pwc-archive/papers-with-abstracts huggingface dataset:
Using Simpson's Paradox to Discover Interesting Patterns in Behavioral Data
Authors: Nazanin Alipourfard, Peter G. Fennell, Kristina Lerman
Date: 2018-05-08 00:00:00

We describe a data-driven discovery method that leverages Simpson's paradox
to uncover interesting patterns in behavioral data. Our method systematically
disaggregates data to identify subgroups within a population whose behavior
deviates significantly from the rest of the population. Given an outcome of
interest and a set of covariates, the method follows three steps. First, it
disaggregates data into subgroups, by conditioning on a particular covariate,
so as minimize the variation of the outcome within the subgroups. Next, it
models the outcome as a linear function of another covariate, both in the
subgroups and in the aggregate data. Finally, it compares trends to identify
disaggregations that produce subgroups with different behaviors from the
aggregate. We illustrate the method by applying it to three real-world
behavioral datasets, including Q\&A site Stack Exchange and online learning
platforms Khan Academy and Duolingo.
Deception in Optimal Control
Authors: Melkior Ornik, Ufuk Topcu
Date: 2018-05-08 00:00:00

In this paper, we consider an adversarial scenario where one agent seeks to
achieve an objective and its adversary seeks to learn the agent's intentions
and prevent the agent from achieving its objective. The agent has an incentive
to try to deceive the adversary about its intentions, while at the same time
working to achieve its objective. The primary contribution of this paper is to
introduce a mathematically rigorous framework for the notion of deception
within the context of optimal control. The central notion introduced in the
paper is that of a belief-induced reward: a reward dependent not only on the
agent's state and action, but also adversary's beliefs. Design of an optimal
deceptive strategy then becomes a question of optimal control design on the
product of the agent's state space and the adversary's belief space. The
proposed framework allows for deception to be defined in an arbitrary control
system endowed with a reward function, as well as with additional
specifications limiting the agent's control policy. In addition to defining
deception, we discuss design of optimally deceptive strategies under
uncertainties in agent's knowledge about the adversary's learning process. In
the latter part of the paper, we focus on a setting where the agent's behavior
is governed by a Markov decision process, and show that the design of optimally
deceptive strategies under lack of knowledge about the adversary naturally
reduces to previously discussed problems in control design on partially
observable or uncertain Markov decision processes. Finally, we present two
examples of deceptive strategies: a "cops and robbers" scenario and an example
where an agent may use camouflage while moving. We show that optimally
deceptive strategies in such examples follow the intuitive idea of how to
deceive an adversary in the above settings.
Active Object Reconstruction Using a Guided View Planner
Authors: Xin Yang, Yuanbo Wang, Yaru Wang, Bao-Cai Yin, Qiang Zhang, Xiaopeng Wei, Hongbo Fu
Date: 2018-05-08 00:00:00
Tasks: Object, Object Reconstruction

Inspired by the recent advance of image-based object reconstruction using
deep learning, we present an active reconstruction model using a guided view
planner. We aim to reconstruct a 3D model using images observed from a planned
sequence of informative and discriminative views. But where are such
informative and discriminative views around an object? To address this we
propose a unified model for view planning and object reconstruction, which is
utilized to learn a guided information acquisition model and to aggregate
information from a sequence of images for reconstruction. Experiments show that
our model (1) increases our reconstruction accuracy with an increasing number
of views (2) and generally predicts a more informative sequence of views for
object reconstruction compared to other alternative methods.
Towards Accurate and High-Speed Spiking Neuromorphic Systems with Data Quantization-Aware Deep Networks
Authors: Fuqiang Liu, C. Liu
Date: 2018-05-08 00:00:00
Tasks: Quantization

Deep Neural Networks (DNNs) have gained immense success in cognitive applications and greatly pushed today's artificial intelligence forward. The biggest challenge in executing DNNs is their extremely data-extensive computations. The computing efficiency in speed and energy is constrained when traditional computing platforms are employed in such computational hungry executions. Spiking neuromorphic computing (SNC) has been widely investigated in deep networks implementation own to their high efficiency in computation and communication. However, weights and signals of DNNs are required to be quantized when deploying the DNNs on the SNC, which results in unacceptable accuracy loss. %However, the system accuracy is limited by quantizing data directly in deep networks deployment. Previous works mainly focus on weights discretize while inter-layer signals are mainly neglected. In this work, we propose to represent DNNs with fixed integer inter-layer signals and fixed-point weights while holding good accuracy. We implement the proposed DNNs on the memristor-based SNC system as a deployment example. With 4-bit data representation, our results show that the accuracy loss can be controlled within 0.02% (2.3%) on MNIST (CIFAR-10). Compared with the 8-bit dynamic fixed-point DNNs, our system can achieve more than 9.8x speedup, 89.1% energy saving, and 30% area saving.
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Authors: Aditya Chattopadhyay, Anirban Sarkar, Prantik Howlader, Vineeth N. Balasubramanian
Date: 2017-10-30 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Caption Generation, Error Understanding, Knowledge Distillation, Object Localization, Temporal Action Localization

Over the last decade, Convolutional Neural Network (CNN) models have been
highly successful in solving complex vision problems. However, these deep
models are perceived as "black box" methods considering the lack of
understanding of their internal functioning. There has been a significant
recent interest in developing explainable deep learning models, and this paper
is an effort in this direction. Building on a recently proposed method called
Grad-CAM, we propose a generalized method called Grad-CAM++ that can provide
better visual explanations of CNN model predictions, in terms of better object
localization as well as explaining occurrences of multiple object instances in
a single image, when compared to state-of-the-art. We provide a mathematical
derivation for the proposed method, which uses a weighted combination of the
positive partial derivatives of the last convolutional layer feature maps with
respect to a specific class score as weights to generate a visual explanation
for the corresponding class label. Our extensive experiments and evaluations,
both subjective and objective, on standard datasets showed that Grad-CAM++
provides promising human-interpretable visual explanations for a given CNN
architecture across multiple tasks including classification, image caption
generation and 3D action recognition; as well as in new settings such as
knowledge distillation.
Efficient online learning for large-scale peptide identification
Authors: Xijun Liang, Zhonghang Xia, Yongxiang Wang, Ling Jian, Xinnan Niu, Andrew Link
Date: 2018-05-08 00:00:00

Motivation: Post-database searching is a key procedure in peptide
dentification with tandem mass spectrometry (MS/MS) strategies for refining
peptide-spectrum matches (PSMs) generated by database search engines. Although
many statistical and machine learning-based methods have been developed to
improve the accuracy of peptide identification, the challenge remains on
large-scale datasets and datasets with an extremely large proportion of false
positives (hard datasets). A more efficient learning strategy is required for
improving the performance of peptide identification on challenging datasets.
  Results: In this work, we present an online learning method to conquer the
challenges remained for exiting peptide identification algorithms. We propose a
cost-sensitive learning model by using different loss functions for decoy and
target PSMs respectively. A larger penalty for wrongly selecting decoy PSMs
than that for target PSMs, and thus the new model can reduce its false
discovery rate on hard datasets. Also, we design an online learning algorithm,
OLCS-Ranker, to solve the proposed learning model. Rather than taking all
training data samples all at once, OLCS-Ranker iteratively feeds in only one
training sample into the learning model at each round. As a result, the memory
requirement is significantly reduced for large-scale problems. Experimental
studies show that OLCS-Ranker outperforms benchmark methods, such as CRanker
and Batch-CS-Ranker, in terms of accuracy and stability. Furthermore,
OLCS-Ranker is 15--85 times faster than CRanker method on large datasets.
  Availability and implementation: OLCS-Ranker software is available at no
charge for non-commercial use at https://github.com/Isaac-QiXing/CRanker.
Category-Based Deep CCA for Fine-Grained Venue Discovery from Multimodal Data
Authors: Yi Yu, Suhua Tang, Kiyoharu Aizawa, Akiko Aizawa
Date: 2018-05-08 00:00:00
Tasks: Articles, Cross-Modal Retrieval, Retrieval

In this work, travel destination and business location are taken as venues.
Discovering a venue by a photo is very important for context-aware
applications. Unfortunately, few efforts paid attention to complicated real
images such as venue photos generated by users. Our goal is fine-grained venue
discovery from heterogeneous social multimodal data. To this end, we propose a
novel deep learning model, Category-based Deep Canonical Correlation Analysis
(C-DCCA). Given a photo as input, this model performs (i) exact venue search
(find the venue where the photo was taken), and (ii) group venue search (find
relevant venues with the same category as that of the photo), by the
cross-modal correlation between the input photo and textual description of
venues. In this model, data in different modalities are projected to a same
space via deep networks. Pairwise correlation (between different modal data
from the same venue) for exact venue search and category-based correlation
(between different modal data from different venues with the same category) for
group venue search are jointly optimized. Because a photo cannot fully reflect
rich text description of a venue, the number of photos per venue in the
training phase is increased to capture more aspects of a venue. We build a new
venue-aware multimodal dataset by integrating Wikipedia featured articles and
Foursquare venue photos. Experimental results on this dataset confirm the
feasibility of the proposed method. Moreover, the evaluation over another
publicly available dataset confirms that the proposed method outperforms
state-of-the-arts for cross-modal retrieval between image and text.
Moiré Photo Restoration Using Multiresolution Convolutional Neural Networks
Authors: Yujing Sun, Yizhou Yu, Wenping Wang
Date: 2018-05-08 00:00:00
Tasks: Denoising, Image Enhancement, Image Restoration

Digital cameras and mobile phones enable us to conveniently record precious
moments. While digital image quality is constantly being improved, taking
high-quality photos of digital screens still remains challenging because the
photos are often contaminated with moir\'{e} patterns, a result of the
interference between the pixel grids of the camera sensor and the device
screen. Moir\'{e} patterns can severely damage the visual quality of photos.
However, few studies have aimed to solve this problem. In this paper, we
introduce a novel multiresolution fully convolutional network for automatically
removing moir\'{e} patterns from photos. Since a moir\'{e} pattern spans over a
wide range of frequencies, our proposed network performs a nonlinear
multiresolution analysis of the input image before computing how to cancel
moir\'{e} artefacts within every frequency band. We also create a large-scale
benchmark dataset with $100,000^+$ image pairs for investigating and evaluating
moir\'{e} pattern removal algorithms. Our network achieves state-of-the-art
performance on this dataset in comparison to existing learning architectures
for image restoration problems.
On Improving Deep Reinforcement Learning for POMDPs
Authors: Pengfei Zhu, Xin Li, Pascal Poupart, Guanghui Miao
Date: 2018-04-17 00:00:00
Tasks: Atari Games, Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making, Time Series, Time Series Analysis

Deep Reinforcement Learning (RL) recently emerged as one of the most
competitive approaches for learning in sequential decision making problems with
fully observable environments, e.g., computer Go. However, very little work has
been done in deep RL to handle partially observable environments. We propose a
new architecture called Action-specific Deep Recurrent Q-Network (ADRQN) to
enhance learning performance in partially observable domains. Actions are
encoded by a fully connected layer and coupled with a convolutional observation
to form an action-observation pair. The time series of action-observation pairs
are then integrated by an LSTM layer that learns latent states based on which a
fully connected layer computes Q-values as in conventional Deep Q-Networks
(DQNs). We demonstrate the effectiveness of our new architecture in several
partially observable domains, including flickering Atari games.
Differential Equations for Modeling Asynchronous Algorithms
Authors: Li He, Qi Meng, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu
Date: 2018-05-08 00:00:00

Asynchronous stochastic gradient descent (ASGD) is a popular parallel
optimization algorithm in machine learning. Most theoretical analysis on ASGD
take a discrete view and prove upper bounds for their convergence rates.
However, the discrete view has its intrinsic limitations: there is no
characterization of the optimization path and the proof techniques are
induction-based and thus usually complicated. Inspired by the recent successful
adoptions of stochastic differential equations (SDE) to the theoretical
analysis of SGD, in this paper, we study the continuous approximation of ASGD
by using stochastic differential delay equations (SDDE). We introduce the
approximation method and study the approximation error. Then we conduct
theoretical analysis on the convergence rates of ASGD algorithm based on the
continuous approximation. There are two methods: moment estimation and energy
function minimization can be used to analyze the convergence rates. Moment
estimation depends on the specific form of the loss function, while energy
function minimization only leverages the convex property of the loss function,
and does not depend on its specific form. In addition to the convergence
analysis, the continuous view also helps us derive better convergence rates.
All of this clearly shows the advantage of taking the continuous view in
gradient descent algorithms.
Augmenting Recurrent Neural Networks with High-Order User-Contextual Preference for Session-Based Recommendation
Authors: Younghun Song, Jae-Gil Lee
Date: 2018-05-08 00:00:00
Tasks: Session-Based Recommendations

The recent adoption of recurrent neural networks (RNNs) for session modeling
has yielded substantial performance gains compared to previous approaches. In
terms of context-aware session modeling, however, the existing RNN-based models
are limited in that they are not designed to explicitly model rich static
user-side contexts (e.g., age, gender, location). Therefore, in this paper, we
explore the utility of explicit user-side context modeling for RNN session
models. Specifically, we propose an augmented RNN (ARNN) model that extracts
high-order user-contextual preference using the product-based neural network
(PNN) in order to augment any existing RNN session model. Evaluation results
show that our proposed model outperforms the baseline RNN session model by a
large margin when rich user-side contexts are available.
Multinomial Logit Bandit with Linear Utility Functions
Authors: Mingdong Ou, Nan Li, Shenghuo Zhu, Rong Jin
Date: 2018-05-08 00:00:00

Multinomial logit bandit is a sequential subset selection problem which
arises in many applications. In each round, the player selects a
$K$-cardinality subset from $N$ candidate items, and receives a reward which is
governed by a {\it multinomial logit} (MNL) choice model considering both item
utility and substitution property among items. The player's objective is to
dynamically learn the parameters of MNL model and maximize cumulative reward
over a finite horizon $T$. This problem faces the exploration-exploitation
dilemma, and the involved combinatorial nature makes it non-trivial. In recent
years, there have developed some algorithms by exploiting specific
characteristics of the MNL model, but all of them estimate the parameters of
MNL model separately and incur a regret no better than
$\tilde{O}\big(\sqrt{NT}\big)$ which is not preferred for large candidate set
size $N$. In this paper, we consider the {\it linear utility} MNL choice model
whose item utilities are represented as linear functions of $d$-dimension item
features, and propose an algorithm, titled {\bf LUMB}, to exploit the
underlying structure. It is proven that the proposed algorithm achieves
$\tilde{O}\big(dK\sqrt{T}\big)$ regret which is free of candidate set size.
Experiments show the superiority of the proposed algorithm.
Towards Knowledge Discovery from the Vatican Secret Archives. In Codice Ratio -- Episode 1: Machine Transcription of the Manuscripts
Authors: Donatella Firmani, Marco Maiorino, Paolo Merialdo, Elena Nieddu
Date: 2018-03-08 00:00:00

In Codice Ratio is a research project to study tools and techniques for
analyzing the contents of historical documents conserved in the Vatican Secret
Archives (VSA). In this paper, we present our efforts to develop a system to
support the transcription of medieval manuscripts. The goal is to provide
paleographers with a tool to reduce their efforts in transcribing large
volumes, as those stored in the VSA, producing good transcriptions for
significant portions of the manuscripts. We propose an original approach based
on character segmentation. Our solution is able to deal with the dirty
segmentation that inevitably occurs in handwritten documents. We use a
convolutional neural network to recognize characters and language models to
compose word transcriptions. Our approach requires minimal training efforts,
making the transcription process more scalable as the production of training
sets requires a few pages and can be easily crowdsourced. We have conducted
experiments on manuscripts from the Vatican Registers, an unreleased corpus
containing the correspondence of the popes. With training data produced by 120
high school students, our system has been able to produce good transcriptions
that can be used by paleographers as a solid basis to speedup the transcription
process at a large scale.
Accurate parameter estimation for Bayesian Network Classifiers using Hierarchical Dirichlet Processes
Authors: Francois Petitjean, Wray Buntine, Geoffrey I. Webb, Nayyar Zaidi
Date: 2017-08-25 00:00:00
Tasks: General Classification, parameter estimation

This paper introduces a novel parameter estimation method for the probability
tables of Bayesian network classifiers (BNCs), using hierarchical Dirichlet
processes (HDPs). The main result of this paper is to show that improved
parameter estimation allows BNCs to outperform leading learning methods such as
Random Forest for both 0-1 loss and RMSE, albeit just on categorical datasets.
  As data assets become larger, entering the hyped world of "big", efficient
accurate classification requires three main elements: (1) classifiers with
low-bias that can capture the fine-detail of large datasets (2) out-of-core
learners that can learn from data without having to hold it all in main memory
and (3) models that can classify new data very efficiently.
  The latest Bayesian network classifiers (BNCs) satisfy these requirements.
Their bias can be controlled easily by increasing the number of parents of the
nodes in the graph. Their structure can be learned out of core with a limited
number of passes over the data. However, as the bias is made lower to
accurately model classification tasks, so is the accuracy of their parameters'
estimates, as each parameter is estimated from ever decreasing quantities of
data. In this paper, we introduce the use of Hierarchical Dirichlet Processes
for accurate BNC parameter estimation.
  We conduct an extensive set of experiments on 68 standard datasets and
demonstrate that our resulting classifiers perform very competitively with
Random Forest in terms of prediction, while keeping the out-of-core capability
and superior classification time.
Generalized Self-Concordant Functions: A Recipe for Newton-Type Methods
Authors: Tianxiao Sun, Quoc Tran-Dinh
Date: 2017-03-14 00:00:00
Tasks: Vocal Bursts Type Prediction

We study the smooth structure of convex functions by generalizing a powerful
concept so-called self-concordance introduced by Nesterov and Nemirovskii in
the early 1990s to a broader class of convex functions, which we call
generalized self-concordant functions. This notion allows us to develop a
unified framework for designing Newton-type methods to solve convex optimiza-
tion problems. The proposed theory provides a mathematical tool to analyze both
local and global convergence of Newton-type methods without imposing
unverifiable assumptions as long as the un- derlying functionals fall into our
generalized self-concordant function class. First, we introduce the class of
generalized self-concordant functions, which covers standard self-concordant
functions as a special case. Next, we establish several properties and key
estimates of this function class, which can be used to design numerical
methods. Then, we apply this theory to develop several Newton-type methods for
solving a class of smooth convex optimization problems involving the
generalized self- concordant functions. We provide an explicit step-size for
the damped-step Newton-type scheme which can guarantee a global convergence
without performing any globalization strategy. We also prove a local quadratic
convergence of this method and its full-step variant without requiring the
Lipschitz continuity of the objective Hessian. Then, we extend our result to
develop proximal Newton-type methods for a class of composite convex
minimization problems involving generalized self-concordant functions. We also
achieve both global and local convergence without additional assumption.
Finally, we verify our theoretical results via several numerical examples, and
compare them with existing methods.