pwc-archive/papers-with-abstracts huggingface dataset:
Creative Invention Benchmark
Authors: Matthew Guzdial, Nicholas Liao, Vishwa Shah, Mark O. Riedl
Date: 2018-05-09 00:00:00

In this paper we present the Creative Invention Benchmark (CrIB), a
2000-problem benchmark for evaluating a particular facet of computational
creativity. Specifically, we address combinational p-creativity, the creativity
at play when someone combines existing knowledge to achieve a solution novel to
that individual. We present generation strategies for the five problem
categories of the benchmark and a set of initial baselines.
Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks
Authors: Charles Eckert, Xiaowei Wang, Jingcheng Wang, Arun Subramaniyan, Ravi Iyer, Dennis Sylvester, David Blaauw, Reetuparna Das
Date: 2018-05-09 00:00:00
Tasks: CPU, GPU, Quantization

This paper presents the Neural Cache architecture, which re-purposes cache
structures to transform them into massively parallel compute units capable of
running inferences for Deep Neural Networks. Techniques to do in-situ
arithmetic in SRAM arrays, create efficient data mapping and reducing data
movement are proposed. The Neural Cache architecture is capable of fully
executing convolutional, fully connected, and pooling layers in-cache. The
proposed architecture also supports quantization in-cache. Our experimental
results show that the proposed architecture can improve inference latency by
18.3x over state-of-art multi-core CPU (Xeon E5), 7.7x over server class GPU
(Titan Xp), for Inception v3 model. Neural Cache improves inference throughput
by 12.4x over CPU (2.2x over GPU), while reducing power consumption by 50% over
CPU (53% over GPU).
Long Short-Term Memory as a Dynamically Computed Element-wise Weighted Sum
Authors: Omer Levy, Kenton Lee, Nicholas FitzGerald, Luke Zettlemoyer
Date: 2018-05-09 00:00:00
Proceeding: ACL 2018 7

LSTMs were introduced to combat vanishing gradients in simple RNNs by
augmenting them with gated additive recurrent connections. We present an
alternative view to explain the success of LSTMs: the gates themselves are
versatile recurrent models that provide more representational power than
previously appreciated. We do this by decoupling the LSTM's gates from the
embedded simple RNN, producing a new class of RNNs where the recurrence
computes an element-wise weighted sum of context-independent functions of the
input. Ablations on a range of problems demonstrate that the gating mechanism
alone performs as well as an LSTM in most settings, strongly suggesting that
the gates are doing much more in practice than just alleviating vanishing
gradients.
Foundations of Sequence-to-Sequence Modeling for Time Series
Authors: Vitaly Kuznetsov, Zelda Mariet
Date: 2018-05-09 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

The availability of large amounts of time series data, paired with the
performance of deep-learning algorithms on a broad class of problems, has
recently led to significant interest in the use of sequence-to-sequence models
for time series forecasting. We provide the first theoretical analysis of this
time series forecasting framework. We include a comparison of
sequence-to-sequence modeling to classical time series models, and as such our
theory can serve as a quantitative guide for practitioners choosing between
different modeling methodologies.
Incorporating Subword Information into Matrix Factorization Word Embeddings
Authors: Alexandre Salle, Aline Villavicencio
Date: 2018-05-09 00:00:00
Proceeding: WS 2018 6
Tasks: Word Embeddings

The positive effect of adding subword information to word embeddings has been
demonstrated for predictive models. In this paper we investigate whether
similar benefits can also be derived from incorporating subwords into counting
models. We evaluate the impact of different types of subwords (n-grams and
unsupervised morphemes), with results confirming the importance of subword
information in learning representations of rare and out-of-vocabulary words.
A Continuous, Full-scope, Spatio-temporal Tracking Metric based on KL-divergence
Authors: Terrence Adams
Date: 2018-05-09 00:00:00
Tasks: Clustering, Multiple Object Tracking, Object, Object Tracking

A unified metric is given for the evaluation of object tracking systems. The
metric is inspired by KL-divergence or relative entropy, which is commonly used
to evaluate clustering techniques. Since tracking problems are fundamentally
different from clustering, the components of KL-divergence are recast to handle
various types of tracking errors (i.e., false alarms, missed detections,
merges, splits). Scoring results are given on a standard tracking dataset
(Oxford Town Centre Dataset), as well as several simulated scenarios. Also,
this new metric is compared with several other metrics including the commonly
used Multiple Object Tracking Accuracy metric. In the final section, advantages
of this metric are given including the fact that it is continuous,
parameter-less and comprehensive.
Hierarchical Transfer Convolutional Neural Networks for Image Classification
Authors: Xishuang Dong, Hsiang-Huang Wu, Yuzhong Yan, Lijun Qian
Date: 2018-03-30 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

In this paper, we address the issue of how to enhance the generalization
performance of convolutional neural networks (CNN) in the early learning stage
for image classification. This is motivated by real-time applications that
require the generalization performance of CNN to be satisfactory within limited
training time. In order to achieve this, a novel hierarchical transfer CNN
framework is proposed. It consists of a group of shallow CNNs and a cloud CNN,
where the shallow CNNs are trained firstly and then the first layers of the
trained shallow CNNs are used to initialize the first layer of the cloud CNN.
This method will boost the generalization performance of the cloud CNN
significantly, especially during the early stage of training. Experiments using
CIFAR-10 and ImageNet datasets are performed to examine the proposed method.
Results demonstrate the improvement of testing accuracy is 12% on average and
as much as 20% for the CIFAR-10 case while 5% testing accuracy improvement for
the ImageNet case during the early stage of learning. It is also shown that
universal improvements of testing accuracy are obtained across different
settings of dropout and number of shallow CNNs.
Batch-based Activity Recognition from Egocentric Photo-Streams Revisited
Authors: Alejandro Cartas, Juan Marin, Petia Radeva, Mariella Dimiccoli
Date: 2017-10-11 00:00:00
Tasks: Activity Recognition

Wearable cameras can gather large a\-mounts of image data that provide rich
visual information about the daily activities of the wearer. Motivated by the
large number of health applications that could be enabled by the automatic
recognition of daily activities, such as lifestyle characterization for habit
improvement, context-aware personal assistance and tele-rehabilitation
services, we propose a system to classify 21 daily activities from
photo-streams acquired by a wearable photo-camera. Our approach combines the
advantages of a Late Fusion Ensemble strategy relying on convolutional neural
networks at image level with the ability of recurrent neural networks to
account for the temporal evolution of high level features in photo-streams
without relying on event boundaries. The proposed batch-based approach achieved
an overall accuracy of 89.85\%, outperforming state of the art end-to-end
methodologies. These results were achieved on a dataset consists of 44,902
egocentric pictures from three persons captured during 26 days in average.
Fast and Accurate Tumor Segmentation of Histology Images using Persistent Homology and Deep Convolutional Features
Authors: Talha Qaiser, Yee-Wah Tsang, Daiki Taniyama, Naoya Sakamoto, Kazuaki Nakane, David Epstein, Nasir Rajpoot
Date: 2018-05-09 00:00:00
Tasks: Segmentation, Tumor Segmentation, whole slide images

Tumor segmentation in whole-slide images of histology slides is an important
step towards computer-assisted diagnosis. In this work, we propose a tumor
segmentation framework based on the novel concept of persistent homology
profiles (PHPs). For a given image patch, the homology profiles are derived by
efficient computation of persistent homology, which is an algebraic tool from
homology theory. We propose an efficient way of computing topological
persistence of an image, alternative to simplicial homology. The PHPs are
devised to distinguish tumor regions from their normal counterparts by modeling
the atypical characteristics of tumor nuclei. We propose two variants of our
method for tumor segmentation: one that targets speed without compromising
accuracy and the other that targets higher accuracy. The fast version is based
on the selection of exemplar image patches from a convolution neural network
(CNN) and patch classification by quantifying the divergence between the PHPs
of exemplars and the input image patch. Detailed comparative evaluation shows
that the proposed algorithm is significantly faster than competing algorithms
while achieving comparable results. The accurate version combines the PHPs and
high-level CNN features and employs a multi-stage ensemble strategy for image
patch labeling. Experimental results demonstrate that the combination of PHPs
and CNN features outperforms competing algorithms. This study is performed on
two independently collected colorectal datasets containing adenoma,
adenocarcinoma, signet and healthy cases. Collectively, the accurate tumor
segmentation produces the highest average patch-level F1-score, as compared
with competing algorithms, on malignant and healthy cases from both the
datasets. Overall the proposed framework highlights the utility of persistent
homology for histopathology image analysis.
Network cross-validation by edge sampling
Authors: Tianxi Li, Elizaveta Levina, Ji Zhu
Date: 2016-12-14 00:00:00
Tasks: Model Selection

While many statistical models and methods are now available for network analysis, resampling network data remains a challenging problem. Cross-validation is a useful general tool for model selection and parameter tuning, but is not directly applicable to networks since splitting network nodes into groups requires deleting edges and destroys some of the network structure. Here we propose a new network resampling strategy based on splitting node pairs rather than nodes applicable to cross-validation for a wide range of network model selection tasks. We provide a theoretical justification for our method in a general setting and examples of how our method can be used in specific network model selection and parameter tuning tasks. Numerical results on simulated networks and on a citation network of statisticians show that this cross-validation approach works well for model selection.
VizWiz Grand Challenge: Answering Visual Questions from Blind People
Authors: Danna Gurari, Qing Li, Abigale J. Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, Jeffrey P. Bigham
Date: 2018-02-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

The study of algorithms to automatically answer visual questions currently is
motivated by visual question answering (VQA) datasets constructed in artificial
VQA settings. We propose VizWiz, the first goal-oriented VQA dataset arising
from a natural VQA setting. VizWiz consists of over 31,000 visual questions
originating from blind people who each took a picture using a mobile phone and
recorded a spoken question about it, together with 10 crowdsourced answers per
visual question. VizWiz differs from the many existing VQA datasets because (1)
images are captured by blind photographers and so are often poor quality, (2)
questions are spoken and so are more conversational, and (3) often visual
questions cannot be answered. Evaluation of modern algorithms for answering
visual questions and deciding if a visual question is answerable reveals that
VizWiz is a challenging dataset. We introduce this dataset to encourage a
larger community to develop more generalized algorithms that can assist blind
people.
On the Limitations of Unsupervised Bilingual Dictionary Induction
Authors: Anders Søgaard, Sebastian Ruder, Ivan Vulić
Date: 2018-05-09 00:00:00
Proceeding: ACL 2018 7
Tasks: Graph Similarity, Machine Translation, Translation, Unsupervised Machine Translation

Unsupervised machine translation---i.e., not assuming any cross-lingual
supervision signal, whether a dictionary, translations, or comparable
corpora---seems impossible, but nevertheless, Lample et al. (2018) recently
proposed a fully unsupervised machine translation (MT) model. The model relies
heavily on an adversarial, unsupervised alignment of word embedding spaces for
bilingual dictionary induction (Conneau et al., 2018), which we examine here.
Our results identify the limitations of current unsupervised MT: unsupervised
bilingual dictionary induction performs much worse on morphologically rich
languages that are not dependent marking, when monolingual corpora from
different domains or different embedding algorithms are used. We show that a
simple trick, exploiting a weak supervision signal from identical words,
enables more robust induction, and establish a near-perfect correlation between
unsupervised bilingual dictionary induction performance and a previously
unexplored graph similarity metric.
The Power of Genetic Algorithms: what remains of the pMSSM?
Authors: Steven Abel, David G. Cerdeno, Sandra Robles
Date: 2018-05-09 00:00:00

Genetic Algorithms (GAs) are explored as a tool for probing new physics with
high dimensionality. We study the 19-dimensional pMSSM, including experimental
constraints from all sources and assessing the consistency of potential signals
of new physics. We show that GAs excel at making a fast and accurate diagnosis
of the cross-compatibility of a set of experimental constraints in such high
dimensional models. In the case of the pMSSM, it is found that only ${\cal
O}(10^4)$ model evaluations are required to obtain a best fit point in
agreement with much more costly MCMC scans. This efficiency allows higher
dimensional models to be falsified, and patterns in the spectrum identified,
orders of magnitude more quickly. As examples of falsification, we consider the
muon anomalous magnetic moment, and the Galactic Centre gamma-ray excess
observed by Fermi-LAT, which could in principle be explained in terms of
neutralino dark matter. We show that both observables cannot be explained
within the pMSSM, and that they provide the leading contribution to the total
goodness of the fit, with $\chi^2_{\delta a_\mu^{\mathrm{SUSY}}}\approx12$ and
$\chi^2_{\rm GCE}\approx 155$, respectively.
Facade Segmentation in the Wild
Authors: John Femiani, Wamiq Reyaz Para, Niloy Mitra, Peter Wonka
Date: 2018-05-09 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Urban facade segmentation from automatically acquired imagery, in contrast to
traditional image segmentation, poses several unique challenges. 360-degree
photospheres captured from vehicles are an effective way to capture a large
number of images, but this data presents difficult-to-model warping and
stitching artifacts. In addition, each pixel can belong to multiple facade
elements, and different facade elements (e.g., window, balcony, sill, etc.) are
correlated and vary wildly in their characteristics. In this paper, we propose
three network architectures of varying complexity to achieve multilabel
semantic segmentation of facade images while exploiting their unique
characteristics. Specifically, we propose a MULTIFACSEGNET architecture to
assign multiple labels to each pixel, a SEPARABLE architecture as a low-rank
formulation that encourages extraction of rectangular elements, and a
COMPATIBILITY network that simultaneously seeks segmentation across facade
element types allowing the network to 'see' intermediate output probabilities
of the various facade element classes. Our results on benchmark datasets show
significant improvements over existing facade segmentation approaches for the
typical facade elements. For example, on one commonly used dataset, the
accuracy scores for window(the most important architectural element) increases
from 0.91 to 0.97 percent compared to the best competing method, and comparable
improvements on other element types.
Personalized neural language models for real-world query auto completion
Authors: Nicolas Fiorini, Zhiyong Lu
Date: 2018-04-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Diversity, Language Modeling, Language Modelling

Query auto completion (QAC) systems are a standard part of search engines in
industry, helping users formulate their query. Such systems update their
suggestions after the user types each character, predicting the user's intent
using various signals - one of the most common being popularity. Recently, deep
learning approaches have been proposed for the QAC task, to specifically
address the main limitation of previous popularity-based methods: the inability
to predict unseen queries. In this work we improve previous methods based on
neural language modeling, with the goal of building an end-to-end system. We
particularly focus on using real-world data by integrating user information for
personalized suggestions when possible. We also make use of time information
and study how to increase diversity in the suggestions while studying the
impact on scalability. Our empirical results demonstrate a marked improvement
on two separate datasets over previous best methods in both accuracy and
scalability, making a step towards neural query auto-completion in production
search engines.
Category Theoretic Analysis of Photon-based Decision Making
Authors: Makoto Naruse, Song-Ju Kim, Masashi Aono, Martin Berthel, Aurélien Drezet, Serge Huant, Hirokazu Hori
Date: 2016-02-26 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Decision making is a vital function in this age of machine learning and
artificial intelligence, yet its physical realization and theoretical
fundamentals are still not completely understood. In our former study, we
demonstrated that single-photons can be used to make decisions in uncertain,
dynamically changing environments. The two-armed bandit problem was
successfully solved using the dual probabilistic and particle attributes of
single photons. In this study, we present a category theoretic modeling and
analysis of single-photon-based decision making, including a quantitative
analysis that is in agreement with the experimental results. A category
theoretic model reveals the complex interdependencies of subject matter
entities in a simplified manner, even in dynamically changing environments. In
particular, the octahedral and braid structures in triangulated categories
provide a better understanding and quantitative metrics of the underlying
mechanisms of a single-photon decision maker. This study provides both insight
and a foundation for analyzing more complex and uncertain problems, to further
machine learning and artificial intelligence.
Layered Optical Flow Estimation Using a Deep Neural Network with a Soft Mask
Authors: Xi Zhang, Di Ma, Xu Ouyang, Shanshan Jiang, Lin Gan, Gady Agam
Date: 2018-05-09 00:00:00
Tasks: Motion Estimation, Optical Flow Estimation

Using a layered representation for motion estimation has the advantage of
being able to cope with discontinuities and occlusions. In this paper, we learn
to estimate optical flow by combining a layered motion representation with deep
learning. Instead of pre-segmenting the image to layers, the proposed approach
automatically generates a layered representation of optical flow using the
proposed soft-mask module. The essential components of the soft-mask module are
maxout and fuse operations, which enable a disjoint layered representation of
optical flow and more accurate flow estimation. We show that by using masks the
motion estimate results in a quadratic function of input features in the output
layer. The proposed soft-mask module can be added to any existing optical flow
estimation networks by replacing their flow output layer. In this work, we use
FlowNet as the base network to which we add the soft-mask module. The resulting
network is tested on three well-known benchmarks with both supervised and
unsupervised flow estimation tasks. Evaluation results show that the proposed
network achieve better results compared with the original FlowNet.
Phase retrieval for Fourier Ptychography under varying amount of measurements
Authors: Lokesh Boominathan, Mayug Maniparambil, Honey Gupta, Rahul Baburajan, Kaushik Mitra
Date: 2018-05-09 00:00:00
Tasks: Retrieval

Fourier Ptychography is a recently proposed imaging technique that yields
high-resolution images by computationally transcending the diffraction blur of
an optical system. At the crux of this method is the phase retrieval algorithm,
which is used for computationally stitching together low-resolution images
taken under varying illumination angles of a coherent light source. However,
the traditional iterative phase retrieval technique relies heavily on the
initialization and also need a good amount of overlap in the Fourier domain for
the successively captured low-resolution images, thus increasing the
acquisition time and data. We show that an auto-encoder based architecture can
be adaptively trained for phase retrieval under both low overlap, where
traditional techniques completely fail, and at higher levels of overlap. For
the low overlap case we show that a supervised deep learning technique using an
autoencoder generator is a good choice for solving the Fourier ptychography
problem. And for the high overlap case, we show that optimizing the generator
for reducing the forward model error is an appropriate choice. Using
simulations for the challenging case of uncorrelated phase and amplitude, we
show that our method outperforms many of the previously proposed Fourier
ptychography phase retrieval techniques.
Secure Mobile Edge Computing in IoT via Collaborative Online Learning
Authors: Bingcong Li, Tianyi Chen, Georgios B. Giannakis
Date: 2018-05-09 00:00:00
Tasks: Edge-computing

To accommodate heterogeneous tasks in Internet of Things (IoT), a new
communication and computing paradigm termed mobile edge computing emerges that
extends computing services from the cloud to edge, but at the same time exposes
new challenges on security. The present paper studies online security-aware
edge computing under jamming attacks. Leveraging online learning tools, novel
algorithms abbreviated as SAVE-S and SAVE-A are developed to cope with the
stochastic and adversarial forms of jamming, respectively. Without utilizing
extra resources such as spectrum and transmission power to evade jamming
attacks, SAVE-S and SAVE-A can select the most reliable server to offload
computing tasks with minimal privacy and security concerns. It is analytically
established that without any prior information on future jamming and server
security risks, the proposed schemes can achieve ${\cal O}\big(\sqrt{T}\big)$
regret. Information sharing among devices can accelerate the security-aware
computing tasks. Incorporating the information shared by other devices, SAVE-S
and SAVE-A offer impressive improvements on the sublinear regret, which is
guaranteed by what is termed "value of cooperation." Effectiveness of the
proposed schemes is tested on both synthetic and real datasets.