pwc-archive/papers-with-abstracts huggingface dataset:
Non-Parametric Transformation Networks
Authors: Dipan K. Pal, Marios Savvides
Date: 2018-01-14 00:00:00
Tasks: Translation

ConvNets, through their architecture, only enforce invariance to translation.
In this paper, we introduce a new class of deep convolutional architectures
called Non-Parametric Transformation Networks (NPTNs) which can learn
\textit{general} invariances and symmetries directly from data. NPTNs are a
natural generalization of ConvNets and can be optimized directly using gradient
descent. Unlike almost all previous works in deep architectures, they make no
assumption regarding the structure of the invariances present in the data and
in that aspect are flexible and powerful. We also model ConvNets and NPTNs
under a unified framework called Transformation Networks (TN), which yields a
better understanding of the connection between the two. We demonstrate the
efficacy of NPTNs on data such as MNIST with extreme transformations and
CIFAR10 where they outperform baselines, and further outperform several recent
algorithms on ETH-80. They do so while having the same number of parameters. We
also show that they are more effective than ConvNets in modelling symmetries
and invariances from data, without the explicit knowledge of the added
arbitrary nuisance transformations. Finally, we replace ConvNets with NPTNs
within Capsule Networks and show that this enables Capsule Nets to perform even
better.
Robust Optimization over Multiple Domains
Authors: Qi Qian, Shenghuo Zhu, Jiasheng Tang, Rong Jin, Baigui Sun, Hao Li
Date: 2018-05-19 00:00:00
Tasks: BIG-bench Machine Learning, Cloud Computing, Fine-Grained Visual Categorization

In this work, we study the problem of learning a single model for multiple
domains. Unlike the conventional machine learning scenario where each domain
can have the corresponding model, multiple domains (i.e., applications/users)
may share the same machine learning model due to maintenance loads in cloud
computing services. For example, a digit-recognition model should be applicable
to hand-written digits, house numbers, car plates, etc. Therefore, an ideal
model for cloud computing has to perform well at each applicable domain. To
address this new challenge from cloud computing, we develop a framework of
robust optimization over multiple domains. In lieu of minimizing the empirical
risk, we aim to learn a model optimized to the adversarial distribution over
multiple domains. Hence, we propose to learn the model and the adversarial
distribution simultaneously with the stochastic algorithm for efficiency.
Theoretically, we analyze the convergence rate for convex and non-convex
models. To our best knowledge, we first study the convergence rate of learning
a robust non-convex model with a practical algorithm. Furthermore, we
demonstrate that the robustness of the framework and the convergence rate can
be further enhanced by appropriate regularizers over the adversarial
distribution. The empirical study on real-world fine-grained visual
categorization and digits recognition tasks verifies the effectiveness and
efficiency of the proposed framework.
Fast Object Classification in Single-pixel Imaging
Authors: Shuming Jiao
Date: 2018-05-19 00:00:00
Tasks: Classification, General Classification, Image Reconstruction, Object

In single-pixel imaging (SPI), the target object is illuminated with varying
patterns sequentially and an intensity sequence is recorded by a single-pixel
detector without spatial resolution. A high quality object image can only be
computationally reconstructed after a large number of illuminations, with
disadvantages of long imaging time and high cost. Conventionally, object
classification is performed after a reconstructed object image with good
fidelity is available. In this paper, we propose to classify the target object
with a small number of illuminations in a fast manner for Fourier SPI. A naive
Bayes classifier is employed to classify the target objects based on the
single-pixel intensity sequence without any image reconstruction and each
sequence element is regarded as an object feature in the classifier. Simulation
results demonstrate our proposed scheme can classify the number digit object
images with high accuracy (e.g. 80% accuracy using only 13 illuminations, at a
sampling ratio of 0.3%).
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
Authors: Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, Minyi Guo
Date: 2018-03-09 00:00:00
Tasks: Click-Through Rate Prediction, Collaborative Filtering, News Recommendation, Recommendation Systems

To address the sparsity and cold start problem of collaborative filtering,
researchers usually make use of side information, such as social networks or
item attributes, to improve recommendation performance. This paper considers
the knowledge graph as the source of side information. To address the
limitations of existing embedding-based and path-based methods for
knowledge-graph-aware recommendation, we propose Ripple Network, an end-to-end
framework that naturally incorporates the knowledge graph into recommender
systems. Similar to actual ripples propagating on the surface of water, Ripple
Network stimulates the propagation of user preferences over the set of
knowledge entities by automatically and iteratively extending a user's
potential interests along links in the knowledge graph. The multiple "ripples"
activated by a user's historically clicked items are thus superposed to form
the preference distribution of the user with respect to a candidate item, which
could be used for predicting the final clicking probability. Through extensive
experiments on real-world datasets, we demonstrate that Ripple Network achieves
substantial gains in a variety of scenarios, including movie, book and news
recommendation, over several state-of-the-art baselines.
Sequential adaptive elastic net approach for single-snapshot source localization
Authors: Muhammad Naveed Tabassum, Esa Ollila
Date: 2018-05-19 00:00:00

This paper proposes efficient algorithms for accurate recovery of
direction-of-arrival (DoA) of sources from single-snapshot measurements using
compressed beamforming (CBF). In CBF, the conventional sensor array signal
model is cast as an underdetermined complex-valued linear regression model and
sparse signal recovery methods are used for solving the DoA finding problem. We
develop a complex-valued pathwise weighted elastic net (c-PW-WEN) algorithm
that finds solutions at knots of penalty parameter values over a path (or grid)
of EN tuning parameter values. c-PW-WEN also computes Lasso or weighted Lasso
in its path. We then propose a sequential adaptive EN (SAEN) method that is
based on c-PW-WEN algorithm with adaptive weights that depend on the previous
solution. Extensive simulation studies illustrate that SAEN improves the
probability of exact recovery of true support compared to conventional sparse
signal recovery approaches such as Lasso, elastic net or orthogonal matching
pursuit in several challenging multiple target scenarios. The effectiveness of
SAEN is more pronounced in the presence of high mutual coherence.
Chief complaint classification with recurrent neural networks
Authors: Scott H Lee, Drew Levin, Pat Finley, Charles M Heilig
Date: 2018-05-19 00:00:00
Tasks: Classification, Diagnostic, General Classification

Syndromic surveillance detects and monitors individual and population health
indicators through sources such as emergency department records. Automated
classification of these records can improve outbreak detection speed and
diagnosis accuracy. Current syndromic systems rely on hand-coded keyword-based
methods to parse written fields and may benefit from the use of modern
supervised-learning classifier models. In this paper we implement two recurrent
neural network models based on long short-term memory (LSTM) and gated
recurrent unit (GRU) cells and compare them to two traditional bag-of-words
classifiers: multinomial naive Bayes (MNB) and a support vector machine (SVM).
The MNB classifier is one of only two machine learning algorithms currently
being used for syndromic surveillance. All four models are trained to predict
diagnostic code groups as defined by Clinical Classification Software, first to
predict from discharge diagnosis, then from chief complaint fields. The
classifiers are trained on 3.6 million de-identified emergency department
records from a single United States jurisdiction. We compare performance of
these models primarily using the F1 score. Using discharge diagnoses, the LSTM
classifier performs best, though all models exhibit an F1 score above 96.00.
The GRU performs best on chief complaints (F1=47.38), and MNB with bigrams
performs worst (F1=39.40). Certain syndrome types are easier to detect than
others. For examples, chief complaints using the GRU model predicts
alcohol-related disorders well (F1=78.91) but predicts influenza poorly
(F1=14.80). In all instances, the RNN models outperformed the bag-of-word
classifiers, suggesting deep learning models could substantially improve the
automatic classification of unstructured text for syndromic surveillance.
Reliable counting of weakly labeled concepts by a single spiking neuron model
Authors: Hannes Rapp, Martin Paul Nawrot, Merav Stern
Date: 2018-05-19 00:00:00
Tasks: Cloud Computing, Decision Making, General Classification, Multiple Instance Learning

Making an informed, correct and quick decision can be life-saving. It's
crucial for animals during an escape behaviour or for autonomous cars during
driving. The decision can be complex and may involve an assessment of the
amount of threats present and the nature of each threat. Thus, we should expect
early sensory processing to supply classification information fast and
accurately, even before relying the information to higher brain areas or more
complex system components downstream. Today, advanced convolutional artificial
neural networks can successfully solve visual detection and classification
tasks and are commonly used to build complex decision making systems. However,
in order to perform well on these tasks they require increasingly complex,
"very deep" model structure, which is costly in inference run-time, energy
consumption and number of training samples, only trainable on cloud-computing
clusters. A single spiking neuron has been shown to be able to solve
recognition tasks for homogeneous Poisson input statistics, a commonly used
model for spiking activity in the neocortex. When modeled as leaky integrate
and fire with gradient decent learning algorithm it was shown to posses a
variety of complex computational capabilities. Here we improve its
implementation. We also account for more natural stimulus generated inputs that
deviate from this homogeneous Poisson spiking. The improved gradient-based
local learning rule allows for significantly better and stable generalization.
We also show that with its improved capabilities it can count weakly labeled
concepts by applying our model to a problem of multiple instance learning (MIL)
with counting where labels are only available for collections of concepts. In
this counting MNIST task the neuron exploits the improved implementation and
outperforms conventional ConvNet architecture under similar condtions.
Optimizing the F-measure for Threshold-free Salient Object Detection
Authors: Kai Zhao, Shang-Hua Gao, Wenguan Wang, Ming-Ming Cheng
Date: 2018-05-19 00:00:00
Proceeding: ICCV 2019 10
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Current CNN-based solutions to salient object detection (SOD) mainly rely on the optimization of cross-entropy loss (CELoss). Then the quality of detected saliency maps is often evaluated in terms of F-measure. In this paper, we investigate an interesting issue: can we consistently use the F-measure formulation in both training and evaluation for SOD? By reformulating the standard F-measure we propose the relaxed F-measure which is differentiable w.r.t the posterior and can be easily appended to the back of CNNs as the loss function. Compared to the conventional cross-entropy loss of which the gradients decrease dramatically in the saturated area, our loss function, named FLoss, holds considerable gradients even when the activation approaches the target. Consequently, the FLoss can continuously force the network to produce polarized activations. Comprehensive benchmarks on several popular datasets show that FLoss outperforms the state-of-the-art with a considerable margin. More specifically, due to the polarized predictions, our method is able to obtain high-quality saliency maps without carefully tuning the optimal threshold, showing significant advantages in real-world applications.
Wildest Faces: Face Detection and Recognition in Violent Settings
Authors: Mehmet Kerim Yucel, Yunus Can Bilge, Oguzhan Oguz, Nazli Ikizler-Cinbis, Pinar Duygulu, Ramazan Gokberk Cinbis
Date: 2018-05-19 00:00:00
Tasks: Face Detection, Face Recognition

With the introduction of large-scale datasets and deep learning models
capable of learning complex representations, impressive advances have emerged
in face detection and recognition tasks. Despite such advances, existing
datasets do not capture the difficulty of face recognition in the wildest
scenarios, such as hostile disputes or fights. Furthermore, existing datasets
do not represent completely unconstrained cases of low resolution, high blur
and large pose/occlusion variances. To this end, we introduce the Wildest Faces
dataset, which focuses on such adverse effects through violent scenes. The
dataset consists of an extensive set of violent scenes of celebrities from
movies. Our experimental results demonstrate that state-of-the-art techniques
are not well-suited for violent scenes, and therefore, Wildest Faces is likely
to stir further interest in face detection and recognition research.
ProofWatch: Watchlist Guidance for Large Theories in E
Authors: Zarathustra Goertzel, Jan Jakubův, Stephan Schulz, Josef Urban
Date: 2018-02-12 00:00:00

Watchlist (also hint list) is a mechanism that allows related proofs to guide
a proof search for a new conjecture. This mechanism has been used with the
Otter and Prover9 theorem provers, both for interactive formalizations and for
human-assisted proving of open conjectures in small theories. In this work we
explore the use of watchlists in large theories coming from first-order
translations of large ITP libraries, aiming at improving hammer-style
automation by smarter internal guidance of the ATP systems. In particular, we
(i) design watchlist-based clause evaluation heuristics inside the E ATP
system, and (ii) develop new proof guiding algorithms that load many previous
proofs inside the ATP and focus the proof search using a dynamically updated
notion of proof matching. The methods are evaluated on a large set of problems
coming from the Mizar library, showing significant improvement of E's standard
portfolio of strategies, and also of the previous best set of strategies
invented for Mizar by evolutionary methods.
Reinforcement Learning of Theorem Proving
Authors: Cezary Kaliszyk, Josef Urban, Henryk Michalewski, Mirek Olšák
Date: 2018-05-19 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Automated Theorem Proving, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce a theorem proving algorithm that uses practically no domain
heuristics for guiding its connection-style proof search. Instead, it runs many
Monte-Carlo simulations guided by reinforcement learning from previous proof
attempts. We produce several versions of the prover, parameterized by different
learning and guiding algorithms. The strongest version of the system is trained
on a large corpus of mathematical problems and evaluated on previously unseen
problems. The trained system solves within the same number of inferences over
40% more problems than a baseline prover, which is an unusually high
improvement in this hard AI domain. To our knowledge this is the first time
reinforcement learning has been convincingly applied to solving general
mathematical problems on a large scale.
Transduction with Matrix Completion Using Smoothed Rank Function
Authors: Ashkan Esmaeili, Kayhan Behdin, Mohammad Amin Fakharian, Farokh Marvasti
Date: 2018-05-19 00:00:00
Tasks: Matrix Completion

In this paper, we propose two new algorithms for transduction with Matrix
Completion (MC) problem. The joint MC and prediction tasks are addressed
simultaneously to enhance the accuracy, i.e., the label matrix is concatenated
to the data matrix forming a stacked matrix. Assuming the data matrix is of low
rank, we propose new recommendation methods by posing the problem as a
constrained minimization of the Smoothed Rank Function (SRF). We provide
convergence analysis for the proposed algorithms. The simulations are conducted
on real datasets in two different scenarios of randomly missing pattern with
and without block loss. The results confirm that the accuracy of our proposed
methods outperforms those of state-of-the-art methods even up to 10% in low
observation rates for the scenario without block loss. Our accuracy in the
latter scenario, is comparable to state-of-the-art methods while the complexity
of the proposed algorithms are reduced up to 4 times.
Nostalgic Adam: Weighting more of the past gradients when designing the adaptive learning rate
Authors: Haiwen Huang, Chang Wang, Bin Dong
Date: 2018-05-19 00:00:00

First-order optimization algorithms have been proven prominent in deep
learning. In particular, algorithms such as RMSProp and Adam are extremely
popular. However, recent works have pointed out the lack of ``long-term memory"
in Adam-like algorithms, which could hamper their performance and lead to
divergence. In our study, we observe that there are benefits of weighting more
of the past gradients when designing the adaptive learning rate. We therefore
propose an algorithm called the Nostalgic Adam (NosAdam) with theoretically
guaranteed convergence at the best known convergence rate. NosAdam can be
regarded as a fix to the non-convergence issue of Adam in alternative to the
recent work of [Reddi et al., 2018]. Our preliminary numerical experiments show
that NosAdam is a promising alternative algorithm to Adam. The proofs, code and
other supplementary materials can be found in an anonymously shared link.
Blockchain to Improve Security, Knowledge and Collaboration Inter-Agent Communication over Restrict Domains of the Internet Infrastructure
Authors: Juliao Braga, Joao Nuno Silva, Patricia Takako Endo, Jessica Ribas, Nizam Omar
Date: 2018-05-14 00:00:00

This paper describes the deployment and implementation of a blockchain to
improve the security, knowledge, intelligence and collaboration during the
inter-agent communication processes in restrict domains of the Internet
Infrastructure. It is a work that proposes the application of a blockchain,
platform independent, on a particular model of agents, but that can be used in
similar proposals, once the results on the specific model were satisfactory.
DenseImage Network: Video Spatial-Temporal Evolution Encoding and Understanding
Authors: Xiaokai Chen, Ke Gao
Date: 2018-05-19 00:00:00
Tasks: Action Recognition In Videos, Gesture Recognition, Video Understanding

Many of the leading approaches for video understanding are data-hungry and
time-consuming, failing to capture the gist of spatial-temporal evolution in an
efficient manner. The latest research shows that CNN network can reason about
static relation of entities in images. To further exploit its capacity in
dynamic evolution reasoning, we introduce a novel network module called
DenseImage Network(DIN) with two main contributions. 1) A novel compact
representation of video which distills its significant spatial-temporal
evolution into a matrix called DenseImage, primed for efficient video encoding.
2) A simple yet powerful learning strategy based on DenseImage and a
temporal-order-preserving CNN network is proposed for video understanding,
which contains a local temporal correlation constraint capturing temporal
evolution at multiple time scales with different filter widths. Extensive
experiments on two recent challenging benchmarks demonstrate that our
DenseImage Network can accurately capture the common spatial-temporal evolution
between similar actions, even with enormous visual variations or different time
scales. Moreover, we obtain the state-of-the-art results in action and gesture
recognition with much less time-and-memory cost, indicating its immense
potential in video representing and understanding.
Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image
Authors: Huazhu Fu, Jun Cheng, Yanwu Xu, Changqing Zhang, Damon Wing Kee Wong, Jiang Liu, Xiaochun Cao
Date: 2018-05-19 00:00:00

Glaucoma is a chronic eye disease that leads to irreversible vision loss.
Most of the existing automatic screening methods firstly segment the main
structure, and subsequently calculate the clinical measurement for detection
and screening of glaucoma. However, these measurement-based methods rely
heavily on the segmentation accuracy, and ignore various visual features. In
this paper, we introduce a deep learning technique to gain additional
image-relevant information, and screen glaucoma from the fundus image directly.
Specifically, a novel Disc-aware Ensemble Network (DENet) for automatic
glaucoma screening is proposed, which integrates the deep hierarchical context
of the global fundus image and the local optic disc region. Four deep streams
on different levels and modules are respectively considered as global image
stream, segmentation-guided network, local disc region stream, and disc polar
transformation stream. Finally, the output probabilities of different streams
are fused as the final screening result. The experiments on two glaucoma
datasets (SCES and new SINDI datasets) show our method outperforms other
state-of-the-art algorithms.
Infrared and Visible Image Fusion using a Deep Learning Framework
Authors: Hui Li, Xiao-Jun Wu, Josef Kittler
Date: 2018-04-19 00:00:00
Tasks: Deep Learning, Infrared And Visible Image Fusion

In recent years, deep learning has become a very active research tool which
is used in many image processing fields. In this paper, we propose an effective
image fusion method using a deep learning framework to generate a single image
which contains all the features from infrared and visible images. First, the
source images are decomposed into base parts and detail content. Then the base
parts are fused by weighted-averaging. For the detail content, we use a deep
learning network to extract multi-layer features. Using these features, we use
l_1-norm and weighted-average strategy to generate several candidates of the
fused detail content. Once we get these candidates, the max selection strategy
is used to get final fused detail content. Finally, the fused image will be
reconstructed by combining the fused base part and detail content. The
experimental results demonstrate that our proposed method achieves
state-of-the-art performance in both objective assessment and visual quality.
The Code of our fusion method is available at
https://github.com/hli1221/imagefusion_deeplearning