pwc-archive/papers-with-abstracts huggingface dataset:
Topic representation: finding more representative words in topic models
Authors: Jinjin Chi, Jihong Ouyang, Changchun Li, Xueyang Dong, Xi-Ming Li, Xinhua Wang
Date: 2018-10-23 00:00:00
Tasks: Reranking, Topic Models

The top word list, i.e., the top-M words with highest marginal probability in
a given topic, is the standard topic representation in topic models. Most of
recent automatical topic labeling algorithms and popular topic quality metrics
are based on it. However, we find, empirically, words in this type of top word
list are not always representative. The objective of this paper is to find more
representative top word lists for topics. To achieve this, we rerank the words
in a given topic by further considering marginal probability on words over
every other topic. The reranking list of top-M words is used to be a novel
topic representation for topic models. We investigate three reranking
methodologies, using (1) standard deviation weight, (2) standard deviation
weight with topic size and (3) Chi Square \c{hi}2statistic selection.
Experimental results on real world collections indicate that our
representations can extract more representative words for topics, agreeing with
human judgements.
Chord Recognition in Symbolic Music: A Segmental CRF Model, Segment-Level Features, and Comparative Evaluations on Classical and Popular Music
Authors: Kristen Masada, Razvan Bunescu
Date: 2018-10-22 00:00:00
Tasks: Chord Recognition

We present a new approach to harmonic analysis that is trained to segment
music into a sequence of chord spans tagged with chord labels. Formulated as a
semi-Markov Conditional Random Field (semi-CRF), this joint segmentation and
labeling approach enables the use of a rich set of segment-level features, such
as segment purity and chord coverage, that capture the extent to which the
events in an entire segment of music are compatible with a candidate chord
label. The new chord recognition model is evaluated extensively on three
corpora of classical music and a newly created corpus of rock music.
Experimental results show that the semi-CRF model performs substantially better
than previous approaches when trained on a sufficient number of labeled
examples and remains competitive when the amount of training data is limited.
Implicit Modeling with Uncertainty Estimation for Intravoxel Incoherent Motion Imaging
Authors: Lin Zhang, Valery Vishnevskiy, Andras Jakab, Orcun Goksel
Date: 2018-10-22 00:00:00
Tasks: parameter estimation

Intravoxel incoherent motion (IVIM) imaging allows contrast-agent free in
vivo perfusion quantification with magnetic resonance imaging (MRI). However,
its use is limited by typically low accuracy due to low signal-to-noise ratio
(SNR) at large gradient encoding magnitudes as well as dephasing artefacts
caused by subject motion, which is particularly challenging in fetal MRI. To
mitigate this problem, we propose an implicit IVIM signal acquisition model
with which we learn full posterior distribution of perfusion parameters using
artificial neural networks. This posterior then encapsulates the uncertainty of
the inferred parameter estimates, which we validate herein via numerical
experiments with rejection-based Bayesian sampling. Compared to
state-of-the-art IVIM estimation method of segmented least-squares fitting, our
proposed approach improves parameter estimation accuracy by 65% on synthetic
anisotropic perfusion data. On paired rescans of in vivo fetal MRI, our method
increases repeatability of parameter estimation in placenta by 46%.
Boosted Convolutional Neural Networks for Motor Imagery EEG Decoding with Multiwavelet-based Time-Frequency Conditional Granger Causality Analysis
Authors: Yang Li, Meng-Ying Lei, Xianrui Zhang, Weigang Cui, Yuzhu Guo, Ting-Wen Huang, Hua-Liang Wei
Date: 2018-10-22 00:00:00
Tasks: Binary Classification, EEG, Eeg Decoding, Electroencephalogram (EEG), Motor Imagery

Decoding EEG signals of different mental states is a challenging task for
brain-computer interfaces (BCIs) due to nonstationarity of perceptual decision
processes. This paper presents a novel boosted convolutional neural networks
(ConvNets) decoding scheme for motor imagery (MI) EEG signals assisted by the
multiwavelet-based time-frequency (TF) causality analysis. Specifically,
multiwavelet basis functions are first combined with Geweke spectral measure to
obtain high-resolution TF-conditional Granger causality (CGC) representations,
where a regularized orthogonal forward regression (ROFR) algorithm is adopted
to detect a parsimonious model with good generalization performance. The
causality images for network input preserving time, frequency and location
information of connectivity are then designed based on the TF-CGC distributions
of alpha band multichannel EEG signals. Further constructed boosted ConvNets by
using spatio-temporal convolutions as well as advances in deep learning
including cropping and boosting methods, to extract discriminative causality
features and classify MI tasks. Our proposed approach outperforms the
competition winner algorithm with 12.15% increase in average accuracy and
74.02% decrease in associated inter subject standard deviation for the same
binary classification on BCI competition-IV dataset-IIa. Experiment results
indicate that the boosted ConvNets with causality images works well in decoding
MI-EEG signals and provides a promising framework for developing MI-BCI
systems.
Block Matching Frame based Material Reconstruction for Spectral CT
Authors: Weiwen Wu, Qian Wang, Fenglin Liu, Yining Zhu, Hengyong Yu
Date: 2018-10-22 00:00:00
Tasks: Computed Tomography (CT)

Spectral computed tomography (CT) has a great potential in material identification and decomposition. To achieve high-quality material composition images and further suppress the x-ray beam hardening artifacts, we first propose a one-step material reconstruction model based on Taylor first-order expansion. Then, we develop a basic material reconstruction method named material simultaneous algebraic reconstruction technique (MSART). Considering the local similarity of each material image, we incorporate a powerful block matching frame (BMF) into the material reconstruction (MR) model and generate a BMF based MR (BMFMR) method. Because the BMFMR model contains the L0-norm problem, we adopt a split-Bregman method for optimization. The numerical simulation and physical phantom experiment results validate the correctness of the material reconstruction algorithms and demonstrate that the BMF regularization outperforms the total variation and no-local mean regularizations.
Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms
Authors: Amirreza Rezvantalab, Habib Safigholi, Somayeh Karimijeshni
Date: 2018-10-21 00:00:00
Tasks: Cancer Classification, Diagnostic, General Classification, Skin Cancer Classification

In this paper, the effectiveness and capability of convolutional neural
networks have been studied in the classification of 8 skin diseases. Different
pre-trained state-of-the-art architectures (DenseNet 201, ResNet 152, Inception
v3, InceptionResNet v2) were used and applied on 10135 dermoscopy skin images
in total (HAM10000: 10015, PH2: 120). The utilized dataset includes 8
diagnostic categories - melanoma, melanocytic nevi, basal cell carcinoma,
benign keratosis, actinic keratosis and intraepithelial carcinoma,
dermatofibroma, vascular lesions, and atypical nevi. The aim is to compare the
ability of deep learning with the performance of highly trained dermatologists.
Overall, the mean results show that all deep learning models outperformed
dermatologists (at least 11%). The best ROC AUC values for melanoma and basal
cell carcinoma are 94.40% (ResNet 152) and 99.30% (DenseNet 201) versus 82.26%
and 88.82% of dermatologists, respectively. Also, DenseNet 201 had the highest
macro and micro averaged AUC values for overall classification (98.16%, 98.79%,
respectively).
Machine Learning Methods for Track Classification in the AT-TPC
Authors: Michelle P. Kuchera, Raghuram Ramanujan, Jack Z. Taylor, Ryan R. Strauss, Daniel Bazin, Joshua Bradt, Ruiming Chen
Date: 2018-10-21 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Multi-class Classification, Transfer Learning

We evaluate machine learning methods for event classification in the
Active-Target Time Projection Chamber detector at the National Superconducting
Cyclotron Laboratory (NSCL) at Michigan State University. An automated method
to single out the desired reaction product would result in more accurate
physics results as well as a faster analysis process. Binary and multi-class
classification methods were tested on data produced by the $^{46}$Ar(p,p)
experiment run at the NSCL in September 2015. We found a Convolutional Neural
Network to be the most successful classifier of proton scattering events for
transfer learning. Results from this investigation and recommendations for
event classification in future experiments are presented.
Differentiable Fine-grained Quantization for Deep Neural Network Compression
Authors: Hsin-Pai Cheng, Yuanjun Huang, Xuyang Guo, Yifei HUANG, Feng Yan, Hai Li, Yiran Chen
Date: 2018-10-20 00:00:00
Proceeding: NIPS Workshop CDNNRIA 2018
Tasks: Neural Network Compression, Quantization

Neural networks have shown great performance in cognitive tasks. When
deploying network models on mobile devices with limited resources, weight
quantization has been widely adopted. Binary quantization obtains the highest
compression but usually results in big accuracy drop. In practice, 8-bit or
16-bit quantization is often used aiming at maintaining the same accuracy as
the original 32-bit precision. We observe different layers have different
accuracy sensitivity of quantization. Thus judiciously selecting different
precision for different layers/structures can potentially produce more
efficient models compared to traditional quantization methods by striking a
better balance between accuracy and compression rate. In this work, we propose
a fine-grained quantization approach for deep neural network compression by
relaxing the search space of quantization bitwidth from discrete to a
continuous domain. The proposed approach applies gradient descend based
optimization to generate a mixed-precision quantization scheme that outperforms
the accuracy of traditional quantization methods under the same compression
rate.
From Machine to Machine: An OCT-trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs
Authors: Felipe A. Medeiros, Alessandro A. Jammal, Atalie C. Thompson
Date: 2018-10-20 00:00:00
Tasks: Deep Learning

Previous approaches using deep learning algorithms to classify glaucomatous
damage on fundus photographs have been limited by the requirement for human
labeling of a reference training set. We propose a new approach using
spectral-domain optical coherence tomography (SDOCT) data to train a deep
learning algorithm to quantify glaucomatous structural damage on optic disc
photographs. The dataset included 32,820 pairs of optic disc photos and SDOCT
retinal nerve fiber layer (RNFL) scans from 2,312 eyes of 1,198 subjects. A
deep learning convolutional neural network was trained to assess optic disc
photographs and predict SDOCT average RNFL thickness. The performance of the
algorithm was evaluated in an independent test sample. The mean prediction of
average RNFL thickness from all 6,292 optic disc photos in the test set was
83.3$\pm$14.5 $\mu$m, whereas the mean average RNFL thickness from all
corresponding SDOCT scans was 82.5$\pm$16.8 $\mu$m (P = 0.164). There was a
very strong correlation between predicted and observed RNFL thickness values (r
= 0.832; P<0.001), with mean absolute error of the predictions of 7.39 $\mu$m.
The areas under the receiver operating characteristic curves for discriminating
glaucoma from healthy eyes with the deep learning predictions and actual SDOCT
measurements were 0.944 (95$\%$ CI: 0.912- 0.966) and 0.940 (95$\%$ CI: 0.902 -
0.966), respectively (P = 0.724). In conclusion, we introduced a novel deep
learning approach to assess optic disc photographs and provide quantitative
information about the amount of neural damage. This approach could potentially
be used to diagnose and stage glaucomatous damage from optic disc photographs.
Probabilistic Latent Semantic Analysis
Authors: Thomas Hofmann
Date: 2013-01-23 00:00:00
Tasks: Information Retrieval, Retrieval

Probabilistic Latent Semantic Analysis is a novel statistical technique for
the analysis of two-mode and co-occurrence data, which has applications in
information retrieval and filtering, natural language processing, machine
learning from text, and in related areas. Compared to standard Latent Semantic
Analysis which stems from linear algebra and performs a Singular Value
Decomposition of co-occurrence tables, the proposed method is based on a
mixture decomposition derived from a latent class model. This results in a more
principled approach which has a solid foundation in statistics. In order to
avoid overfitting, we propose a widely applicable generalization of maximum
likelihood model fitting by tempered EM. Our approach yields substantial and
consistent improvements over Latent Semantic Analysis in a number of
experiments.
Convolutional Deblurring for Natural Imaging
Authors: Mahdi S. Hosseini, Konstantinos N. Plataniotis
Date: 2018-10-25 00:00:00
Tasks: Deblurring, Denoising, Image Deblurring, Image Denoising, Image Reconstruction

In this paper, we propose a novel design of image deblurring in the form of one-shot convolution filtering that can directly convolve with naturally blurred images for restoration. The problem of optical blurring is a common disadvantage to many imaging applications that suffer from optical imperfections. Despite numerous deconvolution methods that blindly estimate blurring in either inclusive or exclusive forms, they are practically challenging due to high computational cost and low image reconstruction quality. Both conditions of high accuracy and high speed are prerequisites for high-throughput imaging platforms in digital archiving. In such platforms, deblurring is required after image acquisition before being stored, previewed, or processed for high-level interpretation. Therefore, on-the-fly correction of such images is important to avoid possible time delays, mitigate computational expenses, and increase image perception quality. We bridge this gap by synthesizing a deconvolution kernel as a linear combination of Finite Impulse Response (FIR) even-derivative filters that can be directly convolved with blurry input images to boost the frequency fall-off of the Point Spread Function (PSF) associated with the optical blur. We employ a Gaussian low-pass filter to decouple the image denoising problem for image edge deblurring. Furthermore, we propose a blind approach to estimate the PSF statistics for two Gaussian and Laplacian models that are common in many imaging pipelines. Thorough experiments are designed to test and validate the efficiency of the proposed method using 2054 naturally blurred images across six imaging applications and seven state-of-the-art deconvolution methods.
Reversible Recurrent Neural Networks
Authors: Matthew MacKay, Paul Vicol, Jimmy Ba, Roger Grosse
Date: 2018-10-25 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Decoder

Recurrent neural networks (RNNs) provide state-of-the-art performance in
processing sequential data but are memory intensive to train, limiting the
flexibility of RNN models which can be trained. Reversible RNNs---RNNs for
which the hidden-to-hidden transition can be reversed---offer a path to reduce
the memory requirements of training, as hidden states need not be stored and
instead can be recomputed during backpropagation. We first show that perfectly
reversible RNNs, which require no storage of the hidden activations, are
fundamentally limited because they cannot forget information from their hidden
state. We then provide a scheme for storing a small number of bits in order to
allow perfect reversal with forgetting. Our method achieves comparable
performance to traditional models while reducing the activation memory cost by
a factor of 10--15. We extend our technique to attention-based
sequence-to-sequence models, where it maintains performance while reducing
activation memory cost by a factor of 5--10 in the encoder, and a factor of
10--15 in the decoder.
Nuclear Norm Regularized Estimation of Panel Regression Models
Authors: Hyungsik Roger Moon, Martin Weidner
Date: 2018-10-25 00:00:00
Tasks: regression

In this paper we investigate panel regression models with interactive fixed effects. We propose two new estimation methods that are based on minimizing convex objective functions. The first method minimizes the sum of squared residuals with a nuclear (trace) norm regularization. The second method minimizes the nuclear norm of the residuals. We establish the consistency of the two resulting estimators. Those estimators have a very important computational advantage compared to the existing least squares (LS) estimator, in that they are defined as minimizers of a convex objective function. In addition, the nuclear norm penalization helps to resolve a potential identification problem for interactive fixed effect models, in particular when the regressors are low-rank and the number of the factors is unknown. We also show how to construct estimators that are asymptotically equivalent to the least squares (LS) estimator in Bai (2009) and Moon and Weidner (2017) by using our nuclear norm regularized or minimized estimators as initial values for a finite number of LS minimizing iteration steps. This iteration avoids any non-convex minimization, while the original LS estimation problem is generally non-convex, and can have multiple local minima.
Efficient Load Sampling for Worst-Case Structural Analysis Under Force Location Uncertainty
Authors: Yining Wang, Erva Ulu, Aarti Singh, Levent Burak Kara
Date: 2018-10-25 00:00:00
Tasks: Experimental Design

An important task in structural design is to quantify the structural
performance of an object under the external forces it may experience during its
use. The problem proves to be computationally very challenging as the external
forces' contact locations and magnitudes may exhibit significant variations. We
present an efficient analysis approach to determine the most critical force
contact location in such problems with force location uncertainty. Given an
input 3D model and regions on its boundary where arbitrary normal forces may
make contact, our algorithm predicts the worst-case force configuration
responsible for creating the highest stress within the object. Our approach
uses a computationally tractable experimental design method to select number of
sample force locations based on geometry only, without inspecting the stress
response that requires computationally expensive finite-element analysis. Then,
we construct a simple regression model on these samples and corresponding
maximum stresses. Combined with a simple ranking based post-processing step,
our method provides a practical solution to worst-case structural analysis
problem. The results indicate that our approach achieves significant
improvements over the existing work and brute force approaches. We demonstrate
that further speed- up can be obtained when small amount of an error tolerance
in maximum stress is allowed.
Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features
Authors: Simone Palazzo, Concetto Spampinato, Isaak Kavasidis, Daniela Giordano, Joseph Schmidt, Mubarak Shah
Date: 2018-10-25 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification, image-classification, Image Classification, Saliency Detection

This work presents a novel method of exploring human brain-visual representations, with a view towards replicating these processes in machines. The core idea is to learn plausible computational and biological representations by correlating human neural activity and natural images. Thus, we first propose a model, EEG-ChannelNet, to learn a brain manifold for EEG classification. After verifying that visual information can be extracted from EEG data, we introduce a multimodal approach that uses deep image and EEG encoders, trained in a siamese configuration, for learning a joint manifold that maximizes a compatibility measure between visual features and brain representations. We then carry out image classification and saliency detection on the learned manifold. Performance analyses show that our approach satisfactorily decodes visual information from neural signals. This, in turn, can be used to effectively supervise the training of deep learning models, as demonstrated by the high performance of image classification and saliency detection on out-of-training classes. The obtained results show that the learned brain-visual features lead to improved performance and simultaneously bring deep models more in line with cognitive neuroscience work related to visual perception and attention.
Signature moments to characterize laws of stochastic processes
Authors: Ilya Chevyrev, Harald Oberhauser
Date: 2018-10-25 00:00:00

The sequence of moments of a vector-valued random variable can characterize its law. We study the analogous problem for path-valued random variables, that is stochastic processes, by using so-called robust signature moments. This allows us to derive a metric of maximum mean discrepancy type for laws of stochastic processes and study the topology it induces on the space of laws of stochastic processes. This metric can be kernelized using the signature kernel which allows to efficiently compute it. As an application, we provide a non-parametric two-sample hypothesis test for laws of stochastic processes.
Batch Normalization Sampling
Authors: Zhaodong Chen, Lei Deng, Guoqi Li, Jiawei Sun, Xing Hu, Xin Ma, Yuan Xie
Date: 2018-10-25 00:00:00
Tasks: Computational Efficiency, GPU

Deep Neural Networks (DNNs) thrive in recent years in which Batch
Normalization (BN) plays an indispensable role. However, it has been observed
that BN is costly due to the reduction operations. In this paper, we propose
alleviating this problem through sampling only a small fraction of data for
normalization at each iteration. Specifically, we model it as a statistical
sampling problem and identify that by sampling less correlated data, we can
largely reduce the requirement of the number of data for statistics estimation
in BN, which directly simplifies the reduction operations. Based on this
conclusion, we propose two sampling strategies, "Batch Sampling" (randomly
select several samples from each batch) and "Feature Sampling" (randomly select
a small patch from each feature map of all samples), that take both
computational efficiency and sample correlation into consideration.
Furthermore, we introduce an extremely simple variant of BN, termed as Virtual
Dataset Normalization (VDN), that can normalize the activations well with few
synthetical random samples. All the proposed methods are evaluated on various
datasets and networks, where an overall training speedup by up to 20% on GPU is
practically achieved without the support of any specialized libraries, and the
loss on accuracy and convergence rate are negligible. Finally, we extend our
work to the "micro-batch normalization" problem and yield comparable
performance with existing approaches at the case of tiny batch size.