Shikhar Murty, Mausam, Soumen Chakrabarti
Date: 2017-06-02 00:00:00
Tasks: Knowledge Base Completion, Knowledge Base Population, Knowledge Graph Completion, Knowledge Graph Embedding, Matrix Factorization / Decomposition

While several matrix factorization (MF) and tensor factorization (TF) models
have been proposed for knowledge base (KB) inference, they have rarely been
compared across various datasets. Is there a single model that performs well
across datasets? If not, what characteristics of a dataset determine the
performance of MF and TF models? Is there a joint TF+MF model that performs
robustly on all datasets? We perform an extensive evaluation to compare popular
KB inference models across popular datasets in the literature. In addition to
answering the questions above, we remove a limitation in the standard
evaluation protocol for MF models, propose an extension to MF models so that
they can better handle out-of-vocabulary (OOV) entity pairs, and develop a
novel combination of TF and MF models. We also analyze and explain the results
based on models and dataset characteristics. Our best model is robust, and
obtains strong results across all datasets.
WiFi based trajectory alignment, calibration and easy site survey using smart phones and foot-mounted IMUs
Authors: Yang Gu, Caifa Zhou, Andreas Wieser, Zhimin Zhou
Date: 2017-06-02 00:00:00

Foot-mounted inertial positioning (FMIP) can face problems of inertial drifts
and unknown initial states in real applications, which renders the estimated
trajectories inaccurate and not obtained in a well defined coordinate system
for matching trajectories of different users. In this paper, an approach
adopting received signal strength (RSS) measurements for Wifi access points
(APs) are proposed to align and calibrate the trajectories estimated from foot
mounted inertial measurement units (IMUs). A crowd-sourced radio map (RM) can
be built subsequently and can be used for fingerprinting based Wifi indoor
positioning (FWIP). The foundation of the proposed approach is graph based
simultaneously localization and mapping (SLAM). The nodes in the graph denote
users poses and the edges denote the pairwise constrains between the nodes. The
constrains are derived from: (1) inertial estimated trajectories; (2) vicinity
in the RSS space. With these constrains, an error functions is defined. By
minimizing the error function, the graph is optimized and the
aligned/calibrated trajectories along with the RM are acquired. The
experimental results have corroborated the effectiveness of the approach for
trajectory alignment, calibration as well as RM construction.
Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for the Diagnosis of Skin Lesions
Authors: Iván González Díaz
Date: 2017-03-06 00:00:00
Tasks: General Classification, Lesion Classification, Lesion Segmentation

This report describes our submission to the ISIC 2017 Challenge in Skin
Lesion Analysis Towards Melanoma Detection. We have participated in the Part 3:
Lesion Classification with a system for automatic diagnosis of nevus, melanoma
and seborrheic keratosis. Our approach aims to incorporate the expert knowledge
of dermatologists into the well known framework of Convolutional Neural
Networks (CNN), which have shown impressive performance in many visual
recognition tasks. In particular, we have designed several networks providing
lesion area identification, lesion segmentation into structural patterns and
final diagnosis of clinical cases. Furthermore, novel blocks for CNNs have been
designed to integrate this information with the diagnosis processing pipeline.
Detection, Recognition and Tracking of Moving Objects from Real-time Video via Visual Vocabulary Model and Species Inspired PSO
Authors: Kumar S. Ray, Anit Chakraborty, Sayandip Dutta
Date: 2017-06-02 00:00:00
Tasks: Classification, General Classification, Object

In this paper, we address the basic problem of recognizing moving objects in
video images using Visual Vocabulary model and Bag of Words and track our
object of interest in the subsequent video frames using species inspired PSO.
Initially, the shadow free images are obtained by background modelling followed
by foreground modeling to extract the blobs of our object of interest.
Subsequently, we train a cubic SVM with human body datasets in accordance with
our domain of interest for recognition and tracking. During training, using the
principle of Bag of Words we extract necessary features of certain domains and
objects for classification. Subsequently, matching these feature sets with
those of the extracted object blobs that are obtained by subtracting the shadow
free background from the foreground, we detect successfully our object of
interest from the test domain. The performance of the classification by cubic
SVM is satisfactorily represented by confusion matrix and ROC curve reflecting
the accuracy of each module. After classification, our object of interest is
tracked in the test domain using species inspired PSO. By combining the
adaptive learning tools with the efficient classification of description, we
achieve optimum accuracy in recognition of the moving objects. We evaluate our
algorithm benchmark datasets: iLIDS, VIVID, Walking2, Woman. Comparative
analysis of our algorithm against the existing state-of-the-art trackers shows
very satisfactory and competitive results.
Facies classification from well logs using an inception convolutional network
Authors: Valentin Tschannen, Matthias Delescluse, Mathieu Rodriguez, Janis Keuper
Date: 2017-06-02 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Facies Classification, General Classification

The idea to use automated algorithms to determine geological facies from well
logs is not new (see e.g Busch et al. (1987); Rabaute (1998)) but the recent
and dramatic increase in research in the field of machine learning makes it a
good time to revisit the topic. Following an exercise proposed by Dubois et al.
(2007) and Hall (2016) we employ a modern type of deep convolutional network,
called \textit{inception network} (Szegedy et al., 2015), to tackle the
supervised classification task and we discuss the methodological limits of such
problem as well as further research opportunities.
Attentive Convolutional Neural Network based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech
Authors: Michael Neumann, Ngoc Thang Vu
Date: 2017-06-02 00:00:00
Tasks: Emotion Recognition, MULTI-VIEW LEARNING, Speech Emotion Recognition

Speech emotion recognition is an important and challenging task in the realm
of human-computer interaction. Prior work proposed a variety of models and
feature sets for training a system. In this work, we conduct extensive
experiments using an attentive convolutional neural network with multi-view
learning objective function. We compare system performance using different
lengths of the input signal, different types of acoustic features and different
types of emotion speech (improvised/scripted). Our experimental results on the
Interactive Emotional Motion Capture (IEMOCAP) database reveal that the
recognition performance strongly depends on the type of speech data independent
of the choice of input features. Furthermore, we achieved state-of-the-art
results on the improvised speech data of IEMOCAP.
On Coreset Constructions for the Fuzzy $K$-Means Problem
Authors: Johannes Blömer, Sascha Brauer, Kathrin Bujna
Date: 2016-12-22 00:00:00

The fuzzy $K$-means problem is a popular generalization of the well-known
$K$-means problem to soft clusterings. We present the first coresets for fuzzy
$K$-means with size linear in the dimension, polynomial in the number of
clusters, and poly-logarithmic in the number of points. We show that these
coresets can be employed in the computation of a $(1+\epsilon)$-approximation
for fuzzy $K$-means, improving previously presented results. We further show
that our coresets can be maintained in an insertion-only streaming setting,
where data points arrive one-by-one.
Image Restoration from Patch-based Compressed Sensing Measurement
Authors: Guangtao Nie, Ying Fu, Yinqiang Zheng, Hua Huang
Date: 2017-06-02 00:00:00
Tasks: compressed sensing, Compressive Sensing, Image Reconstruction, Image Restoration

A series of methods have been proposed to reconstruct an image from
compressively sensed random measurement, but most of them have high time
complexity and are inappropriate for patch-based compressed sensing capture,
because of their serious blocky artifacts in the restoration results. In this
paper, we present a non-iterative image reconstruction method from patch-based
compressively sensed random measurement. Our method features two cascaded
networks based on residual convolution neural network to learn the end-to-end
full image restoration, which is capable of reconstructing image patches and
removing the blocky effect with low time cost. Experimental results on
synthetic and real data show that our method outperforms state-of-the-art
compressive sensing (CS) reconstruction methods with patch-based CS
measurement. To demonstrate the effectiveness of our method in more general
setting, we apply the de-block process in our method to JPEG compression
artifacts removal and achieve outstanding performance as well.