pwc-archive/papers-with-abstracts huggingface dataset:
A Neural Embeddings Approach for Detecting Mobile Counterfeit Apps
Authors: Jathushan Rajasegaran, Suranga Seneviratne, Guillaume Jourjon
Date: 2018-04-26 00:00:00

Counterfeit apps impersonate existing popular apps in attempts to misguide
users to install them for various reasons such as collecting personal
information, spreading malware, or simply to increase their advertisement
revenue. Many counterfeits can be identified once installed, however even a
tech-savvy user may struggle to detect them before installation as app icons
and descriptions can be quite similar to the original app. To this end, this
paper proposes to use neural embeddings generated by state-of-the-art
convolutional neural networks (CNNs) to measure the similarity between images.
Our results show that for the problem of counterfeit detection a novel approach
of using style embeddings given by the Gram matrix of CNN filter responses
outperforms baseline methods such as content embeddings and SIFT features. We
show that further performance increases can be achieved by combining style
embeddings with content embeddings. We present an analysis of approximately 1.2
million apps from Google Play Store and identify a set of potential
counterfeits for top-1,000 apps. Under a conservative assumption, we were able
to find 139 apps that contain malware in a set of 6,880 apps that showed high
visual similarity to one of the top-1,000 apps in Google Play Store.
Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning
Authors: Mohammadhassan Izadyyazdanabadi, Evgenii Belykh, Michael Mooney, Jennifer Eschbacher, Peter Nakaji, Yezhou Yang, Mark C. Preul
Date: 2018-04-26 00:00:00
Tasks: Deep Learning, Diagnostic

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence
imaging technology that has the potential to increase intraoperative precision,
extend resection, and tailor surgery for malignant invasive brain tumors
because of its subcellular dimension resolution. Despite its promising
diagnostic potential, interpreting the gray tone fluorescence images can be
difficult for untrained users. In this review, we provide a detailed
description of bioinformatical analysis methodology of CLE images that begins
to assist the neurosurgeon and pathologist to rapidly connect on-the-fly
intraoperative imaging, pathology, and surgical observation into a
conclusionary system within the concept of theranostics. We present an overview
and discuss deep learning models for automatic detection of the diagnostic CLE
images and discuss various training regimes and ensemble modeling effect on the
power of deep learning predictive models. Two major approaches reviewed in this
paper include the models that can automatically classify CLE images into
diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and
models that can localize histological features on the CLE images using weakly
supervised methods. We also briefly review advances in the deep learning
approaches used for CLE image analysis in other organs. Significant advances in
speed and precision of automated diagnostic frame selection would augment the
diagnostic potential of CLE, improve operative workflow and integration into
brain tumor surgery. Such technology and bioinformatics analytics lend
themselves to improved precision, personalization, and theranostics in brain
tumor treatment.
Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals
Authors: John Alberg, Zachary C. Lipton
Date: 2017-11-13 00:00:00

On a periodic basis, publicly traded companies are required to report
fundamentals: financial data such as revenue, operating income, debt, among
others. These data points provide some insight into the financial health of a
company. Academic research has identified some factors, i.e. computed features
of the reported data, that are known through retrospective analysis to
outperform the market average. Two popular factors are the book value
normalized by market capitalization (book-to-market) and the operating income
normalized by the enterprise value (EBIT/EV). In this paper: we first show
through simulation that if we could (clairvoyantly) select stocks using factors
calculated on future fundamentals (via oracle), then our portfolios would far
outperform a standard factor approach. Motivated by this analysis, we train
deep neural networks to forecast future fundamentals based on a trailing
5-years window. Quantitative analysis demonstrates a significant improvement in
MSE over a naive strategy. Moreover, in retrospective analysis using an
industry-grade stock portfolio simulator (backtester), we show an improvement
in compounded annual return to 17.1% (MLP) vs 14.4% for a standard factor
model.
SpectralLeader: Online Spectral Learning for Single Topic Models
Authors: Tong Yu, Branislav Kveton, Zheng Wen, Hung Bui, Ole J. Mengshoel
Date: 2017-09-21 00:00:00
Tasks: Topic Models

We study the problem of learning a latent variable model from a stream of
data. Latent variable models are popular in practice because they can explain
observed data in terms of unobserved concepts. These models have been
traditionally studied in the offline setting. In the online setting, on the
other hand, the online EM is arguably the most popular algorithm for learning
latent variable models. Although the online EM is computationally efficient, it
typically converges to a local optimum. In this work, we develop a new online
learning algorithm for latent variable models, which we call SpectralLeader.
SpectralLeader always converges to the global optimum, and we derive a
sublinear upper bound on its $n$-step regret in the bag-of-words model. In both
synthetic and real-world experiments, we show that SpectralLeader performs
similarly to or better than the online EM with tuned hyper-parameters.
Accelerator-Aware Pruning for Convolutional Neural Networks
Authors: Hyeong-Ju Kang
Date: 2018-04-26 00:00:00

Convolutional neural networks have shown tremendous performance capabilities in computer vision tasks, but their excessive amounts of weight storage and arithmetic operations prevent them from being adopted in embedded environments. One of the solutions involves pruning, where certain unimportant weights are forced to have a value of zero. Many pruning schemes have been proposed, but these have mainly focused on the number of pruned weights. Previous pruning schemes scarcely considered ASIC or FPGA accelerator architectures. When these pruned networks are run on accelerators, the lack of consideration of the architecture causes some inefficiency problems, including internal buffer misalignments and load imbalances. This paper proposes a new pruning scheme that reflects accelerator architectures. In the proposed scheme, pruning is performed so that the same number of weights remain for each weight group corresponding to activations fetched simultaneously. In this way, the pruning scheme resolves the inefficiency problems, doubling the accelerator performance. Even with this constraint, the proposed pruning scheme reached a pruning ratio similar to that of previous unconstrained pruning schemes, not only on AlexNet and VGG16 but also on state-of-the-art very deep networks such as ResNet. Furthermore, the proposed scheme demonstrated a comparable pruning ratio on compact networks such as MobileNet and on slimmed networks that were already pruned in a channel-wise manner. In addition to improving the efficiency of previous sparse accelerators, it will be also shown that the proposed pruning scheme can be used to reduce the logic complexity of sparse accelerators.The pruned models are publicly available at https://github.com/HyeongjuKang/accelerator-aware-pruning.
Competitive Learning Enriches Learning Representation and Accelerates the Fine-tuning of CNNs
Authors: Takashi Shinozaki
Date: 2018-04-26 00:00:00
Tasks: Representation Learning

In this study, we propose the integration of competitive learning into
convolutional neural networks (CNNs) to improve the representation learning and
efficiency of fine-tuning. Conventional CNNs use back propagation learning, and
it enables powerful representation learning by a discrimination task. However,
it requires huge amount of labeled data, and acquisition of labeled data is
much harder than that of unlabeled data. Thus, efficient use of unlabeled data
is getting crucial for DNNs. To address the problem, we introduce unsupervised
competitive learning into the convolutional layer, and utilize unlabeled data
for effective representation learning. The results of validation experiments
using a toy model demonstrated that strong representation learning effectively
extracted bases of images into convolutional filters using unlabeled data, and
accelerated the speed of the fine-tuning of subsequent supervised back
propagation learning. The leverage was more apparent when the number of filters
was sufficiently large, and, in such a case, the error rate steeply decreased
in the initial phase of fine-tuning. Thus, the proposed method enlarged the
number of filters in CNNs, and enabled a more detailed and generalized
representation. It could provide a possibility of not only deep but broad
neural networks.
Generative Model for Heterogeneous Inference
Authors: Honggang Zhou, Yunchun Li, Hailong Yang, Wei Li, Jie Jia
Date: 2018-04-26 00:00:00
Tasks: Denoising, Image Inpainting, model

Generative models (GMs) such as Generative Adversary Network (GAN) and
Variational Auto-Encoder (VAE) have thrived these years and achieved high
quality results in generating new samples. Especially in Computer Vision, GMs
have been used in image inpainting, denoising and completion, which can be
treated as the inference from observed pixels to corrupted pixels. However,
images are hierarchically structured which are quite different from many
real-world inference scenarios with non-hierarchical features. These inference
scenarios contain heterogeneous stochastic variables and irregular mutual
dependences. Traditionally they are modeled by Bayesian Network (BN). However,
the learning and inference of BN model are NP-hard thus the number of
stochastic variables in BN is highly constrained. In this paper, we adapt
typical GMs to enable heterogeneous learning and inference in polynomial
time.We also propose an extended autoregressive (EAR) model and an EAR with
adversary loss (EARA) model and give theoretical results on their
effectiveness. Experiments on several BN datasets show that our proposed EAR
model achieves the best performance in most cases compared to other GMs. Except
for black box analysis, we've also done a serial of experiments on Markov
border inference of GMs for white box analysis and give theoretical results.
Action Categorization for Computationally Improved Task Learning and Planning
Authors: Lakshmi Nair, Sonia Chernova
Date: 2018-04-26 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft

This paper explores the problem of task learning and planning, contributing
the Action-Category Representation (ACR) to improve computational performance
of both Planning and Reinforcement Learning (RL). ACR is an algorithm-agnostic,
abstract data representation that maps objects to action categories (groups of
actions), inspired by the psychological concept of action codes. We validate
our approach in StarCraft and Lightworld domains; our results demonstrate
several benefits of ACR relating to improved computational performance of
planning and RL, by reducing the action space for the agent.
An ASP Methodology for Understanding Narratives about Stereotypical Activities
Authors: Daniela Inclezan, Qinglin Zhang, Marcello Balduccini, Ankush Israney
Date: 2018-04-26 00:00:00
Tasks: Question Answering

We describe an application of Answer Set Programming to the understanding of
narratives about stereotypical activities, demonstrated via question answering.
Substantial work in this direction was done by Erik Mueller, who modeled
stereotypical activities as scripts. His systems were able to understand a good
number of narratives, but could not process texts describing exceptional
scenarios. We propose addressing this problem by using a theory of intentions
developed by Blount, Gelfond, and Balduccini. We present a methodology in which
we substitute scripts by activities (i.e., hierarchical plans associated with
goals) and employ the concept of an intentional agent to reason about both
normal and exceptional scenarios. We exemplify the application of this
methodology by answering questions about a number of restaurant stories. This
paper is under consideration for acceptance in TPLP.
Patchnet: Interpretable Neural Networks for Image Classification
Authors: Adityanarayanan Radhakrishnan, Charles Durham, Ali Soylemezoglu, Caroline Uhler
Date: 2017-05-23 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, image-classification, Image Classification

Understanding how a complex machine learning model makes a classification
decision is essential for its acceptance in sensitive areas such as health
care. Towards this end, we present PatchNet, a method that provides the
features indicative of each class in an image using a tradeoff between
restricting global image context and classification error. We mathematically
analyze this tradeoff, demonstrate Patchnet's ability to construct sharp visual
heatmap representations of the learned features, and quantitatively compare
these features with features selected by domain experts by applying PatchNet to
the classification of benign/malignant skin lesions from the ISBI-ISIC 2017
melanoma classification challenge.
Hierarchical Density Order Embeddings
Authors: Ben Athiwaratkun, Andrew Gordon Wilson
Date: 2018-04-26 00:00:00
Proceeding: ICLR 2018 1
Tasks: Lexical Entailment, Word Embeddings

By representing words with probability densities rather than point vectors,
probabilistic word embeddings can capture rich and interpretable semantic
information and uncertainty. The uncertainty information can be particularly
meaningful in capturing entailment relationships -- whereby general words such
as "entity" correspond to broad distributions that encompass more specific
words such as "animal" or "instrument". We introduce density order embeddings,
which learn hierarchical representations through encapsulation of probability
densities. In particular, we propose simple yet effective loss functions and
distance metrics, as well as graph-based schemes to select negative samples to
better learn hierarchical density representations. Our approach provides
state-of-the-art performance on the WordNet hypernym relationship prediction
task and the challenging HyperLex lexical entailment dataset -- while retaining
a rich and interpretable density representation.
End-to-End Multimodal Speech Recognition
Authors: Shruti Palaskar, Ramon Sanabria, Florian Metze
Date: 2018-04-25 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Transcription or sub-titling of open-domain videos is still a challenging
domain for Automatic Speech Recognition (ASR) due to the data's challenging
acoustics, variable signal processing and the essentially unrestricted domain
of the data. In previous work, we have shown that the visual channel --
specifically object and scene features -- can help to adapt the acoustic model
(AM) and language model (LM) of a recognizer, and we are now expanding this
work to end-to-end approaches. In the case of a Connectionist Temporal
Classification (CTC)-based approach, we retain the separation of AM and LM,
while for a sequence-to-sequence (S2S) approach, both information sources are
adapted together, in a single model. This paper also analyzes the behavior of
CTC and S2S models on noisy video data (How-To corpus), and compares it to
results on the clean Wall Street Journal (WSJ) corpus, providing insight into
the robustness of both approaches.
Revealing patterns in HIV viral load data and classifying patients via a novel machine learning cluster summarization method
Authors: Samir Farooq, Samuel J. Weisenthal, Melissa Trayhan, Robert J. White, Kristen Bush, Peter R. Mariuz, Martin S. Zand
Date: 2018-04-25 00:00:00
Tasks: Decision Making, General Classification

HIV RNA viral load (VL) is an important outcome variable in studies of HIV
infected persons. There exists only a handful of methods which classify
patients by viral load patterns. Most methods place limits on the use of viral
load measurements, are often specific to a particular study design, and do not
account for complex, temporal variation. To address this issue, we propose a
set of four unambiguous computable characteristics (features) of time-varying
HIV viral load patterns, along with a novel centroid-based classification
algorithm, which we use to classify a population of 1,576 HIV positive clinic
patients into one of five different viral load patterns (clusters) often found
in the literature: durably suppressed viral load (DSVL), sustained low viral
load (SLVL), sustained high viral load (SHVL), high viral load suppression
(HVLS), and rebounding viral load (RVL). The centroid algorithm summarizes
these clusters in terms of their centroids and radii. We show that this allows
new viral load patterns to be assigned pattern membership based on the distance
from the centroid relative to its radius, which we term radial normalization
classification. This method has the benefit of providing an objective and
quantitative method to assign viral load pattern membership with a concise and
interpretable model that aids clinical decision making. This method also
facilitates meta-analyses by providing computably distinct HIV categories.
Finally we propose that this novel centroid algorithm could also be useful in
the areas of cluster comparison for outcomes research and data reduction in
machine learning.
Multiagent Soft Q-Learning
Authors: Ermo Wei, Drew Wicke, David Freelan, Sean Luke
Date: 2018-04-25 00:00:00
Tasks: Policy Gradient Methods, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Policy gradient methods are often applied to reinforcement learning in
continuous multiagent games. These methods perform local search in the
joint-action space, and as we show, they are susceptable to a game-theoretic
pathology known as relative overgeneralization. To resolve this issue, we
propose Multiagent Soft Q-learning, which can be seen as the analogue of
applying Q-learning to continuous controls. We compare our method to MADDPG, a
state-of-the-art approach, and show that our method achieves better
coordination in multiagent cooperative tasks, converging to better local optima
in the joint action space.
On the Dual Geometry of Laplacian Eigenfunctions
Authors: Alexander Cloninger, Stefan Steinerberger
Date: 2018-04-25 00:00:00

We discuss the geometry of Laplacian eigenfunctions $-\Delta \phi = \lambda
\phi$ on compact manifolds $(M,g)$ and combinatorial graphs $G=(V,E)$. The
'dual' geometry of Laplacian eigenfunctions is well understood on
$\mathbb{T}^d$ (identified with $\mathbb{Z}^d$) and $\mathbb{R}^n$ (which is
self-dual). The dual geometry is of tremendous role in various fields of pure
and applied mathematics. The purpose of our paper is to point out a notion of
similarity between eigenfunctions that allows to reconstruct that geometry. Our
measure of 'similarity' $ \alpha(\phi_{\lambda}, \phi_{\mu})$ between
eigenfunctions $\phi_{\lambda}$ and $\phi_{\mu}$ is given by a global average
of local correlations $$ \alpha(\phi_{\lambda}, \phi_{\mu})^2 = \|
\phi_{\lambda} \phi_{\mu} \|_{L^2}^{-2}\int_{M}{ \left( \int_{M}{ p(t,x,y)(
\phi_{\lambda}(y) - \phi_{\lambda}(x))( \phi_{\mu}(y) - \phi_{\mu}(x)) dy}
\right)^2 dx},$$ where $p(t,x,y)$ is the classical heat kernel and $e^{-t
\lambda} + e^{-t \mu} = 1$. This notion recovers all classical notions of
duality but is equally applicable to other (rough) geometries and graphs; many
numerical examples in different continuous and discrete settings illustrate the
result.
HG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering
Authors: Daniel Gribel, Thibaut Vidal
Date: 2018-04-25 00:00:00
Tasks: Clustering, global-optimization

Minimum sum-of-squares clustering (MSSC) is a widely used clustering model,
of which the popular K-means algorithm constitutes a local minimizer. It is
well known that the solutions of K-means can be arbitrarily distant from the
true MSSC global optimum, and dozens of alternative heuristics have been
proposed for this problem. However, no other algorithm has been predominantly
adopted in the literature. This may be related to differences of computational
effort, or to the assumption that a near-optimal solution of the MSSC has only
a marginal impact on clustering validity. In this article, we dispute this
belief. We introduce an efficient population-based metaheuristic that uses
K-means as a local search in combination with problem-tailored crossover,
mutation, and diversification operators. This algorithm can be interpreted as a
multi-start K-means, in which the initial center positions are carefully
sampled based on the search history. The approach is scalable and accurate,
outperforming all recent state-of-the-art algorithms for MSSC in terms of
solution quality, measured by the depth of local minima. This enhanced accuracy
leads to clusters which are significantly closer to the ground truth than those
of other algorithms, for overlapping Gaussian-mixture datasets with a large
number of features. Therefore, improved global optimization methods appear to
be essential to better exploit the MSSC model in high dimension.
Improved Classification Based on Deep Belief Networks
Authors: Jaehoon Koo, Diego Klabjan
Date: 2018-04-25 00:00:00
Tasks: Classification, General Classification

For better classification generative models are used to initialize the model and model features before training a classifier. Typically it is needed to solve separate unsupervised and supervised learning problems. Generative restricted Boltzmann machines and deep belief networks are widely used for unsupervised learning. We developed several supervised models based on DBN in order to improve this two-phase strategy. Modifying the loss function to account for expectation with respect to the underlying generative model, introducing weight bounds, and multi-level programming are applied in model development. The proposed models capture both unsupervised and supervised objectives effectively. The computational study verifies that our models perform better than the two-phase training approach.