pwc-archive/papers-with-abstracts huggingface dataset:
Recurrent Poisson Factorization for Temporal Recommendation
Authors: Seyed Abbas Hosseini, Keivan Alizadeh, Ali Khodadadi, Ali Arabzadeh, Mehrdad Farajtabar, Hongyuan Zha, Hamid R. Rabiee
Date: 2017-03-04 00:00:00
Tasks: Recommendation Systems

Poisson factorization is a probabilistic model of users and items for
recommendation systems, where the so-called implicit consumer data is modeled
by a factorized Poisson distribution. There are many variants of Poisson
factorization methods who show state-of-the-art performance on real-world
recommendation tasks. However, most of them do not explicitly take into account
the temporal behavior and the recurrent activities of users which is essential
to recommend the right item to the right user at the right time. In this paper,
we introduce Recurrent Poisson Factorization (RPF) framework that generalizes
the classical PF methods by utilizing a Poisson process for modeling the
implicit feedback. RPF treats time as a natural constituent of the model and
brings to the table a rich family of time-sensitive factorization models. To
elaborate, we instantiate several variants of RPF who are capable of handling
dynamic user preferences and item specification (DRPF), modeling the
social-aspect of product adoption (SRPF), and capturing the consumption
heterogeneity among users and items (HRPF). We also develop a variational
algorithm for approximate posterior inference that scales up to massive data
sets. Furthermore, we demonstrate RPF's superior performance over many
state-of-the-art methods on synthetic dataset, and large scale real-world
datasets on music streaming logs, and user-item interactions in M-Commerce
platforms.
Automated Top View Registration of Broadcast Football Videos
Authors: Rahul Anand Sharma, Bharath Bhat, Vineet Gandhi, C. V. Jawahar
Date: 2017-03-04 00:00:00
Tasks: Bird View Synthesis, Homography Estimation

In this paper, we propose a novel method to register football broadcast video
frames on the static top view model of the playing surface. The proposed method
is fully automatic in contrast to the current state of the art which requires
manual initialization of point correspondences between the image and the static
model. Automatic registration using existing approaches has been difficult due
to the lack of sufficient point correspondences. We investigate an alternate
approach exploiting the edge information from the line markings on the field.
We formulate the registration problem as a nearest neighbour search over a
synthetically generated dictionary of edge map and homography pairs. The
synthetic dictionary generation allows us to exhaustively cover a wide variety
of camera angles and positions and reduce this problem to a minimal per-frame
edge map matching procedure. We show that the per-frame results can be improved
in videos using an optimization framework for temporal camera stabilization. We
demonstrate the efficacy of our approach by presenting extensive results on a
dataset collected from matches of football World Cup 2014.
Machine Learning Based Source Code Classification Using Syntax Oriented Features
Authors: Shaul Zevin, Catherine Holzem
Date: 2017-03-04 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Code Classification, General Classification, Language Identification

As of today the programming language of the vast majority of the published
source code is manually specified or programmatically assigned based on the
sole file extension. In this paper we show that the source code programming
language identification task can be fully automated using machine learning
techniques. We first define the criteria that a production-level automatic
programming language identification solution should meet. Our criteria include
accuracy, programming language coverage, extensibility and performance. We then
describe our approach: How training files are preprocessed for extracting
features that mimic grammar productions, and then how these extracted grammar
productions are used for the training and testing of our classifier. We achieve
a 99 percent accuracy rate while classifying 29 of the most popular programming
languages with a Maximum Entropy classifier.
Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection
Authors: Yuliang Liu, Lianwen Jin
Date: 2017-03-04 00:00:00
Proceeding: CVPR 2017 7
Tasks: Text Detection

Detecting incidental scene text is a challenging task because of
multi-orientation, perspective distortion, and variation of text size, color
and scale. Retrospective research has only focused on using rectangular
bounding box or horizontal sliding window to localize text, which may result in
redundant background noise, unnecessary overlap or even information loss. To
address these issues, we propose a new Convolutional Neural Networks (CNNs)
based method, named Deep Matching Prior Network (DMPNet), to detect text with
tighter quadrangle. First, we use quadrilateral sliding windows in several
specific intermediate convolutional layers to roughly recall the text with
higher overlapping area and then a shared Monte-Carlo method is proposed for
fast and accurate computing of the polygonal areas. After that, we designed a
sequential protocol for relative regression which can exactly predict text with
compact quadrangle. Moreover, a auxiliary smooth Ln loss is also proposed for
further regressing the position of text, which has better overall performance
than L2 loss and smooth L1 loss in terms of robustness and stability. The
effectiveness of our approach is evaluated on a public word-level,
multi-oriented scene text database, ICDAR 2015 Robust Reading Competition
Challenge 4 "Incidental scene text localization". The performance of our method
is evaluated by using F-measure and found to be 70.64%, outperforming the
existing state-of-the-art method with F-measure 63.76%.
Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks
Authors: Terrance DeVries, Dhanesh Ramachandram
Date: 2017-03-04 00:00:00
Tasks: Classification, General Classification, Lesion Classification, Skin Lesion Classification

We present a deep learning approach to the ISIC 2017 Skin Lesion
Classification Challenge using a multi-scale convolutional neural network. Our
approach utilizes an Inception-v3 network pre-trained on the ImageNet dataset,
which is fine-tuned for skin lesion classification using two different scales
of input images.
Variational Lossy Autoencoder
Authors: Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
Date: 2016-11-08 00:00:00
Tasks: Density Estimation, Image Generation, Representation Learning

Representation learning seeks to expose certain aspects of observed data in a
learned representation that's amenable to downstream tasks like classification.
For instance, a good representation for 2D images might be one that describes
only global structure and discards information about detailed texture. In this
paper, we present a simple but principled method to learn such global
representations by combining Variational Autoencoder (VAE) with neural
autoregressive models such as RNN, MADE and PixelRNN/CNN. Our proposed VAE
model allows us to have control over what the global latent code can learn and
, by designing the architecture accordingly, we can force the global latent
code to discard irrelevant information such as texture in 2D images, and hence
the VAE only "autoencodes" data in a lossy fashion. In addition, by leveraging
autoregressive models as both prior distribution $p(z)$ and decoding
distribution $p(x|z)$, we can greatly improve generative modeling performance
of VAEs, achieving new state-of-the-art results on MNIST, OMNIGLOT and
Caltech-101 Silhouettes density estimation tasks.
Looking at Outfit to Parse Clothing
Authors: Pongsate Tangseng, Zhipeng Wu, Kota Yamaguchi
Date: 2017-03-04 00:00:00
Tasks: Image Retrieval, Retrieval

This paper extends fully-convolutional neural networks (FCN) for the clothing
parsing problem. Clothing parsing requires higher-level knowledge on clothing
semantics and contextual cues to disambiguate fine-grained categories. We
extend FCN architecture with a side-branch network which we refer outfit
encoder to predict a consistent set of clothing labels to encourage
combinatorial preference, and with conditional random field (CRF) to explicitly
consider coherent label assignment to the given image. The empirical results
using Fashionista and CFPD datasets show that our model achieves
state-of-the-art performance in clothing parsing, without additional
supervision during training. We also study the qualitative influence of
annotation on the current clothing parsing benchmarks, with our Web-based tool
for multi-scale pixel-wise annotation and manual refinement effort to the
Fashionista dataset. Finally, we show that the image representation of the
outfit encoder is useful for dress-up image retrieval application.
Wavelet Domain Residual Network (WavResNet) for Low-Dose X-ray CT Reconstruction
Authors: Eunhee Kang, Junhong Min, Jong Chul Ye
Date: 2017-03-04 00:00:00
Tasks: CT Reconstruction, Low-Dose X-Ray Ct Reconstruction

Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT
are computationally complex because of the repeated use of the forward and
backward projection. Inspired by this success of deep learning in computer
vision applications, we recently proposed a deep convolutional neural network
(CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT
Grand Challenge. However, some of the texture are not fully recovered, which
was unfamiliar to some radiologists. To cope with this problem, here we propose
a direct residual learning approach on directional wavelet domain to solve this
problem and to improve the performance against previous work. In particular,
the new network estimates the noise of each input wavelet transform, and then
the de-noised wavelet coefficients are obtained by subtracting the noise from
the input wavelet transform bands. The experimental results confirm that the
proposed network has significantly improved performance, preserving the detail
texture of the original images.
Multi-Scale Wavelet Domain Residual Learning for Limited-Angle CT Reconstruction
Authors: Jawook Gu, Jong Chul Ye
Date: 2017-03-04 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction

Limited-angle computed tomography (CT) is often used in clinical applications
such as C-arm CT for interventional imaging. However, CT images from limited
angles suffers from heavy artifacts due to incomplete projection data. Existing
iterative methods require extensive calculations but can not deliver
satisfactory results. Based on the observation that the artifacts from limited
angles have some directional property and are globally distributed, we propose
a novel multi-scale wavelet domain residual learning architecture, which
compensates for the artifacts. Experiments have shown that the proposed method
effectively eliminates artifacts, thereby preserving edge and global structures
of the image.
DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network
Authors: Afshin Dehghan, Enrique. G. Ortiz, Guang Shu, Syed Zain Masood
Date: 2017-02-14 00:00:00
Tasks: Emotion Recognition

This paper describes the details of Sighthound's fully automated age, gender
and emotion recognition system. The backbone of our system consists of several
deep convolutional neural networks that are not only computationally
inexpensive, but also provide state-of-the-art results on several competitive
benchmarks. To power our novel deep networks, we collected large labeled
datasets through a semi-supervised pipeline to reduce the annotation
effort/time. We tested our system on several public benchmarks and report
outstanding results. Our age, gender and emotion recognition models are
available to developers through the Sighthound Cloud API at
https://www.sighthound.com/products/cloud
Conservative Contextual Linear Bandits
Authors: Abbas Kazerouni, Mohammad Ghavamzadeh, Yasin Abbasi-Yadkori, Benjamin Van Roy
Date: 2016-11-19 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Decision Making, Marketing

Safety is a desirable property that can immensely increase the applicability
of learning algorithms in real-world decision-making problems. It is much
easier for a company to deploy an algorithm that is safe, i.e., guaranteed to
perform at least as well as a baseline. In this paper, we study the issue of
safety in contextual linear bandits that have application in many different
fields including personalized ad recommendation in online marketing. We
formulate a notion of safety for this class of algorithms. We develop a safe
contextual linear bandit algorithm, called conservative linear UCB (CLUCB),
that simultaneously minimizes its regret and satisfies the safety constraint,
i.e., maintains its performance above a fixed percentage of the performance of
a baseline strategy, uniformly over time. We prove an upper-bound on the regret
of CLUCB and show that it can be decomposed into two terms: 1) an upper-bound
for the regret of the standard linear UCB algorithm that grows with the time
horizon and 2) a constant (does not grow with the time horizon) term that
accounts for the loss of being conservative in order to satisfy the safety
constraint. We empirically show that our algorithm is safe and validate our
theoretical analysis.
Do Deep Convolutional Nets Really Need to be Deep and Convolutional?
Authors: Gregor Urban, Krzysztof J. Geras, Samira Ebrahimi Kahou, Ozlem Aslan, Shengjie Wang, Rich Caruana, Abdel-rahman Mohamed, Matthai Philipose, Matt Richardson
Date: 2016-03-17 00:00:00

Yes, they do. This paper provides the first empirical demonstration that deep
convolutional models really need to be both deep and convolutional, even when
trained with methods such as distillation that allow small or shallow models of
high accuracy to be trained. Although previous research showed that shallow
feed-forward nets sometimes can learn the complex functions previously learned
by deep nets while using the same number of parameters as the deep models they
mimic, in this paper we demonstrate that the same methods cannot be used to
train accurate models on CIFAR-10 unless the student models contain multiple
layers of convolution. Although the student models do not have to be as deep as
the teacher model they mimic, the students need multiple convolutional layers
to learn functions of comparable accuracy as the deep convolutional teacher.
Convex Geometry of the Generalized Matrix-Fractional Function
Authors: James V. Burke, Yuan Gao, Tim Hoheisel
Date: 2017-03-04 00:00:00

Generalized matrix-fractional (GMF) functions are a class of matrix support
functions introduced by Burke and Hoheisel as a tool for unifying a range of
seemingly divergent matrix optimization problems associated with inverse
problems, regularization and learning. In this paper we dramatically simplify
the support function representation for GMF functions as well as the
representation of their subdifferentials. These new representations allow the
ready computation of a range of important related geometric objects whose
formulations were previously unavailable.
Generalised Discount Functions applied to a Monte-Carlo AImu Implementation
Authors: Sean Lamont, John Aslanides, Jan Leike, Marcus Hutter
Date: 2017-03-03 00:00:00
Tasks: General Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years, work has been done to develop the theory of General
Reinforcement Learning (GRL). However, there are few examples demonstrating
these results in a concrete way. In particular, there are no examples
demonstrating the known results regarding gener- alised discounting. We have
added to the GRL simulation platform AIXIjs the functionality to assign an
agent arbitrary discount functions, and an environment which can be used to
determine the effect of discounting on an agent's policy. Using this, we
investigate how geometric, hyperbolic and power discounting affect an informed
agent in a simple MDP. We experimentally reproduce a number of theoretical
results, and discuss some related subtleties. It was found that the agent's
behaviour followed what is expected theoretically, assuming appropriate
parameters were chosen for the Monte-Carlo Tree Search (MCTS) planning
algorithm.
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles
Authors: Jung-hun Kim, Se-Young Yun, Minchan Jeong, Jun Hyun Nam, Jinwoo Shin, Richard Combes
Date: 2017-03-03 00:00:00
Tasks: Multi-Armed Bandits

We study contextual linear bandit problems under feature uncertainty, where the features are noisy and have missing entries. To address the challenges posed by this noise, we analyze Bayesian oracles given the observed noisy features. Our Bayesian analysis reveals that the optimal hypothesis can significantly deviate from the underlying realizability function, depending on the noise characteristics. These deviations are highly non-intuitive and do not occur in classical noiseless setups. This implies that classical approaches cannot guarantee a non-trivial regret bound. Therefore, we propose an algorithm that aims to approximate the Bayesian oracle based on the observed information under this model, achieving $\tilde{O}(d\sqrt{T})$ regret bound when there is a large number of arms. We demonstrate the proposed algorithm using synthetic and real-world datasets.
Frank-Wolfe Algorithms for Saddle Point Problems
Authors: Gauthier Gidel, Tony Jebara, Simon Lacoste-Julien
Date: 2016-10-25 00:00:00
Tasks: Structured Prediction

We extend the Frank-Wolfe (FW) optimization algorithm to solve constrained
smooth convex-concave saddle point (SP) problems. Remarkably, the method only
requires access to linear minimization oracles. Leveraging recent advances in
FW optimization, we provide the first proof of convergence of a FW-type saddle
point solver over polytopes, thereby partially answering a 30 year-old
conjecture. We also survey other convergence results and highlight gaps in the
theoretical underpinnings of FW-style algorithms. Motivating applications
without known efficient alternatives are explored through structured prediction
with combinatorial penalties as well as games over matching polytopes involving
an exponential number of constraints.
DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker
Authors: Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Michael Bowling
Date: 2017-01-06 00:00:00
Tasks: Game of Poker

Artificial intelligence has seen several breakthroughs in recent years, with
games often serving as milestones. A common feature of these games is that
players have perfect information. Poker is the quintessential game of imperfect
information, and a longstanding challenge problem in artificial intelligence.
We introduce DeepStack, an algorithm for imperfect information settings. It
combines recursive reasoning to handle information asymmetry, decomposition to
focus computation on the relevant decision, and a form of intuition that is
automatically learned from self-play using deep learning. In a study involving
44,000 hands of poker, DeepStack defeated with statistical significance
professional poker players in heads-up no-limit Texas hold'em. The approach is
theoretically sound and is shown to produce more difficult to exploit
strategies than prior approaches.
Generative Poisoning Attack Method Against Neural Networks
Authors: Chaofei Yang, Qing Wu, Hai Li, Yiran Chen
Date: 2017-03-03 00:00:00

Poisoning attack is identified as a severe security threat to machine
learning algorithms. In many applications, for example, deep neural network
(DNN) models collect public data as the inputs to perform re-training, where
the input data can be poisoned. Although poisoning attack against support
vector machines (SVM) has been extensively studied before, there is still very
limited knowledge about how such attack can be implemented on neural networks
(NN), especially DNNs. In this work, we first examine the possibility of
applying traditional gradient-based method (named as the direct gradient
method) to generate poisoned data against NNs by leveraging the gradient of the
target model w.r.t. the normal data. We then propose a generative method to
accelerate the generation rate of the poisoned data: an auto-encoder
(generator) used to generate poisoned data is updated by a reward function of
the loss, and the target NN model (discriminator) receives the poisoned data to
calculate the loss w.r.t. the normal data. Our experiment results show that the
generative method can speed up the poisoned data generation rate by up to
239.38x compared with the direct gradient method, with slightly lower model
accuracy degradation. A countermeasure is also designed to detect such
poisoning attack methods by checking the loss of the target model.
Building Statistical Shape Spaces for 3D Human Modeling
Authors: Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt, Bernt Schiele
Date: 2015-03-19 00:00:00

Statistical models of 3D human shape and pose learned from scan databases
have developed into valuable tools to solve a variety of vision and graphics
problems. Unfortunately, most publicly available models are of limited
expressiveness as they were learned on very small databases that hardly reflect
the true variety in human body shapes. In this paper, we contribute by
rebuilding a widely used statistical body representation from the largest
commercially available scan database, and making the resulting model available
to the community (visit http://humanshape.mpi-inf.mpg.de). As preprocessing
several thousand scans for learning the model is a challenge in itself, we
contribute by developing robust best practice solutions for scan alignment that
quantitatively lead to the best learned models. We make implementations of
these preprocessing steps also publicly available. We extensively evaluate the
improved accuracy and generality of our new model, and show its improved
performance for human body reconstruction from sparse input data.
Why Deep Neural Networks for Function Approximation?
Authors: Shiyu Liang, R. Srikant
Date: 2016-10-13 00:00:00

Recently there has been much interest in understanding why deep neural
networks are preferred to shallow networks. We show that, for a large class of
piecewise smooth functions, the number of neurons needed by a shallow network
to approximate a function is exponentially larger than the corresponding number
of neurons needed by a deep network for a given degree of function
approximation. First, we consider univariate functions on a bounded interval
and require a neural network to achieve an approximation error of $\varepsilon$
uniformly over the interval. We show that shallow networks (i.e., networks
whose depth does not depend on $\varepsilon$) require
$\Omega(\text{poly}(1/\varepsilon))$ neurons while deep networks (i.e.,
networks whose depth grows with $1/\varepsilon$) require
$\mathcal{O}(\text{polylog}(1/\varepsilon))$ neurons. We then extend these
results to certain classes of important multivariate functions. Our results are
derived for neural networks which use a combination of rectifier linear units
(ReLUs) and binary step units, two of the most popular type of activation
functions. Our analysis builds on a simple observation: the multiplication of
two bits can be represented by a ReLU.