pwc-archive/papers-with-abstracts huggingface dataset:
Coreset-Based Neural Network Compression
Authors: Abhimanyu Dubey, Moitreya Chatterjee, Narendra Ahuja
Date: 2018-07-25 00:00:00
Proceeding: ECCV 2018 9
Tasks: Neural Network Compression, Quantization

We propose a novel Convolutional Neural Network (CNN) compression algorithm
based on coreset representations of filters. We exploit the redundancies extant
in the space of CNN weights and neuronal activations (across samples) in order
to obtain compression. Our method requires no retraining, is easy to implement,
and obtains state-of-the-art compression performance across a wide variety of
CNN architectures. Coupled with quantization and Huffman coding, we create
networks that provide AlexNet-like accuracy, with a memory footprint that is
$832\times$ smaller than the original AlexNet, while also introducing
significant reductions in inference time as well. Additionally these compressed
networks when fine-tuned, successfully generalize to other domains as well.
Deep Contextual Multi-armed Bandits
Authors: Mark Collier, Hector Urdiales Llorens
Date: 2018-07-25 00:00:00
Tasks: Marketing, Multi-Armed Bandits, Thompson Sampling

Contextual multi-armed bandit problems arise frequently in important
industrial applications. Existing solutions model the context either linearly,
which enables uncertainty driven (principled) exploration, or non-linearly, by
using epsilon-greedy exploration policies. Here we present a deep learning
framework for contextual multi-armed bandits that is both non-linear and
enables principled exploration at the same time. We tackle the exploration vs.
exploitation trade-off through Thompson sampling by exploiting the connection
between inference time dropout and sampling from the posterior over the weights
of a Bayesian neural network. In order to adjust the level of exploration
automatically as more data is made available to the model, the dropout rate is
learned rather than considered a hyperparameter. We demonstrate that our
approach substantially reduces regret on two tasks (the UCI Mushroom task and
the Casino Parity task) when compared to 1) non-contextual bandits, 2)
epsilon-greedy deep contextual bandits, and 3) fixed dropout rate deep
contextual bandits. Our approach is currently being applied to marketing
optimization problems at HubSpot.
Judging a Book by its Description : Analyzing Gender Stereotypes in the Man Bookers Prize Winning Fiction
Authors: Nishtha Madaan, Sameep Mehta, Shravika Mittal, Ashima Suvarna
Date: 2018-07-25 00:00:00

The presence of gender stereotypes in many aspects of society is a well-known
phenomenon. In this paper, we focus on studying and quantifying such
stereotypes and bias in the Man Bookers Prize winning fiction. We consider 275
books shortlisted for Man Bookers Prize between 1969 and 2017. The gender bias
is analyzed by semantic modeling of book descriptions on Goodreads. This
reveals the pervasiveness of gender bias and stereotype in the books on
different features like occupation, introductions and actions associated to the
characters in the book.
Multi-view Reconstructive Preserving Embedding for Dimension Reduction
Authors: Huibing Wang, Lin Feng, Adong Kong, Bo Jin
Date: 2018-07-25 00:00:00
Tasks: Dimensionality Reduction, Document Classification, Face Recognition, Image Retrieval, Retrieval

With the development of feature extraction technique, one sample always can
be represented by multiple features which locate in high-dimensional space.
Multiple features can re ect various perspectives of one same sample, so there
must be compatible and complementary information among the multiple views.
Therefore, it's natural to integrate multiple features together to obtain
better performance. However, most multi-view dimension reduction methods cannot
handle multiple features from nonlinear space with high dimensions. To address
this problem, we propose a novel multi-view dimension reduction method named
Multi-view Reconstructive Preserving Embedding (MRPE) in this paper. MRPE
reconstructs each sample by utilizing its k nearest neighbors. The similarities
between each sample and its neighbors are primely mapped into lower-dimensional
space in order to preserve the underlying neighborhood structure of the
original manifold. MRPE fully exploits correlations between each sample and its
neighbors from multiple views by linear reconstruction. Furthermore, MRPE
constructs an optimization problem and derives an iterative procedure to obtain
the low-dimensional embedding. Various evaluations based on the applications of
document classification, face recognition and image retrieval demonstrate the
effectiveness of our proposed approach on multi-view dimension reduction.
Stripe-Based Fragility Analysis of Concrete Bridge Classes Using Machine Learning Techniques
Authors: Sujith Mangalathu, Jong-Su Jeon
Date: 2018-07-25 00:00:00
Tasks: BIG-bench Machine Learning

A framework for the generation of bridge-specific fragility utilizing the
capabilities of machine learning and stripe-based approach is presented in this
paper. The proposed methodology using random forests helps to generate or
update fragility curves for a new set of input parameters with less
computational effort and expensive re-simulation. The methodology does not
place any assumptions on the demand model of various components and helps to
identify the relative importance of each uncertain variable in their seismic
demand model. The methodology is demonstrated through the case studies of
multi-span concrete bridges in California. Geometric, material and structural
uncertainties are accounted for in the generation of bridge models and
fragility curves. It is also noted that the traditional lognormality assumption
on the demand model leads to unrealistic fragility estimates. Fragility results
obtained the proposed methodology curves can be deployed in risk assessment
platform such as HAZUS for regional loss estimation.
Method for Hybrid Precision Convolutional Neural Network Representation
Authors: Mo'taz Al-Hami, Marcin Pietron, Rishi Kumar, Raul A. Casas, Samer L. Hijazi, Chris Rowen
Date: 2018-07-24 00:00:00

This invention addresses fixed-point representations of convolutional neural
networks (CNN) in integrated circuits. When quantizing a CNN for a practical
implementation there is a trade-off between the precision used for operations
between coefficients and data and the accuracy of the system. A homogenous
representation may not be sufficient to achieve the best level of performance
at a reasonable cost in implementation complexity or power consumption.
Parsimonious ways of representing data and coefficients are needed to improve
power efficiency and throughput while maintaining accuracy of a CNN.
Non-local Low-rank Cube-based Tensor Factorization for Spectral CT Reconstruction
Authors: Weiwen Wu, Fenglin Liu, Yanbo Zhang, Qian Wang, Hengyong Yu
Date: 2018-07-24 00:00:00
Tasks: Clustering, Computed Tomography (CT), CT Reconstruction

Spectral computed tomography (CT) reconstructs material-dependent attenuation
images with the projections of multiple narrow energy windows, it is meaningful
for material identification and decomposition. Unfortunately, the multi-energy
projection dataset always contains strong complicated noise and result in the
projections has a lower signal-noise-ratio (SNR). Very recently, the
spatial-spectral cube matching frame (SSCMF) was proposed to explore the
non-local spatial-spectrum similarities for spectral CT. The method constructs
such a group by clustering up a series of non-local spatial-spectrum cubes. The
small size of spatial patch for such a group make SSCMF fails to encode the
sparsity and low-rank properties. In addition, the hard-thresholding and
collaboration filtering operation in the SSCMF are also rough to recover the
image features and spatial edges. While for all steps are operated on 4-D
group, we may not afford such huge computational and memory load in practical.
To avoid the above limitation and further improve image quality, we first
formulate a non-local cube-based tensor instead of the group to encode the
sparsity and low-rank properties. Then, as a new regularizer,
Kronecker-Basis-Representation (KBR) tensor factorization is employed into a
basic spectral CT reconstruction model to enhance the ability of extracting
image features and protecting spatial edges, generating the non-local low-rank
cube-based tensor factorization (NLCTF) method. Finally, the split-Bregman
strategy is adopted to solve the NLCTF model. Both numerical simulations and
realistic preclinical mouse studies are performed to validate and assess the
NLCTF algorithm. The results show that the NLCTF method outperforms the other
competitors.
Sokoto Coventry Fingerprint Dataset
Authors: Yahaya Isah Shehu, Ariel Ruiz-Garcia, Vasile Palade, Anne James
Date: 2018-07-24 00:00:00
Tasks: Gender Prediction

This paper presents the Sokoto Coventry Fingerprint Dataset (SOCOFing), a
biometric fingerprint database designed for academic research purposes.
SOCOFing is made up of 6,000 fingerprint images from 600 African subjects.
SOCOFing contains unique attributes such as labels for gender, hand and finger
name as well as synthetically altered versions with three different levels of
alteration for obliteration, central rotation, and z-cut. The dataset is freely
available for noncommercial research purposes at:
https://www.kaggle.com/ruizgara/socofing
Multi-Scale Gradual Integration CNN for False Positive Reduction in Pulmonary Nodule Detection
Authors: Bum-Chae Kim, Jun-Sik Choi, Heung-Il Suk
Date: 2018-07-24 00:00:00

Lung cancer is a global and dangerous disease, and its early detection is
crucial to reducing the risks of mortality. In this regard, it has been of
great interest in developing a computer-aided system for pulmonary nodules
detection as early as possible on thoracic CT scans. In general, a nodule
detection system involves two steps: (i) candidate nodule detection at a high
sensitivity, which captures many false positives and (ii) false positive
reduction from candidates. However, due to the high variation of nodule
morphological characteristics and the possibility of mistaking them for
neighboring organs, candidate nodule detection remains a challenge. In this
study, we propose a novel Multi-scale Gradual Integration Convolutional Neural
Network (MGI-CNN), designed with three main strategies: (1) to use multi-scale
inputs with different levels of contextual information, (2) to use abstract
information inherent in different input scales with gradual integration, and
(3) to learn multi-stream feature integration in an end-to-end manner. To
verify the efficacy of the proposed network, we conducted exhaustive
experiments on the LUNA16 challenge datasets by comparing the performance of
the proposed method with state-of-the-art methods in the literature. On two
candidate subsets of the LUNA16 dataset, i.e., V1 and V2, our method achieved
an average CPM of 0.908 (V1) and 0.942 (V2), outperforming comparable methods
by a large margin. Our MGI-CNN is implemented in Python using TensorFlow and
the source code is available from 'https://github.com/ku-milab/MGICNN.'
Understanding and representing the semantics of large structured documents
Authors: Muhammad Mahbubur Rahman, Tim Finin
Date: 2018-07-24 00:00:00
Tasks: Articles

Understanding large, structured documents like scholarly articles, requests
for proposals or business reports is a complex and difficult task. It involves
discovering a document's overall purpose and subject(s), understanding the
function and meaning of its sections and subsections, and extracting low level
entities and facts about them. In this research, we present a deep learning
based document ontology to capture the general purpose semantic structure and
domain specific semantic concepts from a large number of academic articles and
business documents. The ontology is able to describe different functional parts
of a document, which can be used to enhance semantic indexing for a better
understanding by human beings and machines. We evaluate our models through
extensive experiments on datasets of scholarly articles from arXiv and Request
for Proposal documents.
Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography
Authors: Chuanqi Tan, Fuchun Sun, Wenchang Zhang, Jianhua Chen, Chunfang Liu
Date: 2018-07-24 00:00:00
Tasks: Brain Computer Interface, Classification, EEG, Electroencephalogram (EEG), General Classification, Optical Flow Estimation, Video Classification

Electroencephalography (EEG) has become the most significant input signal for
brain computer interface (BCI) based systems. However, it is very difficult to
obtain satisfactory classification accuracy due to traditional methods can not
fully exploit multimodal information. Herein, we propose a novel approach to
modeling cognitive events from EEG data by reducing it to a video
classification problem, which is designed to preserve the multimodal
information of EEG. In addition, optical flow is introduced to represent the
variant information of EEG. We train a deep neural network (DNN) with
convolutional neural network (CNN) and recurrent neural network (RNN) for the
EEG classification task by using EEG video and optical flow. The experiments
demonstrate that our approach has many advantages, such as more robustness and
more accuracy in EEG classification tasks. According to our approach, we
designed a mixed BCI-based rehabilitation support system to help stroke
patients perform some basic operations.
A Capsule Network for Traffic Speed Prediction in Complex Road Networks
Authors: Youngjoo Kim, Peng Wang, Yifei Zhu, Lyudmila Mihaylova
Date: 2018-07-23 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

This paper proposes a deep learning approach for traffic flow prediction in
complex road networks. Traffic flow data from induction loop sensors are
essentially a time series, which is also spatially related to traffic in
different road segments. The spatio-temporal traffic data can be converted into
an image where the traffic data are expressed in a 3D space with respect to
space and time axes. Although convolutional neural networks (CNNs) have been
showing surprising performance in understanding images, they have a major
drawback. In the max pooling operation, CNNs are losing important information
by locally taking the highest activation values. The inter-relationship in
traffic data measured by sparsely located sensors in different time intervals
should not be neglected in order to obtain accurate predictions. Thus, we
propose a neural network with capsules that replaces max pooling by dynamic
routing. This is the first approach that employs the capsule network on a time
series forecasting problem, to our best knowledge. Moreover, an experiment on
real traffic speed data measured in the Santander city of Spain demonstrates
the proposed method outperforms the state-of-the-art method based on a CNN by
13.1% in terms of root mean squared error.
A Trace Lasso Regularized L1-norm Graph Cut for Highly Correlated Noisy Hyperspectral Image
Authors: Ramanarayan Mohanty, S. L. Happy, Nilesh Suthar, Aurobinda Routray
Date: 2018-07-22 00:00:00
Tasks: Dimensionality Reduction

This work proposes an adaptive trace lasso regularized L1-norm based graph
cut method for dimensionality reduction of Hyperspectral images, called as
`Trace Lasso-L1 Graph Cut' (TL-L1GC). The underlying idea of this method is to
generate the optimal projection matrix by considering both the sparsity as well
as the correlation of the data samples. The conventional L2-norm used in the
objective function is sensitive to noise and outliers. Therefore, in this work
L1-norm is utilized as a robust alternative to L2-norm. Besides, for further
improvement of the results, we use a penalty function of trace lasso with the
L1GC method. It adaptively balances the L2-norm and L1-norm simultaneously by
considering the data correlation along with the sparsity. We obtain the optimal
projection matrix by maximizing the ratio of between-class dispersion to
within-class dispersion using L1-norm with trace lasso as the penalty.
Furthermore, an iterative procedure for this TL-L1GC method is proposed to
solve the optimization function. The effectiveness of this proposed method is
evaluated on two benchmark HSI datasets.
Spatial Correlation and Value Prediction in Convolutional Neural Networks
Authors: Gil Shomron, Uri Weiser
Date: 2018-07-21 00:00:00
Tasks: General Classification, image-classification, Image Classification, Prediction, speech-recognition, Speech Recognition, Value prediction

Convolutional neural networks (CNNs) are a widely used form of deep neural
networks, introducing state-of-the-art results for different problems such as
image classification, computer vision tasks, and speech recognition. However,
CNNs are compute intensive, requiring billions of multiply-accumulate (MAC)
operations per input. To reduce the number of MACs in CNNs, we propose a value
prediction method that exploits the spatial correlation of zero-valued
activations within the CNN output feature maps, thereby saving convolution
operations. Our method reduces the number of MAC operations by 30.4%, averaged
on three modern CNNs for ImageNet, with top-1 accuracy degradation of 1.7%, and
top-5 accuracy degradation of 1.1%.
A post-processing method to improve the white matter hyperintensity segmentation accuracy for randomly-initialized U-net
Authors: Yue Zhang, Wanli Chen, Yi-fan Chen, Xiaoying Tang
Date: 2018-07-21 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

White matter hyperintensity (WMH) is commonly found in elder individuals and
appears to be associated with brain diseases. U-net is a convolutional network
that has been widely used for biomedical image segmentation. Recently, U-net
has been successfully applied to WMH segmentation. Random initialization is
usally used to initialize the model weights in the U-net. However, the model
may coverage to different local optima with different randomly initialized
weights. We find a combination of thresholding and averaging the outputs of
U-nets with different random initializations can largely improve the WMH
segmentation accuracy. Based on this observation, we propose a post-processing
technique concerning the way how averaging and thresholding are conducted.
Specifically, we first transfer the score maps from three U-nets to binary
masks via thresholding and then average those binary masks to obtain the final
WMH segmentation. Both quantitative analysis (via the Dice similarity
coefficient) and qualitative analysis (via visual examinations) reveal the
superior performance of the proposed method. This post-processing technique is
independent of the model used. As such, it can also be applied to situations
where other deep learning models are employed, especially when random
initialization is adopted and pre-training is unavailable.
Modular Mechanistic Networks: On Bridging Mechanistic and Phenomenological Models with Deep Neural Networks in Natural Language Processing
Authors: Simon Dobnik, John D. Kelleher
Date: 2018-07-21 00:00:00
Tasks: Deep Learning

Natural language processing (NLP) can be done using either top-down (theory
driven) and bottom-up (data driven) approaches, which we call mechanistic and
phenomenological respectively. The approaches are frequently considered to
stand in opposition to each other. Examining some recent approaches in deep
learning we argue that deep neural networks incorporate both perspectives and,
furthermore, that leveraging this aspect of deep learning may help in solving
complex problems within language technology, such as modelling language and
perception in the domain of spatial cognition.
Generic Camera Attribute Control using Bayesian Optimization
Authors: Joowan Kim, Younggun Cho, Ayoung Kim
Date: 2018-07-21 00:00:00
Tasks: Attribute, Bayesian Optimization, Image Generation

Cameras are the most widely exploited sensor in both robotics and computer
vision communities. Despite their popularity, two dominant attributes (i.e.,
gain and exposure time) have been determined empirically and images are
captured in very passive manner. In this paper, we present an active and
generic camera attribute control scheme using Bayesian optimization. We extend
from our previous work [1] in two aspects. First, we propose a method that
jointly controls camera gain and exposure time. Secondly, to speed up the
Bayesian optimization process, we introduce image synthesis using the camera
response function (CRF). These synthesized images allowed us to diminish the
image acquisition time during the Bayesian optimization phase, substantially
improving overall control performance. The proposed method is validated both in
an indoor and an outdoor environment where light condition rapidly changes.
Supplementary material is available at https://youtu.be/XTYR_Mih3OU .
Filter Distillation for Network Compression
Authors: Xavier Suau, Luca Zappella, Nicholas Apostoloff
Date: 2018-07-20 00:00:00
Proceeding: ICLR 2019 5
Tasks: Domain Adaptation, Neural Network Compression, Philosophy

In this paper we introduce Principal Filter Analysis (PFA), an easy to use and effective method for neural network compression. PFA exploits the correlation between filter responses within network layers to recommend a smaller network that maintain as much as possible the accuracy of the full model. We propose two algorithms: the first allows users to target compression to specific network property, such as number of trainable variable (footprint), and produces a compressed model that satisfies the requested property while preserving the maximum amount of spectral energy in the responses of each layer, while the second is a parameter-free heuristic that selects the compression used at each layer by trying to mimic an ideal set of uncorrelated responses. Since PFA compresses networks based on the correlation of their responses we show in our experiments that it gains the additional flexibility of adapting each architecture to a specific domain while compressing. PFA is evaluated against several architectures and datasets, and shows considerable compression rates without compromising accuracy, e.g., for VGG-16 on CIFAR-10, CIFAR-100 and ImageNet, PFA achieves a compression rate of 8x, 3x, and 1.4x with an accuracy gain of 0.4%, 1.4% points, and 2.4% respectively. Our tests show that PFA is competitive with state-of-the-art approaches while removing adoption barriers thanks to its practical implementation, intuitive philosophy and ease of use.
The Helmholtz Method: Using Perceptual Compression to Reduce Machine Learning Complexity
Authors: Gerald Friedland, Jingkang Wang, Ruoxi Jia, Bo Li
Date: 2018-07-10 00:00:00
Tasks: BIG-bench Machine Learning

This paper proposes a fundamental answer to a frequently asked question in
multimedia computing and machine learning: Do artifacts from perceptual
compression contribute to error in the machine learning process and if so, how
much? Our approach to the problem is a reinterpretation of the Helmholtz Free
Energy formula from physics to explain the relationship between content and
noise when using sensors (such as cameras or microphones) to capture multimedia
data. The reinterpretation allows a bit-measurement of the noise contained in
images, audio, and video by combining a classifier with perceptual compression,
such as JPEG or MP3. Our experiments on CIFAR-10 as well as Fraunhofer's
IDMT-SMT-Audio-Effects dataset indicate that, at the right quality level,
perceptual compression is actually not harmful but contributes to a significant
reduction of complexity of the machine learning process. That is, our noise
quantification method can be used to speed up the training of deep learning
classifiers significantly while maintaining, or sometimes even improving,
overall classification accuracy. Moreover, our results provide insights into
the reasons for the success of deep learning.